Regression analysis mathematically describes the relationship between a set of independent variables and a dependent variable. There are numerous types of regression models that you can use. This choice often depends on the kind of data you have for the dependent variable and the type of model that provides the best fit. In this post, I cover the more common types of regression analyses and how to decide which one is right for your data.
I’ll provide an overview along with information to help you choose. I organize the types of regression by the different kinds of dependent variable. If you’re not sure which procedure to use, determine which type of dependent variable you have, and then focus on that section in this post. This process should help narrow the choices! I’ll cover regression models that are appropriate for dependent variables that measure continuous, categorical, and count data.
Related posts: Nominal, Ordinal, Interval, and Ratio Scales, Guide to Data Types and How to Graph Them, and Independent and Dependent Variables Explained
Regression Analysis with Continuous Dependent Variables
Regression analysis with a continuous dependent variable is probably the first type that comes to mind. While this is the primary case, you still need to decide which one to use.
Continuous variables are a measurement on a continuous scale, such as weight, time, and length.
Linear regression
Linear regression, also known as ordinary least squares (OLS) and linear least squares, is the real workhorse of the regression world. Use linear regression to understand the mean change in a dependent variable given a oneunit change in each independent variable. You can also use polynomials to model curvature and include interaction effects. Despite the term “linear model,” this type can model curvature.
This analysis estimates parameters by minimizing the sum of the squared errors (SSE). Linear models are the most common and most straightforward to use. If you have a continuous dependent variable, linear regression is probably the first type you should consider.
There are some special options available for linear regression.

Fitted line plots: If you have one independent variable and the dependent variable, use a fitted line plot to display the data along with the fitted regression line and essential regression output. These graphs make understanding the model more intuitive.
 Stepwise regression and Best subsets regression: These automated methods can help identify candidate variables early in the model specification process.
Advanced types of linear regression
Linear models are the oldest type of regression. It was designed so that statisticians can do the calculations by hand. However, OLS has several weaknesses, including a sensitivity to both outliers and multicollinearity, and it is prone to overfitting. To address these problems, statisticians have developed several advanced variants:
 Ridge regression allows you to analyze data even when severe multicollinearity is present and helps prevent overfitting. This type of model reduces the large, problematic variance that multicollinearity causes by introducing a slight bias in the estimates. The procedure trades away much of the variance in exchange for a little bias, which produces more useful coefficient estimates when multicollinearity is present.
 Lasso regression (least absolute shrinkage and selection operator) performs variable selection that aims to increase prediction accuracy by identifying a simpler model. It is similar to Ridge regression but with variable selection.
 Partial least squares (PLS) regression is useful when you have very few observations compared to the number of independent variables or when your independent variables are highly correlated. PLS decreases the independent variables down to a smaller number of uncorrelated components, similar to Principal Components Analysis. Then, the procedure performs linear regression on these components rather than the original data. PLS emphasizes developing predictive models and is not used for screening variables. Unlike OLS, you can include multiple continuous dependent variables. PLS uses the correlation structure to identify smaller effects and model multivariate patterns in the dependent variables.
Nonlinear regression
Nonlinear regression also requires a continuous dependent variable, but it provides a greater flexibility to fit curves than linear regression.
Like OLS, nonlinear regression estimates the parameters by minimizing the SSE. However, nonlinear models use an iterative algorithm rather than the linear approach of solving them directly with matrix equations. What this means for you is that you need to worry about which algorithm to use, specifying good starting values, and the possibility of either not converging on a solution or converging on a local minimum rather than a global minimum SSE. And that’s in addition to specifying the correct functional form!
Most nonlinear models have one continuous independent variable, but it is possible to have more than one. When you have one independent variable, you can graph the results using a fitted line plot.
My advice is to fit a model using linear regression first and then determine whether the linear model provides an adequate fit by checking the residual plots. If you can’t obtain a good fit using linear regression, then try a nonlinear model because it can fit a wider variety of curves. I always recommend that you try OLS first because it is easier to perform and interpret.
I’ve written quite a bit about the differences between linear and nonlinear models. Read the following posts to learn the differences between these two types, how to choose which one is best for your data, and how to interpret the results.
 What is the Difference Between Linear and Nonlinear Models?
 How to Choose Between Linear and Nonlinear Regression?
 Curve Fitting with Linear and Nonlinear Regression
Regression Analysis with Categorical Dependent Variables
So far, we’ve looked at models that require a continuous dependent variable. Next, let’s move on to categorical independent variables. A categorical variable has values that you can put into a countable number of distinct groups based on a characteristic. Logistic regression transforms the dependent variable and then uses Maximum Likelihood Estimation, rather than least squares, to estimate the parameters.
Logistic regression describes the relationship between a set of independent variables and a categorical dependent variable. Choose the type of logistic model based on the type of categorical dependent variable you have.
Binary Logistic Regression
Use binary logistic regression to understand how changes in the independent variables are associated with changes in the probability of an event occurring. This type of model requires a binary dependent variable. A binary variable has only two possible values, such as pass and fail.
Example: Political scientists assess the odds of the incumbent U.S. President winning reelection based on stock market performance.
Read my post about a binary logistic model that estimates the probability of House Republicans belonging to the Freedom Caucus.
Ordinal Logistic Regression
Ordinal logistic regression models the relationship between a set of predictors and an ordinal response variable. An ordinal response has at least three groups which have a natural order, such as hot, medium, and cold.
Example: Market analysts want to determine which variables influence the decision to buy large, medium, or small popcorn at the movie theater.
Nominal Logistic Regression
Nominal logistic regression, also known as multinomial logistic regression, models the relationship between a set of independent variables and a nominal dependent variable. A nominal variable has at least three groups which do not have a natural order, such as scratch, dent, and tear.
Example: A quality analyst studies the variables that affect the odds of the type of product defects: scratches, dents, and tears.
Regression Analysis with Count Dependent Variables
If your dependent variable is a count of items, events, results, or activities, you might need to use a different type of regression model. Counts are nonnegative integers (0, 1, 2, etc.). Count data with higher means tend to be normally distributed and you can often use OLS. However, count data with smaller means can be skewed, and linear regression might have a hard time fitting these data. For these cases, there are several types of models you can use.
Poisson regression
Count data frequently follow the Poisson distribution, which makes Poisson Regression a good possibility. Poisson variables are a count of something over a constant amount of time, area, or another consistent length of observation. With a Poisson variable, you can calculate and assess a rate of occurrence. A classic example of a Poisson dataset is provided by Ladislaus Bortkiewicz, a Russian economist, who analyzed annual deaths caused by horse kicks in the Prussian Army from 18751894.
Use Poisson regression to model how changes in the independent variables are associated with changes in the counts. Poisson models are similar to logistic models because they use Maximum Likelihood Estimation and transform the dependent variable using the natural log. Poisson models can be suitable for rate data, where the rate is a count of events divided by a measure of that unit’s exposure (a consistent unit of observation). For example, homicides per month.
Example: An analyst uses Poisson regression to model the number of calls that a call center receives daily.
Alternatives to Poisson regression for count data
Not all count data follow the Poisson distribution because this distribution has some stringent restrictions. Fortunately, there are alternative analyses you can perform when you have count data.
Negative binomial regression: Poisson regression assumes that the variance equals the mean. When the variance is greater than the mean, your model has overdispersion. A negative binomial model, also known as NB2, can be more appropriate when overdispersion is present.
Zeroinflated models: Your count data might have too many zeros to follow the Poisson distribution. In other words, there are more zeros than Poisson regression predicts. Zeroinflated models assume that two separate processes work together to produce the excessive zeros. One process determines whether there are zero events or more than zero events. The other is the Poisson process that determines how many events occur, some of which can be zero. An example makes this clearer!
Suppose park rangers count the number of fish caught by each park visitor as they exit the park. A zeroinflated model might be appropriate for this scenario because there are two processes for catching zero fish:
 Some park visitors catch zero fish because they did not go fishing.
 Other visitors went fishing, and some of these people caught zero fish.
Whew! That’s many different types of regression analysis! If you’re trying to figure out which one to choose, I hope you will use this information to point yourself in the right direction!
If you’re learning regression and like the approach I use in my blog, check out my eBook!
Michael says
Dear Jim,
I was wondering what you would do in the following situation.
The data is panel data, with countryyears as the unit of analysis. There are 20 countries over 20 years.
The dependent variable is a quasicount. It has 64% of zero values and 36% of positive values. These positive values range from 1 to 10383 (70 unique values). It so happens that they are all nonnegative integers (although theoretically, they could also be nonnegative NONintegers, i.e. they could theoretically have a decimal point).
The main problem has been with the issue of “too many zeros”.
We have discarded zeroinflated models for 3 reasons: a) we know there are no “true” zeros in our database, because all cases could be positive nonzeros, b) we do not have theoretical or empirical reasons to suspect a twostep process as implied by zeroinflated models where a first step distinguishes zeros from nonzeros and a second step models variation among nonzeros, c) we know the difference between zero and low values (e.g. 1 or 2 or 3) are not really significant theoretically or empirically. For the same reasons, we did not go into Heckman selection models such as a Tobit2 model.
Considering the overdispersed nature of our dependent variable, that it is a quasicount ranging from 0 to 10383 with 64% of zero values, we ended up with a negative binomial model. Because our data is countryyear panel data, we first ran a random effects negative binomial model, and then a fixed effects negative binomial model. Because some people might question whether our dependent variable is a true “count variable” or not, we also run a GEE model (i.e. a populationaverage paneldata model using generalized estimating equations) where we specified a gamma distribution with a log link function.
What do you think?
We are in two minds. On the one hand, we do not have a typical count variable (often, count variables do not go from 0 to 10383, so it is maybe not your typical count and the negative binomial might struggle with such high values) so some might take issue that our nonnegative integer variable should not be modelled through a negative binomial, whilst gee models with a gamma family and a log link function are not so common in my field (and additionally, they often fail to converge for models with too many variables). On the other hand, we are not sure what type of model could cope with 64% zero values, especially considering that zero inflated models (such as zero inflated poisson or zero inflated negative binomial) and heckman selection models do not seem empirically or theoretically appropriate. We substantively know that we do not have a twostep process going on.
Do you have any feedback or advice?
Or do you think that what we did sounds ok?
Sorry for the long post.
Jovelyn says
Hello Jim,
May I ask what to use if my independent variables are ranked data, the data is in percentile rank, can I still use logistic regression?
Jim Frost says
Hi Jovelyn,
Unfortunately, ranked variables are difficult to incorporate as IVs. Or, I should say, there are more complexities involved. You’ll need to decide to include them either as continuous variables or categorical variables. Ranked data are ordinal variables, which share properties of both continuous and categorical variables. The decision to include them either as continuous or categorical variables depends on both the goals of your study and the nature of your data (e.g., sample size, which choice provides a better fit, and number of ranks per variable). I write about this in more detail in my regression analysis book.
Samuel Frimpong says
hello Jim,
I would appreciate your help with the kind of regression model suitable for my experimental design, I am somewhat conflicted.
Okay, so my experimental design is a 3x2x2 factorial design. These three factors comprises one continuous independent variables and two categorical independent variable. your input as to the most suitable regression model that could be used for the data analysis will greatly be appreciated
NB: The responses variables are all continuous.
Thank you very much and hoping for your kind and swift response.
Samuel
Jim Frost says
Hi Samuel, I’d start with a linear model. They can handle a mix of continuous and categorical IVs with a continuous DV.
Nathan says
Hello Jim,
I’ve read your book on Linear Regression: An Intuitive Guide and really enjoyed it. It is clear and well written. One question I have that I did not see in your book nor have found in your website (or others( is with respect to Deming regression. Specifically – do the GaussMarkov assumptions apply to this type of regression? Put another way, is a Deming regression the BLUE for cases where there are errors in both x and y measurements?
Thanks for your time and keep up the good work!
– Nathan
Fatimah says
Hi Jim! I hope you are doing well. thanks for this awesome post.
I need a suggestion on selecting the statistical test for my data… as it got too much complicated for me.
Hypothesis 1: there is a positive correlation between selfconcept and ideal self
Hypothesis 2: selfconcept and ideal self (negatively) impact the preference towards social norms
I want to see if “selfconcept and Ideal self impacts the social norm perception”, and if it does, what kind of relationship can be seen. More specifically, I want to see does “A and B (self concept) and C and D (Ideal self) impact [E1, E2], and [F1, F2, F3, F4, F5, and F6] (social norm perceptions)”, and if it does what kind of relationship can be seen.
Details:
The selfconcept is measured by 2 items, item A and B. Item A has 4 subitems and item B has 5 subitems, which is measured as 0 (no) and 1 (yes). The scale is calculated for each item (ex., the scale for item A = 0 to 4). Then the scale for whole selfconcept is calculated as 0 to 9. [a separate variable is also built by applying minmax normalization for each item and variable, which transformed data to 0to1 scale… (it becomes continuous var?)]. It is used as Independent Variable(s).
The Ideal self is measured by 2 items, item C and D. Each item has 2 subitems (= respondents were asked to select one options for each 1st priority and 2nd priority among several options), which is measured as 0 (brave) and 1 (calm) for item C and 0 (home) and 1 (profession) for item D. The scale is calculated for each item (ex., the scale for item C = 0 to 2). Then the scale for whole ideal self is calculated as 0 to 4. [a separate variable is also built by applying minmax normalization for each item and variable, which transformed data to 0to1 scale]. It is used as Independent Variable(s).
The norm perception is measured across 3 domains: domain E, domain F, and domain G. Domain. E consists of 2 variables “E1” and “E2” and has 5 items for E1 and 3 items of E2. Domain F has 5 variables “F1”, “F2”, “F3”, “F4”, and “F5”. F1=4 items, F2=2, F3=2, F4=4, F5=2, and G1=2 only. Each item is measured as 0 (not inclined) and 1 (inclined). The scale is calculated for each variable (ex., the scale for variable E1 = 0 to 5). [a separate variable is also built by applying minmax normalization for each variable, which transformed data to 0to1 scale]. They are used as (3) Dependent Variables.
Issues 1:
The Cronbach Alpha internal validity for selfconcept and ideal self is low: 0.377, and 0.035 respectively. This is because children were not considerate of ideal self much (I think).
Issue2:
Data for ALL variables are skewed and highly skewed for some. nonnormal distribution of data (children tend to be answering similarly)
My thoughts (But need your advice for better selection of test):
For Hyp1: Bivariate correlation between selfconcept and ideal self (although it seems unassociated or even negatively associate.)
For Hyp2: Hierarchical linear regression. First adding self concept the ideal self OR Moderator analysis: to check if ideal self moderate self concept and preference in norms and vise versa. OR Multivariate analysis to run every thing to run every thing together. [note: I have no skills in any of mentioned statistical tests]
I hope to get some valuable suggestion and guidance from you.
Best regards,
Jim Frost says
Hi Fatimah,
If all of these measurements are individual values for something like a Likert scale, you’d need to use ordinal logistic regression because the dependent variable is ordinal. However, if the scale has 10 possible values, there is some justification for treating it as a continuous scale. There is some debate over that.
It’s trickier including ordinal variables as independent variables. You’ll need to include them as either categorical or continuous. There are several pros and cons for both ways. That’s too long for an explanation in the comments sections but I write about it in my regression book.
It seems like you need to use regression analysis, but all the ordinal variables will make it trickier. I’d suggest consulting with a statistician at your institution because it is a tricky scenario and there isn’t always agreement on how you can handle Likert items.
Cecilia says
HI Jim,
Thank you for this. I am trying to run different Machine learning models to predict Renewable Energy Generation(continuous), with total salary, price of energy, government incentives, price of transportation (all continuous variables) as predictors. I did a linear regression, I don’t seem to be able to do a logistic regression (maybe because its for categorical variables as you just explained) and then I will try regression trees, random forest, lqa, lda, and knn. Is this correct or is this not possible?
ivan says
Hi Jim, I would like to ask which should i use to asses the influence of 1 independent variable (with 3 categories) to a dependent variable.
my problem is this
what is the impact of basic psychological need(autonomy, competence and relatedness with psychological well being.
thanks in advance.
Jim Frost says
Hi Ivan,
If your DV is continuous and the IV has three groups, you can use least squares regression. However, if you have only that one categorical IV, you could just use oneway ANOVA. To see an example of that procedure, read my post about using Excel to perform oneway ANOVA.
[email protected] says
Hello Jim,
Thank you for your insights.
My dependent variable is Fire incidence rates (Fire incidences divided by the population in the corresponding area), which is a ratio, hence it is a continuous variable (am I right?). I have 16 independent variables, most of them are count variables. Some examples of my independent variables are: Number of Households in an area, Number of people who smoke in an area, etc. Can I use multiple linear regression for this scenario, despite my independent variables not being continuous?
Your help regarding this will be massive, thank you!!
prunelle says
thank you for the informations, i would like to know if i can use composite variable to make the ordinal logistic regression. I have 1 dependent variable which is a likert scale with four questions, i make the mean by combining the 4 questions into one question for my ordinal dependent variable item.
Jim Frost says
Hi Prunelle,
Yes, researchers will often combine Likert items by either summing or averaging them and create a continuous variable. Amongst statisticians, there is some debate over whether that’s valid. If you believe that the differences between your Likert values represent a constant difference (i.e., the magnitude of the difference between a Likert score 5 and 4 is the same as the difference between 4 and 3, and so on), you’re on more solid ground.
If you use that approach and your DV is the composite variable (the mean of multiple Likert items), you’d use linear regression or other regression that can use a continuous DV. Given the nature of the composite variable, you’ll need to be extra careful about assessing the residuals for lack of fit!
Hilman says
Hi Jim,
I have 1 DV and 33 IV (26 dichotomous, 6 continuous and 1 ordinal).
Have done the correlation using spearman coefficient and the linear regression for the model.
But it seems that it’s not really adequate fit from the residual check of linear regression.
Which one should I try for the next step, a multinomial or polynomial?
Any advice will be greatly appreciated.
Thank you.
Hilman
Jim Frost says
Hi Hilman,
That’s a lot of variables. Be sure that you have a large enough sample size to avoid overfitting your model, which can produce results you can’t trust.
As for next steps, if you’re seeing patterns in your residuals, you definitely need to make changes. You can try graphing residuals by specific variables to determine if there’s curvature you should be fitting. That includes adding polynomial terms to your model to fit the curvature. You should also check and see if your DV is highly skewed. That can make it hard to get good residuals–you might need a transformation in that case. Although, I always recommend transformations as a last resort.
For more advice and tips about fitting models, read my post about fitting the correct regression model.
Pritam says
Hello Jim,
I work on one linear regression model( very simple dataset having only two features). I got Rsquared value nearly 77%. I have to improve that value. Could you please tell me which transformations are there to improve my Rsquared value( or accuracy of linear regression model)?
Jim Frost says
Hi Pritam,
Rsquared isn’t something that you can just crank up as needed. Trying to go beyond the natural limit of the unexplained variability in your data will cause problems–namely results that you can’t trust. If at all possible, you should conduct some research and determine what other studies in the same area have obtained for their Rsquared values. If they have gotten higher values, then you have reason to see if you can increase yours.
There are some things you can legitimately try. First, check the residual plots. If you see patterns in your data, then you know there are changes you can make to get a better fit. Maybe include polynomials for curvature? Or an interaction effect. If your DV is highly skewed, transforming it might be in order. Try a BoxCox transformation. However, I always recommend data transformations as a last result because they complicate interpretation and the model less intuitive.
For more information, read my post about fitting the correct regression model. Lots of tips and practical advice in it!
Fikadu Tola Seyoum says
my dependent variable is continuous,but im categorizing int four groups.
serum bilirubin as 0.21051,0.520.71,0.720.99 and 0.991.00 so which model of regression i use to analyse data
Jim Frost says
Hi Fikadu,
If you recode your DV in that manner, you’ll be converting continuous data to ordinal data. Hence, you’d need to use ordinal logistic regression. However, I’d strongly recommend against recoding your data like that, if at all possible, because you lose so much valuable information.
Gamachu Diriba says
Hi Dear Jim
I have dichotomous dependent and five points likart Independents. Can I Analysis with binary logistic regression ?
My objective is to understand contributing factors to dependent.
Please 🙏
Jim Frost says
Hi Gamachu,
You’re dichotomous DV is right for binary logistic regression. However, Likert scale IVs pose a problem for all forms of regression, assuming you’re talking about individual Likert scores for each observation rather than an average. Likert, and ordinal data in general, have characteristics of both continuous and categorical data. And you’ll need to choose one of those ways to enter them in the model. That’ll depend on the nature of your data and the goals of your analysis. I cover that in my book about regression analysis.
Hannah says
Hi Jim! May I ask what can you best recommend if I were to correlate multiple discrete ID and one discrete D? In my case, I want to correlate informal settlers, sq km of impervious pavements, and amount of waste disposal (ID) to number of flood occurrences per year (D)
Thank you! and I wish you well!
Jim Frost says
Hi Hannah,
I’ll assume that by ID and D, you mean independent variables (IVs) and the dependent variable (DV), respectively.
I don’t know what informal settlers are. However, it appears like your DV is a count variable, flood occurrences per year. In that case, you might want to try Poisson Regression or the related negative binomial regression. They can model counts of occurrences. If there are enough floods per year, say 30 or more, the Poisson distribution approximates the normal distribution, and you might be able to use linear least squares regression. However, smaller counts will be skewed, and the other types would tend to provide a better fit because they’re designed to fit skewed counts.
I hope that helps!
Laurine says
Hi Jim,
I would like to investigate an mediation effect en will do a regression analysis for it. I have two categorical independent variables; both having 2 categories. My dependent variable has 3 (or maybe 4; I still have to do my research) categories. Is the nominal logistic regression the right type of analysis.
Hope you can help me!
Jim Frost says
Hi Laurine,
That sounds like the right choice based on what you wrote as long as the DV categories don’t have a natural order. If they have an order, such as high, medium, and low, then you’d need ordinal logistic regression. But, if not, then nominal logistic is correct.
Judian says
Hi Mr. Frost
I have a continuous dependent variable and 4 continuous independent variable and 3 categorical independent variable.
Will Multiple Linear Regression statisfy all 7 the predictors?
Jim Frost says
Hi Judian,
Multiple linear regression can handle that combination. Of course, whether you it can provide an adequate fit depends on the nature of the relationship between the variables. But it’s a good place to start.
I’ll add that with so many predictors you’ll need a good sample size to avoid overfitting–at least 70 observations but potentially many more depending on how many levels each categorical variable has.
Muddasir Khan says
Hi jim i hope you’re doing well . i have one independent and 5 dependent variables like to find impact of fdi on Company performance here fdi is independent and for performance i took five dependent variable like return on asset, return on equity, net profit margin, total assets, and total equity, please suggest me best model for this analysis thank you so much
Tewodros says
I want to conduct an anlysis of dependent variable community support for ecotourism development and independent variables like
Personal economic benefit
Social and Environmental benefit
Community attachment
Local benefit
Which regression analysis should i supposed to employ. thank you in advance for your help
Jim Frost says
Hi, if your DV is a continuous variable, I’d recommend starting with linear least squares. If your DV is a different type of variable, look for that type in this post and see which type of regression will work with it.
Justine says
Hello Mr. Frost! I am a student and currently working on a research with one dependent variable, 5 independent variables and 1 moderating variable. A survey will be conducted using the 5 likert scale on agreeableness category. The research objectives includes: to determine the dominant factors that affects the dependent variable and assess the significance of the relationship of the variables. Is PLS SEM analysis applicable for this? I am also confused on whether my data shall be classified as ordinal or interval. Thank you in advance!
Henrik Herrebrøden says
Dear Jim,
Thank you so much for this wonderful website! Great to have someone able to break down stats into plain language.
I have a question about adding categorical covariates into a regression model. Here is a sports study I am working on, investigating relationships between games played for various national teams in soccer:
Outcome variable: Number of games played for senior national team
Predictor variable 1: Number of games played for under21 national team
Predictor variable 2: Number of games played for under19 national team
Predictor variable 3: Number of games played for under17 national team
However, I have a dataset with players from six different countries. I want to investigate whether nationality makes a difference to the relationships here, i.e. if the predictive value (of my predictors) changes depending on which country the player represents.
So my question is: Does it make sense to run a multiple regression model with nationality as covariate? If so, do you know how to run this in SPSS (that is, where to enter the nationality variable)?
Your help would be highly appreciated!
Best wishes,
Henrik Herrebrøden,
PhD Student
Rajiv says
I have one categorical independent variable (Work configuration) and two dependent variables one is stress (Metric) and other is stress coping (categorical). What is right approach to analyze
Jim Frost says
Depending on the number of levels in the categorical IV, you can use either a 2sample ttest or oneway ANOVA for the continuous DV.
For the categorical IV and categorical DV, you can use either the chisquare test of independence or nominal logistic regression.
Egla says
Hello Jim,
Thank you for your explanation.
I have 3 independent variables (1 of them us demographics info so can be a mixture of continuous snd categorical and do the other 2 IV are continuous as the both measure time spent on phone).
My independent variable parenting behaviour measured on a likely scale. What’s the best regression analysis to use?
Your insight would be greatly appreciated.
Egla
Dugasa Tesfaye says
Is it possible to run regression analysis for dependent variable data categorized by interval? I made interval to make the encoding easier but I stuck whether it will take me to an interpretable result?
John Santoyo says
Hi Jim, I’m excited to begin reading Regression Analysis! I hope it’ll equip me to answer questions such as the following for regression:
In regression analysis, is there such a thing as an “unrelated” variable? For example, can I use a marketing campaign total spend amount (Pinterest spend) to predict revenue in a separate campaign channel (Facebook revenue) that also has a spend dependent variable (Facebook spend). So in this example, would Pinterest spend be “unrelated” to Facebook revenue, and thus not useful as a dependent variable for revenue?
I can clarify, if needed. Thanks again for this resource! It’s really helping calm my nerves as I start my career as a Data Analyst…
Jim Frost says
Hi John!
I’m so glad you’re reading my website!
Yes, there is definitely such a thing as an unrelated variable. If an independent variable (IV) has a coefficient near zero and a nonsignificant pvalue (usually defined as greater than 0.05) then you have indicates that the IV might be unrelated. Although, it’s possible that is related but your sample is too small, data too noisy, fluky sample, etc. to detect the effect.
I’m not exactly sure what you’re asking. I can’t tell you whether the Pinterest spend predicts Facebook spend or not. What do the data say?
Shamine Macwan says
Hi Jim,
Since the dataset has 8000+ columns is it feasible to use least square regression.
Jim Frost says
Hi Shamine,
Yes, that is no problem at all for least squares regression.
Shamine Macwan says
Hi Jim,
Your post is very informative. I want to predict salary so my predictors are Industry, Role, Technical Skills and average experience. Industry, Role, Technical Skills are categorical variables so after converting them and creating dummy variables these predictors will have binary values(0/1). So I am not sure which regression model can be used here.
Jim Frost says
Hi Shamine,
Because you have a continuous dependent variable (salary), you can use least squares regression. It can handle the continuous and categorical variables in your model. I actually use a very similar example in my regression book to illustrate how to interpret the different types of variables.
Abhinandan Chakraborty says
Hello, my IV is Income which is continuous variable but in my DV my respondents can write 3 bicycle brand names which appears at the top of their mind. So now I have majorly 6 brand names and for each I have 0 or 1. I wish to check whether Income plays any role in Brand recall. Since I have now 6 data columns I am unsure about which regression I can run and how ?
Jim Frost says
Hi Abhinandan,
Because you have a categorical DV, you should try using nominal logistic regression. It’s also known as multinomial logistic regression. In your case, you can use it determine whether income predicts the probability of an observation falling into the different brands.
Veronica says
Hi Jim,
Thanks for the great info! I have a question, I am trying to determine which combination of methods gives me the best results for an analysis. I have a vector of enrichment values for my dependent variable (the output of
my analysis) , and I have a binary matrix for my independent variable (combinations of methods: 1 if something is used and 0 if not, the matrix is 36 rows by 10 columns). What regression model would you recommend and how should I apply it on these variables?
Allan says
Hello,
May i ask if it’s possible to conduct a moderation analysis with an independent, dependent and moderating variables that are all ordinal? Which regression would work best in that situation.
Thank you.
Jim Frost says
Hi Allan,
Yes, you can. However, there are some additional considerations. The ordinal dependent variable requires that you use ordinal logistic regression, which can handle moderation (interaction) effects. However, entering ordinal variables as independent variables will require you to make some choices. You’ll need to enter them either as continuous or categorical variables. The decision depends on the nature and amount of your data along with goals of your analysis. Unfortunately, I don’t have a blog post to point you towards about all those details, but I do write about it in my regression analysis book.
I hope that helps!
Bab says
Hi Jim,
Many thanks for your informative blog.
I have a large data set (more than 25000 data) investigating the effect of one categorised variable( in this case is when the window is open and when it is closed) on the building temperature (which is my dependent variable). There are other independent variables which have effect on the building temperature including solar radiation, air temperature and illuminance (these are three numerical independent variables). Now I want to find out the effect of my categorised variable (window open and close) on the building temperature (while other three variables are effecting on that as well. It will be much appreciated if you can provid eme with your advice in this matter.
Jim Frost says
Hi Bab,
I’d start with least squares regression because temperature is a continuous variable. It can also handle your mix of categorical and continuous independent variables.
Yeshambel Azmeraw says
dear JIM my DV is saving and my independent variable are age,sex,family size,employment level,education level martial status and incom.SO what type of recommendations are gives to me from you?
Jim Frost says
Hi, because savings is a continuous variable, I’d start with linear regression and see if you can get a good fit with that. You have a mix of continuous and categorical IVs, which is just fine for linear regression.
james mcloughlin says
Good morning Jim You have a remarkable talent as an educator and as a welcomer. I’m reminded of the multitude of scenarios ….statistical scenarios …that have emerged from the Titanic tragedy. PERHAPS YOU CAN REFRESH OUR MEMORY WITH RESPECT TO ONE DICHOTOMOUS INDEPENDENT AND ONE DICHOTOMOUS DEPENDENT VARIABLE USING GENDER AFFECTING SURVIVAL …ON THAT FATEFUL VOYAGE. WHEN YOU HAVE HUNDREDS OF MALE/FEMALE OPTIONS FOLLOWED BY HUNDREDS OF SURVIVAL AND NONSURVIVAL. ARE ANY OF THE REGRESSION APPLICATIONS APPROPRIATE? THANKS, JIM
Jim Frost says
Hi James,
Thanks so much!! I appreciate that!
There are several ways to handle that type of analysis. One would be to use a chisquare test of independence using gender and survival. That analysis will tell you whether an association exists between the two variables. However, that’s not regression.
In terms of regression, I’d use binary logistic regression. I’d set up the analysis so that survival (yes/no) is the dependent variable. You can then include various independent variables, include gender, class, age, etc. to determine which variables played a role in survival and the nature of that role. Here’s a link to a post I wrote that uses binary logistic regression for a different scenario, in case you’d like an idea of how it works.
I hope that helps!
Deepanwita purohit says
Hi Jim,
Very useful post. I am studying the effect of genetic diversity (independent variable) on lifetime breeding success (LBS, dependent variable) in a wild pig species. The dependant variables are all count data ranging from 6 to 45, whereas the independent variables are continuous data. Should I use poisson regression or negative binomial regression (assuming overdispersion in the dependant variables) to test the correlations?
Thanks
Deepanwita
Troy says
Jim,
I am trying to predict interval y with ordinal level x1, x2, and x3 as well as interval level x4, x5, etc. I suspect (with good reason) that the ordinal level IVs are not very reliable, yet they seem to be more predictive (higher Adj B squared) then the presumably reliable interval level IVs. Could the ordinal variables be overestimated in the model? If so, how can I correct the problem?
Troy
waid kaled says
Hi Jim,
I am conducting research using two psychological instruments to assess the quality of life of 150 patients with chronic illness. the instruments are selfassessment Likert Scale with final result dependent variable calculated in scale from 0 to 100 measuring 4 different domains. the independent variables; gender, age, education level, marital status, income, residency, number of family members, and number of complications due to illness. what is the model type that is useful to compare the independent variables as predictors of outcome dependent instrument scale? what statistical test should be used to compare between these two instruments knowing that they are using completely different scaling technique ?
appreciate your kind help
best regards
Lara says
Hi Jim!
I’m helping conduct research using a categorical binary independent variable and a discrete numerical dependent variable. The categorical binary independent variable is originally a nominal variable, but I can choose to transform the data into binary form as the research allows for it.
I was wondering what the best method of analysing would be. How can I go about understanding the correlation in this case in terms of statistical tests?
Keturah says
Hi Jim!
I am currently doing my Psychology Honours thesis and am investigating the relationship between parental separation and conduct disorder (crosssectional in children).
My main IV in binary (parental separation/nonparental separation) and my DV is continuous (conduct disorder rating scale). I am also hoping to control for other variables / other IVs (economic status, peerrelated problems, gender, ethnicity and social support).
I have 3 aims so far. Firstly, I want to explore the intercorrelations between these variables on the DV and examine their one by one relationship. Secondly, I think I want to do a multiple regression with conduct disorder as the outcome for each of these IVs. And lastly, I think I want to use a moderation analysis to examine which of these IVs dampen or strengthen the relationship between parental separation and conduct disorder.
Would you have any advice or feedback regarding if I am on the right track?
Aswell, I would really appreciate any advice on what statistical analyses I might be able to use to measure these aims or how I might go about rewording these aims so it sounds better.
Thank you for your help!
Keturah
Jim Frost says
Hi Keturah,
Most rating scales are ordinal. If that’s the case, you’ll need to use ordinal logistic regression rather than linear regression. So, be sure you understand the nature of your DV. If it’s truly continuous, you can use multiple linear regression.
It sounds like regression, whether ordinal logistic or linear multiple regression, would be appropriate. And, with either type you can model moderation effects. I write a post about including and interpreting interaction effects in regression models. Interaction effects and moderation are different names for the same thing.
It sounds like you’re on the right track!
RABIA NOUSHEEN says
Hi Jim
I would like to know that if my response variable is development time, measured in days, then what family of GLM is suitable?
Thanks a lot for your help
Jim Frost says
Hi Rabia,
Generally speaking, time is considered a continuous variable and you can treat it as such. OLS would be a good place to start!
Louisa Balster says
Hi Jim,
great post – thank you for that! But I coudn´t really find the right regression type for me (maybe you could add some examples). My DV is a variable with a value between 0 and 6 (a company gets a value of 6 if they have an environmental strength in each of the six areas). Do you have a recommendation on what regression type would be suitable? That would be highly appreciated 🙂
Cheers,
Louisa
Jim Frost says
Hi Louisa,
Is your DV and ordinal variable? It sounds like it but the info is sparse. If it is an ordinal variable, use ordinal logistic regression. Look at that section in this post for an example.
Mohammad says
Hi Jim,
First, thank you for all your helps..This page provides a quite comprehensive framework for regression analyis..
Need a little bit help on my current research about the behaviour of households.
– I have a dependent variable (:attitude change) with 3 subvariables (let’s call them a,b and c) (all of them are categorial, 5likertscale).
– There are also various independent variables, which are mostly categorial (5likertscale), and a few nominal (gender, etc.).
– In addition, there are 3 independent groups of observations (households) (each group > 100 samples).
Ideally, I would like to explore the influence of the independent variables on each of the dependent subvariables (a,b,c) for each group, separately. But not sure if it is feasible.
There are two concerns here:
1 Is it better to merge the dependent subvariables together and investigate only the main DV, or it is better to model the subvariables separately? Both are possible due to the nature of the topic, but not sure if it is recommended for Regression analysis.
2 As there are 3 different groups, will there be 3 different models for each group separately, or all of them come in only one model?
I guess the right model to use is the ordinal logistic regression analysis. The initial idea was to use mann whitney u test & kruskal Wallis test, but I am lost on choosing the right method.
Thank you very much in advance..
Chaitra Deshpande says
Hello Jim, thank you for the very helpful and crisp, lucid article. I am working on a data where the dependent variable has 10 categories and the independent variables are all categorical. Could you help me understand whether multinomial logistic would be a good option? If not, what other kind of regression can I use?
Thank you
Jim Frost says
Hi Chaitra,
If the level for the DV have no natural order (i.e, they’re categorical aka nominal), then use nominal logistic regression. However, if they have a natural order, your DV is an ordinal variable and you should use ordinal logistic regression.
Tizita says
Hello Jim I need to know which regration is suitable for my title” the effect of HRM practice on employees’ retention? And also I have demographics and HRM variable so can I test by logit in one block or measure independently. Thank you for your response and with regards.
Miduna says
Well i am comparing the population with the co2 emission worldwide the population is in the x axis and the co2 emission is the y axis so may i know what regression to use?
Jim Frost says
Hi Miduna,
Least squares regression would be a good place to start because you have a continuous dependent variable (CO2 emission). However, you might have some added difficulties. I’m assuming you’re measuring both population and CO2 over time. If so, as the CO2 tends to increase, you might experience heteroscedasticity. Additionally, if you’re tracking over time, you’ll need to assess autocorrelation in the residuals and likely need to include time related independent variables to model it.
Jag Smith says
Hello sir, what if the type of dependent variable is interval? What regression model do I need to use?
Jim Frost says
Hi,
Interval scale variables are a form of continuous variables. Consequently, a good place to start would be least squares regression.
hari says
Hi jim , I am dealing with a regression problem predicting rejection rate of a product after ordered.
due to 95% of products were not returned my data is highly inflated towards 0 . Can you suggest on any approach or algorithm to handle it more efficiently than building a normal ols regression.
Jim Frost says
Hi Hari, if your dependent variable is binary, such as rejected/not rejected, you can use binary logistic regression.
Lukenge M. says
Which data is most suitable for linear regression?
Jim Frost says
Hi, you’ll find that answer in this post. Look in the early sections.
Alina says
Dear Jim,
Thank you for your great and valuable insight. I am still a bit confused on what model to use for my regression. I want to look at the relationship between financial ratios and company bankruptcy for the year 2020. I am thinking a binary logistic regression model would be the right approach but would like to confirm with you whether I am on the right track or if I should switch the model.
Thank you!
Jim Frost says
Hi Alina,
If your dependent variable is binary (bankruptcy, no bankruptcy) then binary logistic regression is a good choice!
Mirwaise Khan says
Hi,
Sir Jim, I have One DV such as GDP and two IV such as External Debt and Debt Servicing. Would you please guide me, Which type of Regression analysis should I use?
Hanadi says
Hi Jim,
I have 5 independent variables (continuous), 1 dependent variable (continuous), sample size is 63, i did normality test and i found some variables not normally distributed based on qq plots.
1 Initially, I compute the correlation coefficient to see the correlation between variables
2 because i have a small sample size i entered (only) the correlated variables in the linear regression and i excluded the non correlated variables and the variables with a high correlation coefficient above or equal (.8)
3 I used enter method: the model is significant but each variable was not
4 then, i used a stepwise method: i got one varible significant.
My question is the previous process true? and which method could i choose in linear regression (enter or stepwise)?
jasper says
Hi Jim,
I have 5 variables 1 DV, 1 IV and three presumed mediators, 4 out of the 5 variables are not normally distrubuted. Which regression is most fitted to test this relation?
Jim Frost says
Hi Jasper,
Least squares regression is a good place to start! The IVs do not need to be normally distributed. Be sure to check the residuals for normality. If they aren’t normally distributed, there are various corrective measures you can try.
FARZANEH CHANGIZI says
Hi Jim
I need your help
I have a dependent variable which is continuous (between zero to twenty). it is about negative feelings and emotions.
I used multinomial regression analysis and generalized linear mixed effect models but I didn’t get a good result.
What is your recommendation for me? to use other types of regression modelling?
By the way, I have several dependent variables which are categorical or continuous.
Jim Frost says
Hi Farzaneh,
Unless your DV values are nicely in the center of that 0 to 20 range, you’ll probably need to use a two limit Tobit model. The problem is if your data are two close to one limit or the other that they’ll be highly skewed, making it hard to get normal residual. Also, the model will predict outside the possible range, giving nonsensical results. Those aren’t necessarily problems for least squares regression when your data are more in the center of the range. Check those residual plots.
However, it is a problem, I hear that a two limit Tobit model is a good option. Unfortunately, I don’t have any practical experience with that type of model to share with you. But it might point you in the right direction.
nur says
Hi Jim,
I have 4 independent numerical (integer) data and 1 dependent continuous (float) data . Which regression algorithm should I use for classification?
Jim Frost says
Hi Nur,
Because you have a continuous DV, a good place to start is least squares regression!
Dewidine says
Thank, Jim!
Dewidine says
Good day Jim, this was very useful, but I do not see multinomial logistic regression. I think that is the analysis I need to use. I want to know whether the two categorical variables influence what group a taxa falls into. This is my data, I have a dependent variable – Group (three categories) and then my independent variables is again categorical data – range (Single vs Multiple sites) and Life form (three categories). I have been running the model separately for the two an also doing a full model with both variables. I want to know whether this type of test is correct for my data? And are there any important things to be aware of when using this type of test, like assumption that need to be met. I really don’t understand the result that I’m getting.
Jim Frost says
Hi Dewidine,
I have that type of regression listed as nominal logistic regression. It’s an alternate name for multinomial logistic regression. And, yes, that is the correct type for your data!
As for assumptions, you want to check the residual plots as normal. Look for things such as homoscedasticity (constant variance) in the residuals. Generally speaking, it’s better to include both IVs in the model rather than fitting separate model. That way each variable is estimated while holding the other variable constant. It sounds like you’re on the right track!
Arnþór Freyr Sigþórsson says
Thanks a lot for the reply Jim, my DV ranges from VERY close to 0 and around 8%, mostly its extremly low.
Jim Frost says
Hi, in that case, I’d guess that least squares regression won’t work and you’ll need to use one of the other methods. You can always try OLS. Just be sure to check those residual plots! But, that’s awfully close to the lower bound, which is likely a problem.
Jane says
Hi Jim,
If I were to use a continuous independent variable (e.g. emotion regulation) with two levels (e.g. cognitive reappraisal, expressive suppression), with one continuous dependent variable (e.g. relationship satisfaction), would it be possible to use simple linear regression? Thanks so much!
Jim Frost says
Hi Jane,
It sounds like your independent variable is actually a categorical variable with two levels. Continuous variables don’t have levels like that. You can perform simple regression with one categorical independent variable. However, for simplicity, you can perform a 2sample ttest and assess the mean difference between your two groups.
Arnthor F. Sigthors says
Hi Jim. I am working with a continuous dependent variable (engagement rate of posts) and 2 categorical independent variables(type – 4 categories and type2 – 4 categories) which represent what kind of post it is. Is logistic regression the way to go for me?
Jim Frost says
Hi Arnthor,
When it comes to selecting the type of regression analysis, the the nature of the DV is front and center. In your case, you actually have a proportion or percentage, which isn’t quite a continuous variable. If it’s not too close to 0 or 100%, you might be able to model it using least squares regression. I’d try that first. Check the results and residual plots. If it works out for your data, that’s the best route to go.
However, if the percentages tend to be near 0 or 100, the model will predict outside possible range for values that don’t make sense. In that case, you might not be able to use least squares regression but instead fit a two limit Tobin model to account for the range of 0100%.
Logistic regression is not a good choice for you because that requires categorical, ordinal, or binary DVs. You don’t have that. You do have categorical IVs, but that doesn’t mean you should use logistic regression. Again, it’s more about the type of DV.
Best of luck with your study!
Uzair says
Hi Jim,
Thanks for all your work explaining different models and answering questions. I am currently working on statistical analysis with a categorical dependent variable and two categorical independent variables (based on ethnicity and religion). The dependent variable has three categories (“Yes”, “No”, “I don’t know”). I have tried the multinomial logistic regression model with these variables but I am getting extremely high pvalues to my surprise. I have considered converting the dependent variable into a binary variable (removing “I don’t know” category) to see if that can improve the results. Any advice on what I should do here? Thanks
Jim Frost says
Hi Uzair,
You can certainly try that. It is a bit of cherry picking the analysis because the first one didn’t give you results that you expected. Ideally, you’d state up front that you were going to process and analyze the data that way (just use two categories). In the write up, be upfront about that process (the why and how).
You don’t want to do excessive cherry picking because if you do that, you’ll always find something significant even if it’s just a chance correlation. However, if that’s the only post hoc change you’ve made to your plan, it doesn’t sound too bad.
jayden says
Hi Jim!
Would you know why a logit regression model with interval variables would be favored over a logit regression model with categorical variables and vice versa?
Jim Frost says
Hi Jayden, logit regression models are designed to model the probability of an event for binary or categorical variables (more than two levels). Events are categorical outcomes such as pass or fail, or scratch, dent, and tear.
james mcloughlin says
Hi jim…such a gifted teacher! Thank you.
Can you do a regression with one categorical independent variable predicting one categorical dependent variable?
EG. Gender predicting admission to MIT..
Thanks, Jim
Jim Frost says
Hi James,
Yes you can! In your case, you’re talking about a binary dependent variable because it has only two levels (presumably), admitted and not admitted. In that case, you’d use binary logistic regression and it’s fine to use a binary (or categorical) independent variable.
If your dependent variable had more than two levels, you’d use nominal logistic regression.
Nimra Azhar says
Hi. Thank you so much for this. I have a question. Is there a regression analysis that can be used for overlapping categories of dependent variable?
Example: I am using a sample of rural agricultural households where individuals are engaged in multiple paid (business or wage work) and unpaid activities
So my y variable is broadly classified into 3 categories: business, wage, unpaid
Can Maximum Likelihood models be used in this case?
ntombi skosana says
Hi Jim
Thank you so much for your response. It has helped a lot.
I really appreciate your assistance.
Jim Frost says
You’re very welcome, Ntombi!
Denton says
Hi Jim,
Thanks for the reply. I thought about using ANOVA but the data isn’t normally distributed and the variances are unequal so normal ANOVA and KruskalWallis don’t work. I asked my supervisor about using Welch’s ANOVA and was told to use regression instead as it is easier to interpret and relatively robust to non normal data.
The dependent variable is the number of people who have a disease. So across 10,000 doctors, each doctor uses one OS, and records how many patients they have with e.g. diabetes. I’m trying to see if the choice of OS affects the recording of diabetes.
The data isn’t normally distributed, but with log(), it does normalise the data but I would also need to +0.1 (arbitrarily chosen constant, does it matter much the constant is?) to every data point as there would be log(0)s that prevent further analysis eg with qqnorm. Should I normalise the data and all do analysis (e.g. I guess at my supervisors suggestion, regression) on log()+0.1 data?
Thank you.
Jim Frost says
Hi Denton,
Ftest ANOVA and regression are two sides of the same coin because they use the same math “under the hood.” It’s true that ANOVA can be robust against deviations from normality, but it depends on how many samples you have per group. Read my post about parametric vs. nonparametric analyses. That has pros and cons for both. But look particularly for a table with samples for different analyses. It’ll show you how large they need to be so that you generally don’t need to worry about the normality assumption.
However, regular ANOVA and regression are susceptible to violations of the assumption of equal variances. I talk about that in this post about Welch’s ANOVA. You can’t get out of that assumption by having a larger sample size. If your variability is substantially different, you really should be using Welch’s ANOVA and NOT the Ftest ANOVA, regression, or nonparametric analyses.
You might not need to transform the data with large enough sample sizes unless your data are very skewed. If your smallest group is 50, you have a good enough sample to not worry about nonnormality unless it’s very skewed. I suppose the transformation could also solve your nonconstant variance problem too. I could see going that way as you suggest. However, you can still consider Welch’s ANOVA and potentially analyze your data in the raw data units, which I like to do whenever possible. You’re probably fine either way.
ntombi skosana says
Hi Jim
My DV is the the % exports for sales of volumes. Is this a continuous?
There are a lot of zeros for those who did not export for my time period of interest. I do not want to drop these as they are true zeros – as they represent firms that did not export anything for whatever reason. Can I just go ahead and run my OLS with this DV ?
What can I do with the ” I do not know” responses?
Jim Frost says
Hi Ntombi,
Percentages as a DV can be a bit tricky. If the percentages are close to zero or 100 percent, the distribution tends to be skewed. If so, you might need to transform it. I’d graph the percentages to see what they look like in a histogram. Are they very skewed? If not, OLS might be fine. Try fitting a model and see how the residuals look. If they look good, that’s a great sign you can use OLS. If not, try transforming the DV to get a more normal distribution.
I hope that helps!
Denton says
Hi Jim,
I’m not sure how to go about analysing my data statistically.
I have 4 computer Operating Systems that doctors can choose from, that all record 5 different disease conditions.
These 4 OS’ have different amounts of data – one OS is only used by 50 doctors whereas another is used by 3000 doctors(unsure if this makes a difference).
I want to identify variation in the prevalence rates (how common the condition is) of the 5 different conditions and if this is due to the different OS’. i.e. Does the OS used cause a difference in whether or not a disease is recorded. The descriptive statistics are all pretty different.
Thanks!
Jim Frost says
Hi Denton,
It sounds like you need to use oneway ANOVA. You have one factor (OS) with four levels (the different OS). It sounds like you have a continuous dependent variable, although I wasn’t totally clear about that. If you want to compare the means between the groups and determine whether they are statistically significant, oneway ANOVA is what you should use. I write about using oneway ANOVA in Excel. Even if you’re not using Excel, it should give you an idea of what it can do.
I hope that helps!
Pranab Chatterjee says
Hi Jim! Thanks for this great post! Just wondering why you did not include the logbinomial model in this post. Could be a good fit right after the binary logistic regression point, perhaps?
Aishwarya Kanitkar says
Hello Jin,
Thank you very much for this article and the article on multicollinearity, they really helped me.
I am struggling with my Master Thesis. I am analyzing the impact of food product attributes on organic food buying tendency.
On one hand, (for independent variable) I have a constant sum scale for 5 food attributes and for the dependent variable I have the number of organic food people buy out of 10 grocery products (so 1 to 10 – higher the number of organic products higher the buying tendency)
I am confused as to which type of regression to use, given that my independent variables would be correlated.
Any help is appreciated!!
Thank you,
Aishwarya
Jim Frost says
Hi Aishwarya,
I’ve never used a constant sum scale before. However, I’d imagine you’d need to pick one or several but not all of the attributes and use them for the IV(s). I don’t really know if there is a recommended practice in terms of how many attributes to include. Maybe there are one or two that are particularly correlated with the DV? You can’t use all of the attributes because that produces perfect multicollinearity for a constant sum scale.
Your DV is a count variable if I understand it correctly. I’d try Poisson regression or possibly negative binomial. Look in the count DV section for more details about that type. I’d try those types first. Being a count variable, it’s likely to be skewed but those regression types can handle it. If the DV isn’t too skewed, you could conceivably treat the DV as continuous and use linear least squares regression. But, that depends on the properties of the data.
I hope that helps!
Claudia says
Yes! It really does 🙂 thank you so much!
Mark says
Thanks for the excellent post. It seems that regression works for both continuous and binary target label. However, if in a situation, the target label is continuous but is between 0 and 1. What will happen if we transform the target to binary values (0 and 1) with an indicator and fit the regression model? How will the model parameter and standard error change compared to the fitted regression with the original continuous label?
claudia says
Hi Jim,
Super useful page thank you! I am struggling to find examples that describe what to do with a binary categorical dependent AND independent variable. I want to know if the mating status of a father (mated/unmated) predicts the mating status of his son (mated/unmated). Would this still be a binary logistic regression?
Any help greatly appreciated!
Thank you 🙂
Jim Frost says
Hi Claudia,
You can certainly use binary logistic regression with both a binary dependent variable and a binary independent variable.
However, if you have only those two variables, you can also/instead perform a 2sample proportions test. That’s easier to interpret (for you and your readers). That will answer the question of does the proportion of mating sons differ by the mating status of the father?
If you want to see a 2sample proportions test in action, read my post about Flu Shot Effectiveness, where I use a 2sample proportions test on real data to determine whether the proportion of flu infections differ by vaccination status.
If you have additional IVs that you didn’t mention, then you’d need to use binary logistic regression.
I hope that helps!
Santhosh Srinivas says
Greetings Jim
Great post , I have a query.
I am working on a study where dependent variable is num of records(values start from 0 to a million) , around 6500 rows of data. which is the best mode to analyse this? please help.
Bewketu says
Hi, I have question on my research preparation. I have two dependent variable which is categorical(yes/No). I used binary logistic regression. but my question is .how i can do the analysis? Is that separately or mixed? How? I thanks who can helping me!
rukky says
Hi Jim
Very happy to have found you.
What is the best regression to determine asset allocation for a group of firms
Dependent variable equity asset allocation
independent variables – leverage ratio, market cap, stdev of operating cashflow, book value of equity etc
Jim Frost says
Hi Rukky,
Typically, you don’t choose regression models based on the subject area. Instead, start by looking at the type of DV and using that to choose a type of regression model as I describe in this post. If equity asset allocation is a continuous variable, I’d focus on that section in this post.
Adila says
Hi Jim.
I am currently doing my study on technology investment decision.
The DV is a binary (1= firm invest in technology; 0=firm do not invest in technology). There are about 6 IV with different types of data (i.e dummy, ordinal, continuous)
The time horizon is longitudinal (i.e panel data firm=n and year=i)
I am not sure which method is suitable. Is it panel data logistic regression? I also read some articles that use Generalized Method of Moment analysis.
Please help me. I need your opinion.
Thank you
Martin says
Hi Jim,
thanks a lot for the great post!
I am struggling with finding the appropriate statistical method for my master thesis. The purpose of my study is to identify reasons why farmers invest in or refrain from investing in new agriculture technologies on farms in Portugal. I want to test different hypotheses to understand what drives the adoption of technologies among farmers in Portugal. This means that my dependent variable is binary (adoption yes/no).
Now, I have different types of independent variables:
– Binary variables (e.g. did you participated in any training program related to new technologies? yes/no)
– Ordinal variables (e.g. I find new technologies easy to use. likertscale)
I will probably need two statistical models. For independent binary variables, I could use the logistic regression. For independent ordinal variables, I am not sure which regression method to use? Or should I use a correlation method like chi square test for independence or Spearman’s?
I would be grateful for your input!
Looking forward to hear from you soon.
Best regards,
Martin
Jim Frost says
Hi Martin,
As I mention in this post, you often choose the type of regression model based on the type of dependent variable. The types of independent variables don’t typically influence the choice in regression model.
Because you have a binary dependent variable, you’ll need to use binary logistic regression regardless of the types of independent variables. You’ll be able to predict the probability that a farmer will adopt technologies based on the values of the independent variables.
However, ordinal IVs present a challenge in regression analysis. You’ll need to enter each ordinal variable into your model as either as a categorical (nominal) variable or a continuous (numeric) variable. The decision depends on the nature and amount of your data combined with your research goals. I write about that in my book about regression analysis. In fact, because your thesis depends on regression analysis, I’d recommend it for that reason too.
By the way, I don’t think a chisquared test or correlation is the way to go. Those analyses won’t give you the answers you need.
I hope that helps!
RABIA NOUSHEEN says
Hi Jim
Thanks for very useful post. I have a question that can we use square root rather than log link in poisson regression? Poisson regression with my data results in p values = 1 for all coeffecients when I use log link.
I want to ask another thing , I am confused how to treat my variables in regression analysis, either numeric or factor? my variables are Concentration(500,1000,2000,10000 particles), Exposuretime(1,2,3,24 hours), Size of particles (3 microns,10 microns) and particle type (aged, unaged, with additives, without additives). I feel that all should be entered as factors but not sure. When I treat variables having number as numeric, software (R) by default considers them continuous variable (got to know it when did post hoc test and observed that software is choosing any value between set levels (like 7 hours between 3 and 24 hours) for comparison rather than the ones tested).
Please guide me as I have wasted much time in getting answer of these questions. I did not get any of my question unanswered on this forum and that gives me a great hope. I shall be thankful for your time and valuable suggestions.
Packartt Tuga says
Hey there Jim,
I need your help with the regression analysis portion of my paper, which deals with effectiveness of debt relief programs in the subsaharan african region. My dependent variables will be macroeconomic factors likeGNI, Inflation etc and the independent variable is the amount of relief received.
With the help of this blog, I think Lasso regression will suit my research best.
Can you suggest other regression methods or statistical tools that can be used?
Most times I’m not able to comprehend the statistical language/ formulae that most guides used, do you know of a stepbystep guide that provides a basic framework for regression/quant analysis.
Thanks a lot.
Degarege Asefa says
Hi JIm, greetings
my research title is: “Determinant factors for effective donor utilization by NGOs working in Ethiopia: the case of ORDA”
my Dependent variable is ” effective donor fund utilization”
and independent variables are: managerial factors, technical factors, operational environment, financial management and donor behavior. so, could you tell me the metric and non metric of both variables (dep. & Indpen.) ?
what type of regression to be used? multiple regression or continuous linear regression?
thank you for your fast and cheerful professional advice!
Jim Frost says
Hi Degarege,
It takes subject area expertise and research to determine the best metrics for your study. Unfortunately, I’m not an expert in donor fund utilization. So, I don’t know which metrics are best. You’ll probably need to do a bit of research to identify them. For more information about this process, please read my post about 5 Steps for Conducting Studies with Statistics. The earlier stages of the process explain how you need to operationalize your variables.
The type of regression largely depends on the type of metric you use for the dependent variables, which is unclear at this point. If it’s a continuous variable, then multiple regression is probably a good place to start. I believe what you describe as “continuous linear regression” is synonymous with multiple regression.
Thanks for writing!
Maria Martin says
Really clear and useful post, Jim. Thank you!
May I ask, if the dependent variable is slightly right skewed, is it still appropriate to use a linear regression? would I necessarily have to transform the data by taking natural log of the variable? If I do this, I lose around n=100 (total n=1004) participants.
I ask because I received a comment to create a histogram or illustration of the dependent variable distribution to demonstrate that it is acceptable to model it with linear regression. Are there other factors that can make the dependent variable more or less appropriate?
I think I should plot the independent and dependent variable with a scatterplot – but this is not so informative because my independent variable is a categorical item with five levels (gender identity).
Many thanks in advance for your help,
Jim Frost says
Hi Maria,
The real question to ask is: are the residuals are normally distributed? Regression assumptions center on the properties of the residuals. So, the question about the distribution of the dependent variable is largely irrelevant. I say “largely” because if you have a highly skewed DV, then it can be hard to obtain normally distributed residuals. For more information on this aspect, please read my post about OLS assumptions.
In that post, I indicate you should use a normal probability plot of the residuals to determine whether they follow a normal distribution. For more information about that type of plot, please read my post about histograms vs. normal probability plots for assess normality. In that post, I’m not writing about residuals specifically, but the information applies. One thing to note, with 1000 observations you have a fairly large dataset. You could theoretically use a normality test to assess normality. However, with so many data points, the test will be very powerful and could potentially detect inconsequential deviations from the normal distribution. More reason to use a normal probability plot!
Before trying anything else, I’d check the residuals. Determine whether they’re normally distributed. You might not even have a problem. If they’re not normally distributed, or you see other patterns, there are other steps you can take that depend on the nature of the problem.
Because your research involves regression analysis, you might be interested in my regression analysis book. I write about all of these issues in that book along with various ways to resolve problems.
If your IV with five levels is an ordinal scale, such as Likert scale, you might have some difficulties with that because ordinal data have a mix of properties of continuous and categorical variables. You’ll have to choose one or the other ways to enter that variable into the model. I don’t have a post for that but I do write about in my book.
I hope this helps!
Chandra Shekhar Tiwari says
Just to clarify further the above question:
Apologies for a long message again. I have been advised to do like this by my project supervisor:
I am going to do Multiple regression after taking mean (or average of my ordinal variables).
1. I have total 10 independent variables out of 3 constructs (for independent variable)
and 3 dependent variables (CA1, CA2, CA3) out of 1 construct (for dependent variable for competitive advantage) and all are ordinal variables. So basically, I have total 13 variables, therefore will I have 13 columns of average if I need to calculate?
2. Then I need to take average of all 10 independent variables (because they are on ordinal scale) and take average of 1 dependent variable (e.g. CA1) at a time, and then run the multiple regression analysis. Because dependent variable can be only one at a time.
Then repeat the step for CA2 and CA3.
I will have 3 equations Y1 = m1X1+m2X2+m3X3+…+m10X10 +C, this is for CA1
Y2= m1X1+m2X2+m3X3+…+m10X10 +C, this is for CA2
Y3 = m1X1+m2X2+m3X3+…+m10X10 +C, this is for CA3
to interpret from output of SPSS. Output is in different tables.
And all these three equations should satisfy separately.
Am I doing this alright? Or shall I just keep only CA and delete other two?
Or shall I just run ordinal regression 3 times for 3 dependent variables as SPSS allows ordinal regression so no need to take average (Interpretation will be a bit difficult)?
I am going to try on paper the linear regression after taking average 3 times and then see if there is a relationship as mentioned in 3 equations.
Looking forward to hearing from you soon.
Regards,
Chandra
Jim Frost says
Hi Chandra,
Ah, in my reply to your other comment, I didn’t realize you were boiling down all your IVs to one average. When you can use a good number of Likert values to produce an average, it is good.
Are your Y variables going to be averages or the actual Likert values (ordinal)? If you use the ordinal values, you’ll need to use ordinal logistic regression. If the Y variables are an average of multiple Likert items, you might be able to treat it as continuous and use linear least squares.
So, I’m not totally sure what your final model will be? Will have the 10 separate IVs (Likert items) or are you averaging them down to one? And, will the DV be ordinal or continuous?
But, in general, yes, I’d fit a model for each DV as you describe. As I mentioned in my other reply, check those residual plots to be sure that you’re not missing curvature or otherwise misspecifying your model! That’s always important but it’ll be extra important in your case because you’re working with averaging Likert items, which can work but not always linearly.
Chandra Shekhar Tiwari says
Hello Jim,
Thanks for your great posts!!
Need a little bit of help if you could oblige for my MBA project. I went through your various posts already.
Here is the scenario of my project:
I have total 10 Independent variables all in Likert scale from 17 (ordinal), these variables are coming out of my 3 constructs.
And I have 3 dependent variables (part of one main dependent variable) again on Likert scale of 17(ordinal).
My questions:
1.Which regression method do I use?
2. Can I take all independent variables and only one dependent variable at a time? And then analyze?
3. Can I take average of all my independent variables(as they are on Likert) and average of my dependent variables (again on Likert)? and run the analysis? But I am not sure how do I do it.
4. How do I interpret the result?
5. My preference is to use Linear regression.
Just to briefly describe about my project, I am analyzing Impact of innovation on competitive advantage. so 10 IVs from Impact of innovations and 3 DVs(Competitive advantage).
I would be grateful for your inputs. This is a bit of struggle at the moment.
Looking forward to hear from you soon.
Regards,
Chandra
Jim Frost says
Hi Chandra,
You’ll need to use ordinal logistic regression because of the ordinal dependent variable. Yes, it’s fine to use separate models as you indicate.
Many analysts will average (or sum) your Likert items together and treat them as continuous variables. That can potentially avoid the problems I discuss below. Just be sure that averaging or summing is appropriate for your variables. You’ll need to be extra sure to check your residual plots to be sure your model has an adequate fit.
Ordinal independent variables can be problematic. I write more about ordinal IVs in my book about regression analysis. They have properties of both categorical and continuous variables. You’ll need to enter them one way or the other. The correct decision depends on a combination of the characteristics of your data and dataset, and the goals of your study. But, if you can sum or average scores, you might be able to avoid these difficulties.
You can’t use linear regression when you have an ordinal DV. Although, if you can average/sum the DV to produce a continuous variable, you might be able to use least squares linear regression. Again, be extra sure to check those residual plots!
Because your study depends on regression analysis, you might consider getting my regression book. You can get it as an ebook from my website (best price) or in print from Amazon and other sellers.
Patabedige P.M. says
Hellow sir,
I have a problem with my research work..My dependent variable is ordinal type (5 point likert scale) and my all the independent variables are also only ordinal type (likert scale).what is the most suitable regression type for me? According to my knowledge i think that my all the independent variables are ordinal variables are ordinal regression can not be used.because it do not have any continues or nominal variable..according to the assumptions ..
Jim Frost says
Hi, because of your ordinal dependent variable, you’ll need to use ordinal logistic regression. However, as you note, the ordinal independent variables are potentially problematic. I write more about ordinal IVs in my book about regression analysis. They have properties of both categorical and continuous variables. You’ll need to enter them one way or the other. The correct decision depends on a combination of the characteristics of your data and dataset, and the goals of your study.
If it’s appropriate, you can average (or sum) your Likert items together and treat them as continuous variables.
Arya Devi K.S. says
Hi Sir,
I have more than one categorical dependent variable and one continues independent variable. Which regression I can use for it?
Jenny says
Hi Jim,
thank you very much for the article. I was wondering if you could help me find the fitting method for my thesis. I am doing a survey. My independent variable as well as my dependent variable are both binary (“yes” and “no”, which I coded in 1 and 0). Furthermore I have a lot of control variables which are either binary as well like for example sex or continuous (e.g. age) or categorical (14 or 15). Can I use a normal binomial logistic regression, even though not only my dependent but also my independent variable is binary/ a dummy variable? Or what else should I use? And how could I then interpret the results, just like with normal logistic regression?
I also want to test the mediation effect of two variables which are categorical (measured on a 4 or 5 point Likert Scale). How can I do that?
I would really appreciate if you could help me! Thank you so much in advance!
Jim Frost says
Hi Jenny,
Yes, that’s absolutely fine to use binary logistic regression in that context of also having binary variables as IVs along with the DV. What might actually cause some difficulty are the ordinal (Likert) IVs. You’ll have to model them as either continuous or categorical. The best method depends on which method gives you a good fit, the number of observations versus categorical levels (if you use that approach), and your research goals. I write about that in my book about regression analysis, although not in the binary logistic context, but the issues are the same.
I hope that helps!
Michael Dean Green says
Hi Jim, Thanks for helping us statistically challenged folk! I manually corrected my dependent variable by adjusting for time and plugged the data into some software that does Backward Stepwise OLS and got a final answer, but it failed Normality (P<0.001) and Constant Variance (P<0.001) test. When I took the square root of my dependent variable and adjusted for time, the result were entirely different, but they passed Normality (P=0.980) and Constant Variance (P=0.144) tests. I read one can normalize data simply by doing a log transformation or taking the square root. Is this practice legit? I feel like I had manipulated the data.
Jim Frost says
Hi Michael,
It is true that that you can transform your data as you describe to solve both those problems. I mention this in my post about heteroscedasticity (nonconstant variance). It can also fix the normality problems associated with a very skewed dependent variable. Just be sure that the nonnormality is not caused by a misspecified model, such as incorrectly modeling curvature.
While it is an acceptable solution to these problems, it should be your solution of last resort. Try solving the problems other ways first. I mention some in my post about heteroscedasticity. And, you can read about other reasons for nonnormal residuals in my post about OLS assumptions. However, if you can’t fix those problems using other methods, then you can use the data transformation. Just be aware that you’re then describing the relationship between your IVs and the transformed DV, which makes the interpretation less intuitive than using the raw data. The goodness of fit statistics, such as Rsquared, apply to the transformed DV as well. I write more about that in my book about regression analysis.
So, yes, it can be a good solution but try resolving the problems other ways first!
Shannon says
Hi Jim,
Thanks for the great overview! I’m wondering if you could explain a little about how orthogonal linear regression fits into the mix?
Thanks!
Jim Frost says
Hi Shannon,
Orthogonal regression is useful when you have two different measurement systems and you want to determine the relationship between the two. For example, you might have an old and new way to measure weight, blood pressure, etc. and you want to understand the relationship between the two systems. Most regression analysis assumes that measurement error exists only in the dependent variable. In orthogonal regression, the analysis can handle error in both the IV and DV. However, you do need to know the ratio of measurement error for the two systems to be able to use orthogonal regression.
I hope that helps!
Caroline Öhman says
Hi Jim! I’m struggling with the statistical method for my thesis in bioscience. I have only taken a basic course in statistics (we use R) and need to use another analysis than what I’ve done so far, for this project.
I have a number of sites where bat activity has been recorded. The data is in the form of “activity” or “no activity”, Now I want to compare the activity with qualities of the nature at the site. This will be measured in type of nature (alternatives like decidous forest, grassland, planted forest, edge zone), close to water (yes or no), slope (facing south, west, east,north or no slope) and maybe more factors. Someone suggested regression trees, but the information I have found is at a too difficult level for my knoledge, and I’m not that good at mathematical English… And I’m not sure how many sites I would need. to get relevant results from my partly nominal data. I have about 1535 sites with “activity” and the total number of sites is 35, if I count “no activity”.
Do you have any idea of a good analysis or how to use my data? Please ask if I my explanation is unclear or you need more information. Thank you for doing this website!
Jim Frost says
Hi Caroline,
It sounds like you can use binary logistic regression, which I write about in this post. Your response variable is binary, activity or no activity. And, you can include all the variables you mention. Using the model, you can learn how each factor relates to the binary outcome. You can even predict the probability of an event (one of the outcomes) occurring for a set of predictor values.
Michael Dean Green says
Hi Jim,
Thanks very much for your explanations of the types of regressions. I was wondering if you could advise me on the type of regression that is suitable to the data I have collected.
I would like to know which of the supplements I have been taking has any influence on my Parkinson’s Disease symptoms.
The severity of my symptoms vary from day to day. I can measure the severity by doing a simple tennis ball rolling exercise to get “rotations per minute” or RPM. I record the time of day (T) I do the exercise and record the combination of supplements I took the night before.
So my dependent variable is RPM and my independent variables are T and the supplements I took the night before (1=yes, 0=no).
What statistical method would you recommend?
Thanks,
Mike
Jim Frost says
Hi Michael,
If your outcome data are continuous (it sounds like they are), then linear least squares (OLS) regression would be a great place to start. Because your model involves time (daily measurements), you’ll need to be extra careful looking for autocorrelation in the residuals. Read my post about OLS assumptions for more information. Look for the autocorrelation assumption.
John Grenci says
Hi Jim, thank you for this site. well written, and i am sure it takes up much of your time. Your passion shines thru. I am hung up a bit on one of the assumptions for binary logistic regression. in particular, where Independent variables are linearly related to the log odds of the dependent variable. how would we go about checking that? thanks John
Laura Kukkonen says
Hi Jim!
Thank you a lot for your posts, they are very useful and wellwritten.
I’m trying to find a way to analyse multiple hypotheses based on questionnaire data. Each hypothesis requires the analysis of multiple dependent and independent variables. The independent variables are of continuous, categorical and binary types, while the dependent variables are in this case all categorical (Likert scale). If possible, I would like to find a single model to analyse each hypothesis, but I’m having a hard time figuring out if there is any statistics available to fit my needs. I have been considering a multinomial logistic regression, but as I understand it only allows for one dependent variable. Is there an alternative where multiple dependent variables are available? If all of my different types of independent variables cannot be included in a single test, which types of separate tests would you recommend? I am thinking of a MANOVA for the categorical independent variables, but I don’t know of any suitable tests for the continuous and binary variables where I can include more than one dependent variable. Thank you a lot in advance!
Kinds regards,
Laura
Jim Frost says
Hi Laura,
Because of the ordinal DVs, I think you’ll need to use ordinal logistic regression and analyze each DV in a separate model. Those models can each contain all of your continuous and binary IVs. The regular MANOVA isn’t suitable for ordinal DVs. I’m not aware of a similar analysis that can handle multiple ordinal DVs.
Girma Gedamu says
hi Hi Jim, I’m interested to find out the prediction of drought of categorical independent variables on a continuous dependent variable.
for example my is data contain five variables one dependent and four other independent but the dependent variable again has 5 categories so which regression model i use please help me
Jim Frost says
Hi Girma,
If the DV variable’s categories have no natural ordering, then you’d use nominal logistic regression. However, if they do have a natural ordering (e.g., a five point Likert scale), you’d need to use ordinal logistic regression.
Tim says
Hello Jim,
Thanks for your post.
I have been tasked to perform an OLS regression on wage of a certain population. We are asked to have a close look at the definition of the variable education (1:low and 5:high). What kind of conceptual problem might there be in using it in an OLS?
Thanks in advance!
Jim Frost says
Hi Tim,
One potential issue is that you have an ordinal independent variable. You’ll have to determine whether to include it as a categorical variable or a continuous variable. That depends on the goals of your analysis, the nature and amount of data, and the adequacy of the fit using either method. If you have my book about regression, I write more about the decision and challenges of both approaches.
Also, regarding the definition of the variable, that sounds fairly subjective/vague. You should have a clear definition of how you’ll measure education level. Ideally, come up with an operational definition. Then test it on sample data to be sure that it provides consistent, accurate results in representing education level. I talk about creating operational definitions of your variables in my post about including statistical analyses in scientific studies.
İrem says
Hi Jim,
Hello I’m trying to build a regression model. I have 122 independent variable and I am trying to predict
legal proceeding amount .I also have many zeros in my dependent variable. These zeros show that he is not under legal prosecution.Most of these independent variables include the frequency of loan usage by years. I also have a variable in how much credit they use in total. But there are too many zeros in these variables. Because it was entered as zero for not using credit in any year. Therefore, I have different variables such as gender and age. Which regression model would be appropriate for me in this context ?
Sanjay Mali says
I have a table showing readings of a variable that depends on 4 different ‘YesNo’ type of variables which are independent of each other. Besides, in the descriptive part, I am told that these “+ – ” type variables are themselves can vary in the sense that suppose there is a circular button. This button is on or off is given in the table. Besides if on, its range of rotation is also given that influences on part of question. What type of regression model should I use?
Abeer says
Hi ,,
thank you for the valuable information ..
I have a question about ordinal logistic regression.
I have a likertscale questionnaire with several subscales. I have calculated the composite score for each subscale. First , I did compressions between males and females in my sample using mean scores of each . further I focused on one of the subscales and wish to do ordinal logistic regression for each item under that subscale separately to test its strength of association with other IV (gender, GPA, etc) .
would that be a correct approach ?OR is it contraindicated to treat items separately using ordinal logistic regression if they have already been combined as a composite score in the same study?
Hugo says
HI, I was wondering if my DV is numerical (ratio), what is de best regression analysis? The IVs are both ratio (numerical) and continuous.
Jim Frost says
Hi Hugo, the best place to start would be linear least squares!
Ian Berryman says
Hi, great informative webpage. I was curious. If dependent variable is natural logged and key ind. var is not (semilog form) can your controls be natural logged? my dep var is percent change in poverty, my key ind. var. is # of new churches(so must be measured by units) and my controls are all measured in percents (i.e. percent of the population thats black, percent of the population that earned a HS diploma, etc.) so do I need to have these controls natural logged to interpret my regression as “a 1% increase in a control variable leads to a x% change in poverty” or, since they are already measure in my data set as percentages, is that already how the regression interprets?
Thanks!
Jim Frost says
Hi Ian,
Yes, you can “mix and match” as needed with using natural logs or not! It really depends on the the nature of the data and theories about what is appropriate for it.
Sajeeka Gunasekara says
Thank you for replying me.! If I want to investigate the impact of happiness on the GPA, what would be suitable? Happiness would be collected as a average score it would be in between 0 to 6. What regression would be suitable?
Jim Frost says
Hi Sajeeka,
Is happiness an average of a set of scores that are something like Likert scale items? If so, taking the average is a method to use it as a continuous independent variable in your model. The coefficient for this variable indicates the average increase in GPA for a oneunit increase in the happiness score. That’s still least squares regression.
Sajeeka Gunasekara says
Hi Jim!
Thank you for your posts they are really useful. I have a problem with my scenario. My DV is grade point averages (GPA). It is a continuous value between 0 and 4. My IVs are happiness score, gender and academic level (it has 4 levels as 1000,2000,3000 and 4000 level). what type of regression is suitable here? What type of regression would you recommend for this??
Thank you so much!
Jim Frost says
Hi Sajeeka,
I’d start with linear least squares regression (OLS). You have a continuous DV. Gender and academic level are categorical independent variables, which OLS can handle. For academic level, the regression model will determine whether the mean GPA for each academical level is significantly different from one the baseline academic level (which is something you have to pick).
I hope that helps!
Helen says
Hi Jim,
My dependent variable is a score 1 – 5, with 5 being the best rating, and 1 being the worst. I want to see if gender has an association with a higher rating. I have been looking into doing an ordinal logistic regression analysis, but not sure if this is correct. I know I could create a binary outcome of (13) vs (4,5) but not sure if I want to lose that data? What type of regression would you recommend for this analysis?
Thanks!
Jim Frost says
Hi Helen,
Your dependent variable is an ordinal variable. Consequently, you’ll need to use ordinal logistic regression. You won’t need collapse any values using that method. That method will tell you if gender relates to those ordinal values.
Bidisha Chakraborty says
Hi Jim! Your posts are really helpful. My DV is an happiness index lying between 0 and 1(continuous) that has been constructed from various responses in categorical form. Hence DVis ordered. Should ordered logit or ordered probit would be ideal regression model to fit? Earlier, I have fitted OLS to the happiness scores lying between 10 to 45. R square is coming out to be very low. In ordered logit, chi square is highly significant, but, the pseudo R square is very low. Waiting for your valuable comments.
Thanks and regards,
Bidisha
Jim Frost says
Hi Bidisha,
It sounds like you have a continuous DV that is restricted in range between 0 and 1. Because it is continuous, you cannot use binary or ordinal logistic regression. However, you can use a logistic sigmoid function to model it. This process forces the model to respect the limits of the DV. I’ve never performed this type of analysis, so I can’t offer much help. But that should point you in the right direction!
Louis G. Daily says
Jim, I have one continuous predictor (IV) variable in my multiple regression and one ordinal predictor (IV). Can I do this? Can I incorporate this ordinal variable which comes from a Likert scale into the multiple regression?
Jim Frost says
Hi Louis,
I write about this in more detail in my regression book. Go to my webstore for more details about it.
Ordinal variables have a mix of attributes of categorical and continuous variables, which makes including them in a model a bit more complex. You’ll need to enter your ordinal variable as either a continuous variable or a categorical variable. The decision depends on the nature of your data, the goals of your research, and the quality of fit each approach provides.
Abel Chipeta says
Hey Jim
Thanks for the clarifications on the models…they really help.
Am conduction a study on Gender decomposition on the effect of education on household savings….but under this Savings is my dependent Variable while education, gender, sex, age, income, marital status and occupation are the independent variables…Help me on the best model to use since saving is categorical variable while in the explanatory variables their is mix of both categorical and continuous variables…
Looking forward from your Help
best regard
Jim Frost says
Hi Abel,
I would start with linear least squares regression (aka ordinary least squares). Your dependent variable is continuous (savings), which this type of regression can handle. Linear least squares can also handle a mix of continuous and categorical independent variables, such as you have.
Meriem says
Hi Jim,
Could you recommend any references?
Thank you in advance
joel says
Hi Jim,
I’m interested to find out the impacts of categorical independent variables on a continuous dependent variable.
For example, what are the effects of the type of news (financials, dividend payout) on the volatility of stocks.
Jim Frost says
Hi Joel, that’s possible in linear least squares regression along with other types of regression. Assessing the role of categorical IVs is a fairly common and basic usage for many types of regression models. If you have only categorical variables in your model, that’s often called ANOVA (analysis of variances). However, ANOVA uses the same mathematics “under the hood” as regression.
Gemechu says
I have two dependent variables(aboveground biomass and carbon content) and three indepedent variables(wood density, diameter and height). Which model is best for anaysing my data? Waiting you under reply. Thank you
Meriem says
Thank you for your answer. What about the fixed and random effects is ok to use the mix of binary and continuous varibales?
Jim Frost says
Yes, just be sure to use a type of model where you can specify both fix and random effects, such as the MIXED procedure in SPSS. That aspect does affect the type of procedures you can use.
Meriem says
Hi Jim,
Your content has helped me a lot in my work, Thank you!
I’m conducting a regression analysis using panel data on a sample of 74 individuals on an 8 year period of time. I’ve been wondering if I can use a mix of binary and continuous independent variables to explain a continuous dependent variable. Would that be ok? how would it affect the type of regression I use?
Thanks
Meriem
Jim Frost says
Hi Meriem,
Yes, it’s entirely ok to use a mix of binary and continuous independent variables for ordinary least squares. You still need to satisfy the same set of OLS assumptions, but there are no additional requirements. Binary independent variables are also known as indicator variables and analyst frequently use them in linear regression. Typically, the 1s and 0s of an indicator variable represent the presence or absence of a characteristic. You just need to interpret their coefficients differently. The coefficient represents the mean difference between observations with and without the characteristic. The pvalue indicates whether that difference is statistically significant.
I hope this helps!
Erick Loetz says
Hello Jim.
When using logistic regression how critical is to have balanced treatment groups. I have a binary response variable evaluated from two treatments at n=40 and n=14? How does the need for balance (approximately equal n’s for each group) compares to OLS analysis? Is there any published information on the subject? I appreciate your comments and expertise. Many thanks.
Jim Frost says
Hi Erick,
I don’t have a reference specific to binary logistic regression. However, in general, it’s OK to have unbalanced groups like that. Having balanced groups helps you maximize power for any given number of subjects. But, it’s ok to have an unequal number. I’m not sure for logistic regression, but for most analysis I’d say that having 14 in the smallest group is fine.
Perry Gonen says
When doing multiple regression analysis, as apposed to a simple OLS, where we have a number of independent variables, do you recommend to plot each independent variable against the dependent variable, one at a time to see how the plot of each variable on its own (without the other variables) against the dependent variable looks like. After analyzing each plot on its own go forward with the statistical analysis
Sarah says
Hi Jim,
I am hoping to do a regression analysis on social posts. The question I am usually trying to answer is does a certain variable (for example a photo vs no photo) play a role in engagement rate.
I’ve been going about this as coding the post: “Does this post have a photo or not?” Yes = 1, No=0 , but then I’m unsure what type of analysis would make the most sense?
(I would replicate this analysis on a bunch of different categories as well, like is the post light vs. dark, morning or night etc )
Any help/ thoughts would be much appreciated!!
Sarah
Nick Dekkers says
Dear Jim,
I would like to describe the correlation between 1 categorial factor (3 levels) and 2 continuous responses which are nonlinear over time. I have a continuous factor (concentration) which I would like to add to the model. But I’m not sure if I should use the MANCOVA or a nonlinear regression model. What would you suggest?
Thanks in advance!
Daphne says
Dear Jim,
I’ve been reading some of your blogposts and they are very helpful! However, I do have some remaining doubts about the regression I should run for my thesis. The doubt is mainly related to the setup of my dependent variable. I am constructing a measure of corporate social responsibility performance by summing strengths and concerns of several dimensions for each firm per year. There are about 50 dimensions in total and a firm is either given a 1 or a 0 in case the firm is either known to perform well/poorly(1) or 0 otherwise. Hence the dependent variable is constructed by summing these binary data observations. The minimum value of both the total strengths and the total concerns variable is 0. The maximum value of the strengths is equal to 44 and for the concerns this is equal to 36. I have thus only nonnegative integer values. The mean for both strengths and concerns is around 3.0, however the standard deviation of the strengths is equal to 3.8 and for the concerns it equals 4.9. I’m hoping to be able to use OLS regression, however I was wondering whether there is enough variance in the DV and whether it can be considered a continuous variable. After reading this blogpost I also started wondering about the count data dependent variable and whether something like a poisson regression isn’t more suited. I was also considering the ordinal logistic regression. For a bit more clearance: I am trying to find the effect of several ownertypes on CSR performance. I hope you can help me out! In case you have some questions about my dependent variable, please let me know, then I will elaborate further!
Many many thanks in advance!
Jayson says
Hello Sir Jim,
What if there are 12 variables 2 Variants ( Variable 1 to 6 measures well being and Variable 7 to 12 positive thinking?
Should I run simple linear regression or multiple? How can I know if the variables are affecting each other.thank you
Jessica says
Hello Mr Jim Frost,
Thank you so much for the information you have provided on your website, as well as the answers to the questions above they are very helpful!
I am currently designing a research proposal that will assess medication adherence for rural vs urban groups in Canada. I plan on using an adherence scale that will give the participants a score (from 18) and based on the score, their adherence will be categorised. So >7 =high adherence. 58 =moderate adherence. <5 is low adherence.
I am planning the statistical analysis for the adherence.
Based on the responses I have read, would it be correct to conduct an ordinal logistic regression for the two groups? We’re also planning on conducting an independent samples ttest between the two groups and a paired samples ttest to compare the same group at the start and end of the study. Would these ttests be possible if we transformed the data to continuous variables?
We also wanted to conduct a Chisquare test; is this possible if we don’t have estimated adherence scales.
How would you go about the statistical analysis for this study?
Thanks!
Jessica
Jim Frost says
Hi Jessica,
Yes, your plan sounds like a sound one. Ordinal logistic regression is a good choice. You can use ttests for Likert scale data. Your data aren’t exactly Likert scale. It’s an 8 point ordinal scale. My guess (though I don’t a a reference to cite) is that you could not use a ttest based on the recategorized scale but probably on the full eight point scale. If you really want to use the recategorized scale, you might need to use a MannWhitney test instead. Transformation is probably not necessary. As an ordinal scale has more points, it become more like continuous data. Not perfectly so, but studies have shown that at least 5 point scales are close enough to use ttests. Read my post about using ttests for Likert data for more information.
About the chisquare test, I’m not sure what you mean by not having estimated adherence scales?
I think any of those approaches are valid. Ordinal logistic regression has the benefit of being designed for ordinal data. While studies have shown that ttests and Mann Whitney tests can both work, it avoids a potential debate about the results if you just use a test designed for that data type! Consequently, I’d probably lean in that direction myself.
Geska says
Hii Jim,
If one respondent answering same set of question for different brand, when i run the regression analysis, should I compared both answer and use dummy variable, or I should run the regression separetly? because one of my aim is looking how customer trust affect customer loyalty
Jim Frost says
Hi Geska,
Let me make sure I’m understanding your scenario correctly. You’ll have the respondents answer the same questions about two or more brands and you’ll some dependent/outcome variable that you’re measuring? And, you want to determine how the responses related to the brands?
In that case, you’ll need to use a mixed ANOVA model. You’ll need to include a subject ID as a random factor, survey responses as fixed factors or covariates depending on the type of response, and the brand itself as a fixed factor.
I hope that helps! Unfortunately, I haven’t written a post about mixed models yet. But, that’s the direction it sounds like you need to go!
Dave says
Hi Jim
I am wondering whether regression analysis can be used to predict the strength of relationships between multiple income variables and bottom line expenditure. I have a conundrum in how I compare Business performance pre Covid ( Feb) to future performance in say ( October) and how i might model and estimate future income and expenditure. Can you enlighten?
Fei says
Hi Jim, thank you for sharing this information. It is very helpful! I have a question regarding gender. There are only a few males in my sample, the female – male ratio is 14:1. Will this affect my analysis if I run a multiple linear regression? How about a simple mediation or moderation analysis (IV, DV, mediator/moderator). I plan to use gender as a control variable for these three types of analyses.
Thank you! I look forward to your reply.
Jim Frost says
Hi Fei, because there are so few males, it’ll be hard for the model estimate the effect of gender and the statistical test will have low statistical power. However, if the effect of gender is large, it might still be a valuable addition and you could possibly get significant results. Honestly, I would just try it and see how it works. Don’t expect too much from gender, but if the effect is large it might still be important to include it.
Michaela says
Hi Jim,
I hope you can help me, I am riding the clock a bit.
I hope you are well and safe with all the madness of COVID19 in the world. I was hoping you could please help me. I am a student currently in need of a lot of help. Statistical analysis is not my forte but when needs must, we grin and bare it and look to the professionals for help.
I have just received my data back from my survey and I have found that my independent variables are all 15 Likert scales. I am looking at consumer attitude and intention, however, I believe if I am not mistaken that my dependent variable is my ‘Intention’ as that is what I am looking to find out overall. From my research, I think ordinal logistic regression is most appropriate to use. I ended up looking at youtube on how to run the test and what goes where but a lot of it is contradictory to the next.
My independent variables are;
Relative advantage/ Compatibility/ Environmental Impact / Psychological ownership.
Each of these are subcategories with between 4 to 8 questions under each – All 5 point Likert
I need to determine these expectancy values against attitude and intention but I have no idea how to.
So my questions are:
Firstly, am I using the right regression
Do the IV go into the ‘factors’ or the ‘Covariate’ box
Is it Test of Parallel lines where I am receiving my null hypothesis data?
Is there another test I should be running with it?
Also when analysing the output, if I run it through ‘factor’ box, in the parameter estimates, under the location information, is the significance data suggesting that my data is proving that – say for instance – Relative advantage significance is linked to consumer intention or does it mean that there is no link between RA and intention?
Thank you very much for your time. I hope you can help.
Jim Frost says
Hi Michaela,
Choosing the correct type of regression depends on the dependent variable, and I’m not sure what your DV is. If your DV is also Likert scale, then, yes, ordinal logistic regression is the correct type.
For the IVs, Likert scale items can be tricky if you’re using the individual item scores for your values. They’re not continuous but they’re not categorical. However, you’ll need to model them one of those ways. That gets to your question about whether they are “factors” (categorical) or covariates (continuous). There are pros and cons for both ways. And, the goals of your study also play a role. I don’t have an article to refer you to about that, but I do write about it in my regression ebook.
If you’re summing or averaging Likert items together and using those values in your model, you might be able to treat them as continuous variables (covariates).
The test of parallel lines is for ordinal (or ordered) logistic regression tests whether the coefficients are the same for all levels of your DV, which I’m assuming are the Likert scale values. This test really is about determining whether you have a good model or not. If this test is significant, it’s not good. It means that there is something wrong with your model, but it doesn’t tell you what exactly. You could just have a poor fitting model or you might be using the incorrect link function. You want this pvalue to be greater than your significance level.
You should also look at the chisquared goodnessoffit tables. Again, you want pvalues that are higher than your significance levels. Low pvalues are problematic here because they indicate your model doesn’t fit the data well.
You should also look at the pseudo Rsquared values. Like the regular Rsquared in linear regression, they give you an idea of how well the model fits the data. Low values don’t necessarily mean the model is incorrect but they do mean that it won’t be good for prediction.
For the location parameter estimates, low pvalues that are statistically significant indicate that the IV is statistically significant. Suggests that the effect of IV does not equal zero (i.e., there is an effect). It’s a good thing!
I hope that helps! Best of luck with your study!
Robbie says
Hi Jim
I have read your books and a lot of your blog and have found all incredibly useful. One area I am repeatedly confused by (perhaps as it is not in your books) is the interpretation of logistic regressions.
I am looking at various negative outcomes for children (child labour, begging, etc.) and comparing them with a range of households characteristics (child headed, large households, elderly headed households etc)
The results show some interesting findings. For instance, regression the binary dependent variable of ‘begging’ or not give me the odds ratio of 2.5 for child headed households. For the same regression, te margins commands shows probabilities of 0.57 for child headed households and 0.35 for adult headed households.
I am at a loss as the best way to interpret these findings in a manner which makes sense for the data…
Is it more intuitive to say child headed households are 2.5 times more likely to beg than adult headed households (or is this not correct interpretation), or is the better interpretation that child headed households are 1.6 times more likely to beg than adult headed households (0.57 v 0.35).
Can you assist?
Robbie says
Hi Jim
Thanks so much for your useful blog and posts. Its really helped me understand regressions and do some really interesting analysis. And inevitably led me to by your books on linear regressions and statistics… I only wish there was one on logistic regressions too 🙂
I have run a simple logistic regression using a child labour survey in an East African country.
I ran the number of households that reported begging against those households that were child headed.
The logistic regression showed that child headed households had an odds ratio of 2.51. The constant was 0.54.I interpret this as meaning that child headed households are 2.5 times more likely to beg that nonchild headed households (although I am not sure if i need to subtract the constant?)
However the margins command shows the predicted probability of begging in a childheaded household is 0.57 while in a nonchild headed household it is 0.35. The odds ratio and margins do not speak to each other as I understand. How can the odds of begging in a child headed household be 2.5 times greater using the odds ratio but the probability of begging in a childheaded household be far less at 1.6 times greater?
Apologies for the no doubt obvious question, but I am struggling to find any answers.
. logistic begging i.child_head
Logistic regression Number of obs = 1,288
LR chi2(1) = 3.95
Prob > chi2 = 0.0470
Log likelihood = 836.89941 Pseudo R2 = 0.0024
——————————————————————————
begging  Odds Ratio Std. Err. z P>z [95% Conf. Interval]
————+—————————————————————
1.child_head  2.51981 1.180173 1.97 0.048 1.006238 6.310081
_cons  .545676 .032052 10.31 0.000 .4863365 .6122557
——————————————————————————
Note: _cons estimates baseline odds.
.
end of dofile
. margin child_head
Adjusted predictions Number of obs = 1,288
Model VCE : OIM
Expression : Pr(begging), predict()
——————————————————————————
 Deltamethod
 Margin Std. Err. z P>z [95% Conf. Interval]
————+—————————————————————
child_head 
0  .3530339 .0134158 26.31 0.000 .3267393 .3793285
1  .5789474 .113269 5.11 0.000 .3569443 .8009505
——————————————————————————
Varma says
Jim,
Thanks for the valuable advice!
jamesey10 (@jamesey10) says
Hi Jim,
Maybe you can tell me if I’m on the right track or getting derailed.
I think an Ordinal Logistic Regression or Poisson Regression is right for me. I’m still obtaining my counts and learning RStudio, but I’ve been reading your site and others to figure out what I’ll do with the data I collect.
I have over 1200 observations from content analysis (text mining)
My dependent variable is a count of words relating to one of 5 levels (a,b,c,d,e.)
My independent variable is a count of words relating to one of 4 typologies (y,z,w,xorder does not matter.)
I want to determine if any of the 4 typologies correlate to any of the 5 levels.
Dan says
Hi Jim,
Thanks for you article, it is very helpful!
I have a question about my research: I want to examine if a correlation exists between clinical characteristics (amount of pain, degree of physical disability, etc.) and radiological scoring of an Xray. Most clinical parameters are continuous variables, but one is binary (success/failure) and another is Likert scale with 7 categories (ordinal of course). The Xray scoring is ordinal with 4 categories (from no degeneration to extensive degeneration).
I figure I could use Spearman’s rho for continuous variables; Kendall’s tau, Somer’s d or Goodman and Kruskal’s gamma for Likert; and rankbiserial correlation coefficient for binary variable (success/failure). This way I can correlate these variables to the ordinal Xray scoring.
The next step is to do regression analysis to determine which independent variables account most for the Xray scoring. Since my dependent variable is ordinal I was thinking of ordinal logistic regression. But is it possible to incorporate continuous, ordinal and binary independent variables into this model?
Kind regards,
Dan
Jim Frost says
Hi Dan,
Yes, ordinal logistic regression is the way to go for your data. It can handle continuous and binary data with no problem. It can handle categorical data with recoding as indicator variables, which most software should do automatically these days. Your ordinal independent variable is a bit problematic. You’ll need to include it either as a continuous variable or a categorical variable. There are pros and cons for each way. The correct answer depends upon a combination of the goals of your analysis and the nature of your data. I don’t have article to point you towards but I do write about it in my regression ebook. I write about in in the context of least squares regression, but the same principles apply to ordinal logistic regression.
Best of luck with your analysis!
Varma says
Great post, Jim.
I am dealing with a challenge. I am not sure what regression analysis method to use to analyze a data set where the independent variables are in Likert scale (1 through 5 – completely disagree through completely agree) and the dependent variables are Likert items (1 through 5 – completely disagree through completely agree).
Your advice would be greatly appreciate.
Thanks in advance
Jim Frost says
Hi Varma, because your dependent variable is ordinal (Likert scale), you’ll need to use ordinal logistic regression. However, including ordinal independent variables is problematic. You’ll need to include them as either categorical or continuous variables. Each approach has strengths and weaknesses. The correct choice depends on the goals of your analysis and the characteristics of your data. I write about it in my ebook about regression analysis.
Best of luck with your analysis!
YUSUF JAMAL says
Hello Jim,
I want some help in determining a threshold poplation density.I have two data sets.One is the poulation density in many counties of US and another is number of cholera disease cases in those counties.I wat to figure out that population density at which there are 50% chances of getting infected with cholera.Can regression help me here?
Thank You so much
Jim Frost says
Hi Yusuf,
It sounds like you need to use binary logistic regression because that’ll allow you to calculate the probability of an event occurring (cholera infection). You’d need the number of events (infections) and opportunities (county population) along with the population density values. Although, I’m not sure if any US county ever got up to 50% cholera infections.
ronnie says
hello jim, so my doctor in college gave me the task to make a regression analysis on the impact of smoking, exercise level. and i dont know how to approach it.
EJ says
Hi there! I am trying to find the right correlation test for my data. I have a continuous independent variable, and a categorical dependent variable (with 4 possible categories). Do you have any suggestions for this type of data?
Jim Frost says
Hi EJ,
I’m not sure that finding a correlation is what you’d want to do with that type of data. Instead, use a oneway ANOVA to determine whether the mean of your continuous variable is different between the four groups. Click the link to see an example of that analysis.
Alexis says
Hello Jim , i need your help with my masters project, i have a set of audio recordings from which i extracted the MFCC matrix and the corresponding image sequence from which the mouth landmarks were detected for each MFCC frame, my goal ultimately is to give an audio to a system that will predict the mouth landmarks for each MFCC vector but i literally have no idea what that system should be or how to proceed , can you give me any advice concerning that?
Yusuf Jamal says
Hello Sir,
Is HosmerLemeshow statistic really important in binary logistic regression? Does a pvalue of less tan 0.05 of HosmerLemeshow sufficient enough to discard the otherwise good binary logistic regression model?
Thank You
Jim Frost says
Hi Yusuf,
In binary logistic regression, the HosmerLemeshow goodnessoffit test compares the observed and expected frequencies of events to determine whether the model adequately fits the data. It evaluates whether the differences between the expected probabilities and the observed probabilities are statistically significant. Consequently, your low pvalue indicates that your model does not fit the data well because these differences are significant.
In this case, try different link functions and/or change your model.
haneen says
Another question, sorry but I am a beginner in statistics. Now I have to check the residual plot ( same as scatterplot right?) for positive correlation right? If there is a positive correlation does it mean the dependent variable is normally distributed? Or shall I do another test to check for it’s normal distribution? One more question, I have to check the residual and the normal distribution for the dependent variable “for the means?” or for the original responses?
Thanks alot for your response
Jim Frost says
Hi, I have an article about residual plots. I put a link in my other reply. That’ll answer your questions.
haneen says
Dear Jim,
Yes there is a positive relationship, you mean by the residual plot the scatter plot right? Can I send a screenshot for the scatter plot as a message on your fb page just to make sure that all is ok?
Jim Frost says
Hi Haneen,
You use residual plots to check the OLS assumptions. Read those posts for more information.
Haneen says
Dear Jim,
I have a sample of 140 instructors and 21 tool in Moodle website and I want to show statistically that the the awareness of Moodle tools (in general) influences its usage.
I conducted a 5items likert scale survey. Two questions for each tool, one asks about awareness of the tool and the other about usage of the tool. Then I calculated the overall usage of each tool (by finding the mean of the responses for each tool) and the overall awareness of the tool (by finding the mean of the responses for each tool). then I used a simple linear regression between the overall awareness and the overall usage (i.e. between the means) (resulting in 21 plot in the scatter plot)) is this right???? or shall I make the linear regression between between all responses for all tools?? (21*140 dot in the scatter plot)
Can you kindly reply a.s.a.p?
regards,
Jim Frost says
Hi Haneen,
I’m not quite clear about your model. For Tool A, are you looking at the mean awareness of tool A by all subjects and the mean usage of tool A for all subjects? And, you have 21 tools, so for your model, your 21 observations are basically 21 means for the DV and the IV? And each mean is calculated using 140 values. Is that right?
You should be able to fit the model that way. Typically, each observation is an individual but in your case each observation is a mean. But, it should tell you whether higher awareness leads to higher usage. You should find a positive relationship. Just be sure to check the residual plots. Using 140 values to calculate each average should allow you to treat the underlying ordinal variables as continuous data. However, because they are based on ordinal data with a constricted range and the difference between values might not be constant, you might have curved relationships and nonnormal distributions. Pay particular attention to the residual plots. If you satisfy the assumptions, you should be good. If the distribution of the DV is highly skewed, you might need to use a transformation. Or possibly fit curvature.
Rose says
Hi Jim,
I really liked your article, I only have one question. My dependent variable is a categorical variable with three levels, and my independent variable is a discrete variable with 5 levels. Is ordinal logistic regression then the right way to analyse my data?
Thanks in advance!
Rose
Vanessa says
Hi Jim,
Thank you so much for all your valuable posts! i was going over them in order to prepare for my own analysis.
I decided to both run an ordered logic regression (ordered categorical dependent variable) and a multinomial logic regression (unordered categorical dependent variable). The question i right now have is what is the best way to interpret my coefficients received from the logit regressions?
shall i use the odds ratio? the RRR? or simply use conditional or average marginal effects? is there anything i need to take into account?
Many thanks already very much for your reply!! I am so looking forward to it!
Jamaldeen Nurudeen says
please, which type of regression is appropriate to quantify the influence of funding on academic success and why?
Jim Frost says
Hi Jamaldeen, you don’t pick the type of regression based on the subject area but by the types of variables you have, particularly the type of dependent variable. So, determine what your dependent variable is and then look through this post for the types of regression that can analyze that type of dependent variable.
David Lee says
Do you discuss use Information Theortic Approach in your blog? In particular do you discuss use of AICc in comparing models? I recently purchased your Regression Analysis book but don’t see this discussed.
Gaia Fiordispini says
Thank you very much Jim! I really appreaciate your responsiveness.
I know it would be better to consider it as a nested dataset, and this makes the analysis more complex and difficult to understand for me. Since I am a marketing student and not a statistician, and my research question wether there is a relationship between the maturity levels (fixed values for each brand, that I want to use as independent continous variables) and the consumer mindset metrics (different for each respondents, who answered for one brand only, and that I want to consider as my dependent variable)… wouldn’t it be sufficient to run a correlation? It would come with limitations but I have to answer my research question with my competencies.
Thank you and all the best!
Jim Frost says
There are several considerations. If your DV is Likert, you can’t use the regular Pearson’s correlation, you’d need to use Spearman’s correlation.
However, there is a larger issue with the correlation approach. When you include multiple independent variables in your model, it automatically controls for all the variables in the model. Consequently, the effect of each variable is calculated while controlling for (i.e., holding constant) the other variables in the model. You lose that with correlation because you’re assessing IVs one at a time. You could end up with biased estimates–basically omitted variable bias, which might or might not be a problem with your data. That’s a risk to weigh and involves subjectarea knowledge that I don’t have. Read here to see an example of this problem in action.
I’m rethinking the nested design as I think I’m understanding your design better. Nesting occurs on the IV side of things. And you don’t have that there. Respondents are answering for one brand, but their responses are on the DV side. So, disregard that. I’d perform ordinal logistic regression with no nesting.
You could perform Spearman’s correlation but with the caveat I mentioned earlier.
Gaia Fiordispini says
Thank you! My supervisor suggested me to think about CLUSTERED DATA and perform REAPEATED MEASURE MULTIVARIATE ANOVA. This should imply to transform my dataset? And, also, I do not understand if it is the proper way to analyze my data, or if a specific type of correlation (e.g. I tried to perform Spearman) can be enough!
Jim Frost says
Hi Gaia,
Unless I misunderstood your design, which is possible (see my other comment), it doesn’t sound like you’re using repeated measures. It does sound like you have a nested design because each respondent is answering about one brand.
My other comment has thoughts about the type of regression and other details about your model. It’s a rather complex mix of things, at least if I’m understanding correctly.
Gaia Fiordispini says
Hi Jim! Could you please reply to my question? It is important for my Master Thesis!
Jim Frost says
Hi Gaia,
So sorry about the delay. I’ll get to it soon. It unintentionally fell through the cracks!
Lauren says
Hi Jim,
Great article! I am still a bit confused. Would a type of regression still be best if the data included 1 categorical independent variable (two groups), 2 continuous dependent variables, and 1 categorical dependent variable?
Jim Frost says
Hi Lauren,
Typically, you have just one dependent variable per regression model. If you have multiple dependent variables, you’ll need to fit separate models.
You can use least squares models to fit models for your continuous dependent variables with the categorical independent variables. Although, if you have a continuous DV and just two groups, you can use a 2sample ttest for that instead of regression.
For the categorical dependent variable, you’d need to use a binary logistic model if it has two levels or a nominal logistic model if it has more than two levels and assuming there is no natural order to the levels.
I hope that helps!
Diego says
Hi Jim,
Great article! Thanks for the info.
I have a question regarding a doubt I have on which regression to use for the dataset I’m currently working in.
I’m trying to analyze the impact of HIV on MSK health among women aging with HIV. So, I assessed body composition measures (fat mass, lean mass, etc) a in a cohort of women HIVpositive and women HIVnegative to see if there are differences between them. Also, I did some physical test (grip, chair standing among others). So, I want to see if body composition measurements affects in the scoring of those physical tests, and how HIV influence that.
In this case, as body composition measurments are continues variables and the physical test scores are also continues variables should I use a linear regression model? But I’m not sure how to plot the HIV impact part on that model. Thanks!!
Best,
Diego
Gabriel Villas says
Hi Rahma!
If you want to investigate the level of attendance, you dependent variable will be Categorical, because it has a natural order (Low, Medium and High) to obey.
Considering this, the regression model I suggest you is the Ordinal Logistic Regression.
Kind regards,
Gabriel Villas.
Jim Frost says
Hi Gabriel,
A categorical variable with a natural order is an ordinal variable, and I agree with the recommended analysis. Even better would be to record the number of days attended and use either least squares regression or Poisson (or related) regression depending on how well the count approximates the normal distribution. You’d probably get more information that way.
Gaia Fiordispini says
Hello I have a question regarding a dataset I must analyze. I did two research steps:
1. Based on a managerial maturity model, I ranked 8 brands according to their level of usage of social media tools. For example how many different content formats they use form 3 to +5. They are a total of 7 components that, summed all together, give the total maturity score of each brand.
2. I made a survey among consumers to measure their level of brand awareness, consideration and purchase intent. Each respondent was randomly assigned to a brand for which he had to give his rankings, so I have 8 conditions in the dataset.
3. Now my hypothesis is that there is a relationship between the level of maturity and the consumer mindset metrics (namely higher levels of maturity should score higher levels of mindset metrics).
So the questions are:
– I am analyzing the full sample because each condition has only 50 respondents. Should I split the dataset per condition for some analysis?
– The variables I want to compare are: interval Likert scales 15 for the mindset metrics (dependent) and ratio (probably I will rescale them from 0100% for example) that are fixed for each condition (independent). How to do correlation and regression analysis? Specifically, which regression is more appropriate?
Sorry for the long request but I tried to be as precise as possible.
Jim Frost says
Hi Gaia,
This is much deeper into the real deep details of study than I can usually go in blog comments. As you’ll see, your design is a complex mix of elements. From your description, I’m not sure that I have a completely clear understanding of your study, which makes it difficult to comment. However, I think I’ve got and here are some thoughts.
If your dependent variable consists of ordinal data (Likert scale), you’ll probably need to use something like ordinal logistic regression–unless the DV is a sum of multiple Likert items, which you can sometimes treat as continuous. If the DV is a sum of multiple Likert items, you can try using least squares regression but you’ll need to pay particular attention to the residual plots to ensure you’re getting an unbiased fit.
It sounds like each respondent is nested within one condition (brand). If that’s the case, you’ll need to use a nested design. You’ll probably need to include respondent as a random factor (as opposed to a fixed factor) in your design.
That’s a complex model to run and you might need to consult with a statistician at your institution to help you out.
I hope that helps! Best of luck with your analysis!
Naduth Must says
Hi
I have a dataset with 3 continuous variables in which the dependent variable includes negative values too. How can i decide wat type of regression to apply(other than linear)? by looking at the scatterplot or something?
Rahma says
Hi!! I have a question!
I have data on the number of classes that 86 student attended.
I have grouped those students into LOW attendance, MEDIUM attendance and HIGH attendance, based on the number of classes they attend.
And I have many Independent Variables (age, gender, BMI, smoking status, employment status, martial status), both categorical and continuous.
1) Is my Dependent Variable categorical or continuous?
2) Which regression do I use?
Waiting for your answer!!! Thank you
Maggie says
Hi, Jim.
I really need your help on the appropriate test for my undergraduate thesis. I controlled 3 categorical variables (A,B,C). Each have 3 levels. This is a within subject experiment where each subject was tested for 12 conditions (e.g. condition 1: A1,B1,C2; condition 2: A1,B2, C2…) Could you please suggest a test to analyse the contribution of each variable? I was thinking of the binary logistic test since the response is binary.
Thank you so much for your attention!!
Jim Frost says
Hi Maggie,
In general, that sounds like the right approach. One potential hiccup is that given the withsubjects nature of the study, you’ll need to include subject as a random factor. I discuss this a bit in my post about repeated measures designs. I’ve never seen a similar type analysis with a binary dependent variable and a random independent variable. So, I’m not sure if there’s software out there that can do that analysis. I’d consult with a statistician that can look into the special needs of your study and find the best solution. If it weren’t for the random factor, I’d say that binary logistic regression would meet your needs.
Katuta says
Hi Jim
I wanted to analyze visual acuity outcome, which is primarily continuous variable but I have made the range and categorised it into three groups as good outcome, borderline and poor outcome, can I use ordinal, multinomial or still I required to use linear regression?
My independent variables are, age, sex, type of cataract, commobidities, and level of education.
Thanks.
Jim Frost says
Hi Katuta,
You can use ordinal logistic regression. However, if you have the continuous data for acuity outcome, you might consider analyzing it using linear regression on the continuous data rather than converting it to an ordinal variable. But, if you do transform it as you describe, use ordinal logistic regression.
Priya Mohan says
Hi Jim,
Your post is very helpful and too clear, thank you. In my observtional study, the outcome variable is categorical, dichotomous – yes/no and the independent or explanatory variables are also categorical example – age groups, gender, education, smoking etc. I understood that we need to use binary logistic regressiON. Is this right? and how would we interpret beta coefficient?
Fads says
Hi Jim, I have a question on the type of regression analysis that would be the most appropriate for me to use . I’m investigating the impact factors such as ethnicity, geographical region and gender have on educational attainment at GCSE level
I have 3 categorical variables; ethnicity, geographical region and gender which im measuring against a continous dependent variable.
Thank you!
ariel says
Hi JIm,
Thank you again for the prompt response.
My regression lacks the offset variable.
So, in the Parameter Estimates table (SPSS) of this regression there is an Exp b column and I wonder how to refer to its values as IRR or RR?
thanks,
ariel
ariel says
Hey Jim,
You are right my dependent variable is not a count variable but it is not a pure rating variable either. It is difference/delta between the student rating pre and post a course and it has a poisson distribution. Is it wrong to use a poisson regression for this type of variable?
Thank you for the prompt answer,
Ariel
Jim Frost says
Hi Ariel,
Yes, you can use Poisson regression in your case. And, that does seem to be borne out. However, it might raise questions amongst reviewers because it is an unusual use for it. However. Poisson regression is designed for data the follow a Poisson distribution. Usually that means count data. However, if your DV follows a Poisson distribution, the procedure will work fine even when it’s not count data. Just be ready to explain that!
ariel says
Hey Jim,
I have a dependent variable that is the variation between likert scale student’s ratings pre and post a course they took. I did a KolmogorovSmirnov test and found that this variable has a poisson distribution so I constructed a multivariate poisson regression (without an offset variable).
My first question is did I construct the right regression?
My second question is that the Parameter Estimates table of these regression has an Exp b column and I wonder if to refer to it as IRR or RR?
thanks,
ariel
Jim Frost says
Hi Ariel,
Typically, you use Poisson regression for count data. However, the student ratings don’t sound like count data. Ratings are ordinal data. Consequently, you should consider using ordinal logistic regression.
Aujah says
I need help finding the right regression model for a categorical, binary independent variable and 3 different continuous dependent variables. Please!
Jim Frost says
Sounds like you need to use a binary logistic model.
Erik says
Jim,
Could you give a thumbs up if zeroinflated models would be appropriate given the following question:
Is there a relationship between the number of backpacking trips in a 3year period among a purposive sample (n=50) of participants in a outdoor leadership class (dependent variable) and their age, sex, occupation, and race (independent variables)?
Note: there is some evidence that many of the participants did not go on a backpacking trip in the last 3 years.
Thank you in advance and stay safe!
Jim Frost says
Hi Erik,
From what you describe, it sounds like a zeroinflated model might be appropriate. At least as a something to consider. You need to consider whether there are two separate processes that you can describe. One would be the desire to go on the trips. Some people want to go while others don’t. That will explain some of the zeros. Then, of those who want to go, there’s an expected distribution of trips. That distribution will likely include zero because I’d imagine there are people who would want to go but for some reason or other weren’t able to. So, you’d have the two sources of zeros, those who just didn’t want to go and those who wanted to go but weren’t able to go.
However, you don’t have to go straight to a zeroinflated model. You can try a regular Poisson or negative binomial model to see if you get a good fit. Sometimes you can even get a good fit with least squares regression when the mean count is high enough.
So, it does sound like a good possible approach but you might give the simpler models a try first. See if you do in fact have an excessive amount of zero trips that the simpler models can’t explain.
Best of luck with your analysis!
Luis Saenz says
Hi Jim, first thank you for making statistics and regression analysis so easy to understand for the masses.
I am currently working on an analysis to find the cost of an hypothetical zero load carrying gas distribution pipe. All of my variables are continuous, as the pipe size increases the cost of it as well. But I have two main types of material (Plastic and Metal). How can I do this analysis, doing a regression analysis for each material type and then average the results or using an ANOVA? Im lost.
Thank you
Miro Mar says
Valuable information and it explains the subject to its primary scratch!!!
I just want to know the difference between Regression analysis and ANOVA?
Jim Frost says
Hi Miro,
If we’re comparing ANOVA to least squares regression, they’re actually the same analysis but have different historic traditions and terminology. However, the math “under the hood” is the same.
Regression typically works with continuous predictors, although you can add categorical variables. For categorical variables, regression uses binary coding (1, 0) so that you compare the results for each categorical value to a baseline value.
ANOVA typically uses categorical factors. These factors will often use effects coding (1, 0, 1), which allows you to compare each factor level to the overall mean (rather than to a baseline group). You can include continuous predictors but they’re called covariates. ANOVA is more focused on explaining the differences between groups that are formed by categorical variables. Consequently, ANOVA is also linked to post hoc testing that carefully assesses the differences between multiple groups. However, you could perform the same post hoc tests on regression results.
Despite these differences, you will obtain completely consistent results using both of these methods. Although the differences between coding for categorical variables can make some of the coefficients appear to be different, but they’re telling the same story. If you can control the coding scheme in your statistical software, you can obtain completely identical results.
Jyoti Pandey says
Hi Jim,
This post is very useful i just need to know that if we have continuous dependent variable and three independent variable one is continuous second nominal and third ordinal, in this situation how to calculate regression.
Thanks!!
Jim Frost says
Hi Jyoti,
Yes, you can use least squares regression for that type of model with one caveat. There’s no way to include an ordinal independent variable in the model. You’ll need to include it in the model as either a continuous variable or a categorical (nominal) variable. There are pros and cons either way. I don’t have a post that discusses that decision but I do write about it in my regression ebook.
Best of luck with your analysis!
Gabriella says
Hi Jim, thanks for the detailed breakdown of the different types of regression analysis!
I would like to seek your advice on how I should run some of my own tests for my undergrad final year study, which is on the topic of how temporary depletion of level of executive functioning may affect honesty behaviour in young children.
– My main IV is executive functioning: either depleted or not depleted (categorical).
– My other variables are a mix of categorical and continuous variables, namely: gender, age (in years), Theory of Mind score, and English vocabulary score.
– My only DV is the child’s honesty behaviour: either truthteller or lieteller (categorical).
I am currently about to carry out my statistical tests on SPSS, and have dummy coded my categorical variables to numerical codes.
I haven’t learned the more advanced statistical tests in my undergrad courses, but based on what I have read in your post, I think the Binary Logistic Regression is most suitable for my study’s design. The question I have is, how do I proceed from there?
1) Specifically, how do I control for my other variables (i.e. gender, age, ToM score, vocab score) to ensure that my model is mostly looking at the effects of executive functioning on honesty behaviour? Is that already accounted for by the regression? If not, do I need to do additional tests even before I carry out my regression? What tests would these be to control for these other factors?
2) Conversely, how do I check if the other variables have main effects or interaction effects with my main IV, assuming that they may present? Can the regression reveal these? If not, what other tests should I run?
I am sorry if these questions are really basic, I am just a mess when it comes to math and statistics. I would appreciate any feedback I can get from you! Thank you and stay safe!
Jonny says
Hi Jim,
I hope you’re keeping well wherever you are during these uncertain times.
I’d really appreciate it if you could give me some help when you get the chance!
I’m writing up the results from my study which is looking at the effect of a single nucleotide polymorphism (random genetic variation) on postprandial glucose response.
I have identified no significant difference between any of the three genotype groups and any of my glucose response outcome variables (fasting, peak, 2hour postprandial, and incremental area under the curve). I have 73 participants in my sample (27 female and 46 male), and identified a significant difference between sexes for 2hour postprandial glucose concentration.
Using SPSS statistics, I would like to conduct some kind of multiple regression to assess the effects of both sex and genotype on the different measures of glucose response (primarily 2hour postprandial to begin with). Therefore, my variables will be categorical.
Would I have to assign numbers to my categories?
I am unsure if my data has a linear relationship, and I don’t know how to check this in SPSS especially with categorical variables. Most of my outcome measures are also nonnormally distributed.
Will a nonnormal distribution and lack of linear relationship prevent me from doing a multiple regression analysis?
Thank you in advance!
Jim Frost says
Hi Jonny,
Can you more fully describe the nature of your dependent variable and how you’ll record it? That would help me answer. Thank you!
Gretchen Edstrom says
Thank you so much!
Easy explanation of the different types of regression
Nibaldo Colina says
Hi Jim,
I would like to establish the correlation or lack there of between the cost of accidents and the experience level of the person who caused the accident. I have three levels of experience.
Thank you
eva says
thank you jim! its all making much more sense. i guess im getting confused because ive been seeing some examples where people use t tests or mann whitney u tests to compare things like age or amount of a drug (continuous variables) to a categorical outcome, but that would obviously be incorrect since they’re the independent variables not the dependent variables right?
appreciate your help!
eva says
Hi There! thank you very much for this. I was wondering what kind of analysis you would use to compare a continuous independent variable (i.e. age) with a binary categorical dependent variable (i.e. mortality)?
would that just be a logistic regression?
Jim Frost says
Hi Eva, yes, that sounds like a situation where you’d use a binary logistic regression model.
Sujit says
Hello Jim,
So nice of you !
Thank you so much for the guidance 🙂
Regards
Sujit
Jim Frost says
You’re very welcome! Again, sorry for the delay! 🙂
Sujit says
You Never replied my query 🙂
Regards
Sujit
Jim Frost says
Hi Sujit,
Apologies for the delay. Sometimes life gets in the way, especially during these days.
Because you’re doing a count of walkins, it sounds like you might need Poisson or Negative Binomial regression. Although it depends on the distribution of your count variable. If the count is high enough, the distribution can approximate the normal distribution and OLS might be okay to use. You’ll need to define a consistent period for the count. For more information, read the section in the post titled, “Regression Analysis with Count Dependent Variables”–it’s near the end.
S says
Thanks a lot for the explanation!
I’m collecting data at 2 time points, and would like my variables at Time 1 to predict Time 2. So my DV is a variable at Time 2 and as predictors, I have 1 variable at Time 1 (not continuous) as well as a 1 categorical variable from Time 2 (condition = 1 or 2). I am assuming that I’ll need to use multiple linear regression?
Regression is new to me, so I’m not sure which model to use if I have 1 categorical predictor and another predictor that isnt continuous.
Jim Frost says
Hi,
What type of variable is Time 1 and what type is Time 2?
Anwasia Anthonia says
I want estimate farmers willingness to use renewable energy. So I want to find out if I can use switch regression to analyze willingness to use. Thanks and waiting your response
James Knuppenburg says
I want to test if science and technology has increased peak performance and age amongst different positions in the NFL. What type of regression do you think would be best for this type of test?
Jim Frost says
Hi James,
Choosing the type of regression analysis is not usually based on the subject area. (There are some exceptions to that rule.) In other words, there’s not a correct type for performance in the NFL. Instead, you need to determine which variable(s) are your dependent variables and the nature of the relationship they have with the independent variables. If your DVs are continuous, I’d recommend starting with least squares regression and determining whether you can obtain a satisfactory fit with it. You can always change analyses as need. Sometimes you’ll learn things along the way that require you to change the type of regression. Although, advance research will help you in that regard as well.
Best of luck with your analysis!
Steven says
Hello! Hope this finds you well. I am running a multilevel multiple regression analysis for my dissertation. At the individual level, my IV is bullying experiences in schools and DVs are three different health outcomes for students (1 dichotomous, 2 continuous; all mental health). I also have a moderator (LGBTQ identity, dichotomous). This data has been identified all at the school district level for an entire state. At the group level, I have voting data for school districts (second IV; currently in percentages: % for Hillary, % for Trump, in 2016 election). My analysis is hoping to explain the relationship between school district voting records, bullying experiences, and health outcomes for LGBTQ students.
I am planning on using Mplus for analysis. I have a few questions after reading this article:
1) What would your recommended analysis plan be? After reading this article, I was unsure how to apply this to a multilevel, multiple regression analysis in Mplus.
2) What would be the best way to insert voting data into the data set for analysis, given that it is in percentage form? Currently, I was thinking of calculating the difference between voting (80% Clinton, 20% Trump = .60; 20% Clinton, 80% Trump = .60 on a scale of 1 to 1 from most conservative to most liberal). That way, it could serve as a continuous IV at the group level (level 2) of the multilevel regression.
Lemme know what you think. Thanks for any help you can provide a novice statistician!
Sujit says
Thank you so much Jim . Have a great vacation
Regards
Sujit
Sujit says
Hello Jim,
Any suggestions on my request
Regards
Sujit
Jim Frost says
Hi Sujit, I’m on vacation now. Will reply when I return. Thanks.
Dessalegn Wudima says
I present my thesis proposal entitled Impacts of ICT on Tax Administration for the Degree Master in Development Economics, I have prepared a yes no questionnaires does ICT has an impact on cost of taxation, tax compliance and tax revenue collection, but here the challenge is which model is preferable, for the time being in the proposal I mention to use Multilinear regression using OLS, so my question is that my model choice really good?
bedilu adugna says
HI JIM ,
I am going to do master thesis on determinant/factors that affect graduate youth unemployment and which regression model you advise me to use for single dependent variable
Jim Frost says
Hi Bedilu,
I’d start with linear regression and see how that works. From there you, see how well the model satisfies the assumptions.
Best of luck with your thesis!
Dan says
Hi everybody,
I’m using panel data to examine the effect of CEO age on tone of annual reports for 5 years. I have applied this model but I am not sure whether it is the best model or no. I am using Stata, year as dummies and random effect:
xtreg tone CEOage controls i.year, re robust
Any help would be hugely appreciated.
Yours Sincerely,
dan billy
Linda says
Hi,
I want to use a regression model to find the number of charging stations in cities (which is a count data) with respect to different features of the cities (i.e., population, vehicle miles traveled, etc). I tried different models and linear regression gave me the best goodness of fit. Is it wrong to use linear regression for this purpose?
Jim Frost says
Hi Linda,
The Poisson distribution, which is based on count data, begins to model the normal distribution when the counts are large enough. So, it might be entirely appropriate to use linear regression. Be extra careful in checking those residual plots. If those look good, it’s probably OK to use linear regression. With smaller counts, it’s harder to satisfy the OLS assumptions.
Keep in mind, that you can only interpret goodnessoffit statistics when the model satisfies the assumption, which you assess by residual plots. A good goodnessoffit doesn’t necessarily indicate that the residuals are good. Read my post about Rsquared for more about this issue. If the residuals aren’t good, you can’t trust the model regardless of what the goodnessoffit statistics indicate (i.e., you can’t trust them either).
Sujit Dora says
Hello Jim,
Need your guidance for an exercise where I need to establish Walkin to a store as a function of number of print inserts by a brand + print inserts by competition
Regards
Sujit
PV says
Hi Jim . If my dependent variable is “Number of job switches” , it is Discrete random variable. Can I still use MLR?
Michael says
Jim, thanks for the great information. While describing PLS regression you mentioned this is a good technique for predictive models but not good for screening variables. My motivation for wanting to understand regression techniques is largely because I want to be able to make predictions about things. What about PLS regression makes it better suited for this? Can’t we make predictions from any regression technique that fits the data?
Jim Frost says
Hi Michael,
I guess I wouldn’t say that PLS is better at prediction than other methods in general. Rather, PLS is a good method to use when you face particular problems–few observations per predictor and multicollinearity. In this context, PLS is relatively good at predictions and not so good at screen variables. However, if you don’t have those problems, I wouldn’t consider using PLS and would probably start with OLS. If you have severe multicollinearity and are interesting in determining the significance of particular variables, consider Ridge or LASSO regression.
Ivac says
Hi Jim,
I am a junior doctor and I need to do some paper in Statistics about ,,Types of regression analysis used in biomedical sciences”. Can you help me where I can find that data and which are the types of regression analysis used in biomedical sciences? Everyone is mentioning just linear regression analysis.
Thank you in advance.
Jim Frost says
Hi,
I wouldn’t be surprised if linear regression is the most common. I’ve also heard that binary logistic regression is commonly used in things like surgical procedures where the researchers want to model the variables that affect the probability of survival. They use binary logistic regression because the outcome is binary (patient either survives or does not). I can also imagine them using binary logistic regression to model the success or failure of other treatments. I once helped a fertility doctor to model the probability of pregnancy by the treatment and other factors. Again, the outcome is binary (pregnant and notpregnant).
Hospitals could use Poisson regression to model the availability of hospital beds. The outcome is a count of available beds.
I think the medical sciences could use many different types of regression just like other fields. It depends on the nature of the dependent variable and the characteristics of their data. I would guess that linear regression and binary logistic regression are two of the more common types.
I hope this helps!
Naz says
Hi Jim,
I have a binary outcome variable (presence of disease = yes or no)
And would like to study the effect of different independent variables on the presence of disease
Examples of my independent variables are as follows:
geographic longitude
geographic latitude
age
all of which are on a continuous scale
Which regression model should I use?
Thank you
Jim Frost says
Hi Naz,
Given your binary outcome (dependent) variable, you’d need to use binary logistic regression.
Elsa Pakpahan says
Hi, Jim. I’d like to ask, I want to examine how selfcompassion (iv) can predict depression (dv), selfcompassion itself is formed by 6 dimensions, could you suggest what regression that would fit for my research? I already run simple linear regression for sc and depression, so if I use multiple regression for the 6 dimensions and depression, am I doing it right? or should I run simple linear regression for each dimension and depression?
an answer would be really appreciated, thank you! 🙂
Jim Frost says
Hi Elsa,
The best place to start with is least squares regression. Use multiple regression so you can include all your IVs in the model. You don’t want to fit separate models for each IV because it won’t be controlling for the other variables.
If you had to form the 6 dimensions because of need to reduce the number of dimensions, and now you want to perform a regression on the dimensions (which are not variables), you might need to use something like partial least squares (PLS) regression instead. That will find the dimensions and perform regression on them. I don’t have much experience with that type of regression. There might be other methodologies that I’m unfamiliar with that you can use. Generally, you’ll have a specific need for reducing the number of dimensions and working with those rather than just using the variables themselves.
Randunu Galhena says
Thank you your post,
i’m doing advance regression modeling for house price prediction, so there have very large number of variables.do you can give me a suggest a method for the dimension reduction?
Jim Frost says
Hi Randunu,
I’ve seen regression models for house prices that use least squares regression. Unless you’re using a small sample size, I’m not sure that you need to reduce the dimensions. I’d at least start your investigation using least squares regression and move away from that only if it’s really required.
deepanshu mishra says
Hi Jim,
what if I want to apply regression if I have variables of # of complications like: dengue, Malaria etc. and have variable of deaths due to these complications. which regression should be used in this data.
thank you!!
Jim Frost says
Hi Deepanshu,
One thing I hope to have conveyed and the readers understand about this post is that the type of regression you use large depends on the type of variables you have, particularly for the dependent variable (DV).
For your case, you don’t state how you’re recording deaths. What type of variable is the DV? Are data recorded by patient and you have a binary death/survival outcome for each patient? If so, binary logistic regression. Is it by hospital (or geographic region) and you have counts of deaths, you could use Poisson, Negative Binomial, or possibly least squares regression if the counts are high enough to approximate the normal distribution. In that case, you might need to convert it to a rate for comparison purposes.
Alaa says
Hi, I am a student, am still confused which type of regression should I use, the Dependent variable is binary (Benign or Malignant).
Independent variables are three
1 Age group: which are 6 groups (1,2,3,4,5,6)
2Color: binary variable only two color option.
3 Third variable is also binary variable.
What should I choose?
thanks in advance Mr. Frost.
Jim Frost says
Hi Alaa, you’d use binary logistic regression.
Zaafir says
Hi I have 11 continuous random variables X1 . . . X10,Y. I need to create a regression model for predicting values of Y based on the values of X1 . . . X10. Which regression should i chose.
Jesse says
Hi Jim, sorry for the confusion! Each observation is a single Likert Value, so I guess Ordinal logistic regression applies. Could stepwise regression still be used with this kind of data?
Jesse
Jim Frost says
Hi Jesse,
Yes, it sounds like you’ll need to ordinal logistic regression. Also, as I mentioned in an earlier comment, you’ll have some thinking to do about whether to treat the ordinal DVs as continuous or categorical. There are pros and cons both ways. More details in my ebook about regression analysis!
Jane says
I mean to say discrete and continuous variables…
Jane says
Hello
Thank you for your time in answering our questions.
I have a problem and I would like your help please.
I have a dependent variable that takes the value 0 and 1. This variable is explained by other independent binary and continuous variables.
Only in the survey the answers of my dependent variable are conditioned by the answer obtained to a previous question ( say Q1)
so that if Q1 = 0 “no” then Q2 (which is my outcome) is asked.
if Q1 = 1 “yes” then Q2 is not asked.
Q1 and Q2 are not exclusive and Q1 is not a selection issue. The individual interviewed can answer yes to Q1 and also answer yes to Q2. I don’t understand why the survey design excludes them and it really bothers me
My question is, apart from a multinomial model, do you see any other models that I could apply to get around this serious selection problem?
Jim Frost says
Hi Jane,
I’m not entirely clear about the scenario and what you want to analyze.
Q2 is your outcome. And, only particular respondents are presented Q2 based on responses to other questions. At first, it sounded like it depended entirely on their response to Q1, but that might not be the case. Maybe other answers as well? You’ll have to examine how the survey presents questions to respondents to understand that portion.
If you’re analyzing Q2 as your outcome variable, I’d suggest figuring out who exactly answered it. If you can identify a particular subpopulation that answers Q2, you can perform the analysis and understand that the results apply only to that specific subpopulation. If Q2 is binary, you could use binary logistic regression with Q2 as the DV.
I hope that helps!
Jesse says
Hi Jim, Thank you for the response! I’m not sure I understand your question, but I think they are the score of each item, the scores for each scale is not averaged or summed. I think because its a Likert style scale with an absolute zero (ratio data), we would treat it as a continuous variable. But would this be a stepwise regression method to see which variables stay in the equation (i.e., are still significant and have the highest coefficient)?
Thanks!
Jim Frost says
Hi Jesse,
I’m asking whether for each observation (i.e., row in your data sheet) the values for each variable are a single Likert value (1, 2, 3, 4, 5, 6, or 7). Or is each value an average or sum or multiple Likert scores?
It makes a difference. A straight Likert score item, as I described before, is an ordinal variable. It is not a continuous variable. However, sometimes you can treat sums or averages of multiple Likert scores as a continuous variable. In fact, if you’re using a single Likert score for each value of the DV, you’d need to use ordinal logistic regression rather than least squares regression.
Jesse says
Thank you for the response Jim! To clarify, we had customers fill out a survey, all likert (17) style questions. We had an overall satisfaction question (Are you satisfied with our company overall?) and multiple satisfaction questions based on dept (e.g. How satisfied are you with our customer service?, How satisfied are you with our webpage?).
We basically wanted to see which dept was the best predictor of Overall satisfaction… essentially, which area of our company should we focus on most based on satisfaction scores?
Thanks again!!
Jim Frost says
Thanks for the extra details! I don’t use SPSS so I wasn’t familiar with their Enter Method!
One more question. Are the values for the independent variables and outcome variable for each observation the score of one item (e.g. 1, 2, 3, 4, etc) or the sum or average of multiple items? The answer to that will affect the method you can use. Least squares regression doesn’t play well with ordinal outcome variables. However, if it’s a sum of multiple Likert items, it might work fine.
For the IVs, if you’re using the value of individual items, it’s an ordinal variable which has the properties of both continuous and categorical (nominal). You’d have to go with one or the other. (I talk about that in my ebook about regression analysis.) If you use a sum or average of several items, you might be able to treat them as continuous IVs.
In theory, you should be able to do what you want to do. However, ordinal variables (such as Likert items) can be a bit tricky and you need to work out those details and choose correct analyses.
I hope this helps!
Jesse says
Hi Jim,
This is a simple regression model but I’m getting a difference of opinions on the answers. I’m looking at overall customer satisfaction (likert scale), and I want to know which area of the company is contributing most to this.. each area of the company each has their own likert scale. Would I just use overall satisfaction as a DV and put the different areas of the company as IVs in a ‘Enter’ type regression?
Thank you Jim!
Jim Frost says
Hi Jesse,
I don’t know what you mean by an “enter” type regression.
Here’s what it sounds like to me that you want to do. Please let know if it’s incorrect. You want see if there’s an association between area of the company and the Likert score? If so, you can do that. The dependent variable is Likert score, which is an ordinal variable if you’re just using a single Likert item for the DV values. In this case, you’d need to use ordinal logistic regression. If you want to use area of company (e.g., department) as an independent variable, that would be one categorical variable where the different values are the various departments. You can fit that model and see if there is a statistically significant association between department and the Likert score.
If you are summing multiple Likert scores and using that summed value as the DV, rather than just using the score for an individual item, you might be able treat the DV as a continuous variable and use least squares regression or ANOVA to assess the differences by department.
I hope this helps!
Jesse says
And by “Enter Method’ I meant in SPSS the ‘Enter’ procedure in linear regression (as opposed to Stepwise, or backward elimination methods. I just put in all the different depts as predictors and total sastisfaction as an outcome variable, then looked at which predictors were significant, and their coefficients.
James says
Hi Jim, thanks for all your great posts! Apologies if this has already been covered but perhaps you can offer some further clarification.
Let’s say I have two variables, a continuous variable (e.g. test performance) and a categorical grouping variable with three levels (e.g. group 1, group 2, and group 3).
My question relates to how to structure the regression analysis itself. We could model “test performance ~ group” (so test performance is the DV and group is the IV). Alternatively we could use a logistic model “group ~ test performance” (so group is the DV and test performance is the IV).
Are there rules of thumb here in deciding which way to structure the model? Are both valid or does it depend on the specifics of the experiment and the underlying data?
Any thoughts on this would be appreciated!
Jim Frost says
Hi James,
If you’re creating a model to explain the relationships between variables, you’ll use the explanatory variable(s) as the independent variable and the variable you want to explain as the dependent variable. Often you’ll use theory, subjectarea knowledge, and literature to guide you about the direction of the relationship. In your example, if you think the group assignment might cause differences in the test scores, you’d use group as the independent variable and score as the dependent variable. You want to learn about how the different groups affect the test scores.
However, if the test scores measure some characteristic of the individuals that might affect the group that the individuals join, you could use test score as the independent variable and group as the dependent variable. You want to learn about the characteristic that the test measures affects the group that individuals join.
So, a large part is the direction of the relationship that you are assuming to be true based on theory and subjectarea knowledge.
However, if you’re creating a model to make predictions, then you’ll always use the variable you want to predict as the dependent variable. Imagine you want to predict variable A, which is correlated to variable B. Suppose the value of A causes changes in the values of B. In an explanatory model, you’d use B as the dependent variable because that fits the known nature of the relationships. However, imagine that you want to predict the value of A. In this case, you’d use A as the dependent variable and B as the independent variable.
In the end, you can really do the analysis either way depending on whether you want the model to explain the actual relationships between the variables or you’re making predictions for a variable.
Jessica Walker says
Hi Jim,
I am trying to determine the predictive power of a nominal independent variable with 8 groups (age cohorts) for a continuous count dependent variable (number of stress incidences). What test do you think would be most appropriate? My samples are also very small (<5) for some age cohorts.
Âákäsh Síñglâ says
sir i have this business problem
Company collected data from 5000 customers. The objective of this case study is to understand what’s driving the total spend of credit card(Primary Card + Secondary card)
Prioritize the drivers based on the importance.
and i have to built linear regression model.
please help me out like how to deal with two dependent variables at a same time with a set of predictors
Jim Frost says
Hi Âákäsh,
You’re asking about learning how to use, perform, and interpret multiple regression analysis. That’s way more than I can write about in a comment!
However, you’re in luck, I have written an ebook all about regression analysis, with a focus on multiple regression. Please take a look at my intuitive guide to regression ebook!
Naty says
Hi Jim,
Thanks for such an insightful article!
I am trying to predict the best time (date) to contact a customer for an offer by analysing purchase history. I want to use Regression for this but having a hard time to decide which one to use. I have considered using logistic Regression where I thought I will see if in a month it is best time or not. But on the other hand also think that date is a continuous variable so I might be better off using multiple linear regression.
Would be very helpful to hear your thoughts.
Many thanks,
Naty
Jim Frost says
Hi Naty,
In making this decision, a good place to start is to understand your dependent variable. What’s the outcome that you want to explain/predict. What type of variable is it? If it’s something binary, like Sale/No Sale, and you want to use independent variables such as date/time to predict that, you’d need to use binary logistic regression. With binary logistic regression, you can use independent variables such as time (continuous) and day of week (categorical/nominal) to predict the binary dependent variable of sales/no sales.
That sounds like the direction you might want to go, but it really depends on the nature of your dependent variable.
Yaynabeba says
Dear Sir,
If both independent and dependent variables are categorical and dependent variable have natural order, which test variables do you suggest?
Jim Frost says
Hi, sounds like you should use ordinal logistic regression!
I don’t know what you’re studying. You’ll have to use your subject area knowledge to determine which test variables to include. Depends on what you’re testing!
michael says
i am trying to analyse car accident data set.
there is 1 dependent and 1 independent variable in my equation
dependent variable is categorical (4 category) and represents number of wounded person
1 (1 person wounded)
2 (2 person wounded)
3 (3 person wounded)
4 (4 or more person wounded)
independent variable is also categorical (6 category) and represents crash type
1 (rear crash)
2 (side crash)
etcetera……
which type regression should i use to analyse crash type effect on wounded person number?
Jim Frost says
Hi Michael,
There would seem to be a variety of problems with modeling this data set.
The DV is sort of a count variable except for the four or more category. For count data, you can often use Poisson or Negative Binomial regression as I indicate in this blog post. However, because of the four or more category, it’s not really count data. I suppose the best would nominal logistic regression and treat your DV as a categorical variable.
However, there would seem to be too much information left out of your model to get meaningful results Number of passengers in the car, speed, and seat belt usage just to name three quickly. You might have the worst possible accident yet only have 1 person injured if there is only one person in the vehicle. I suppose if you had a large enough sample size you might get some meaningful information.
Again, you can try nominal logistic regression. Or even perform a chisquare test of independence because you have two categorical variables.
Dr. M.A Habib says
Dear Jim,
I have scale (income) dependent variable and both scale and categorical variables. Which regressuin model is appropriate?
Abeeha Zaidi says
Dear Sir,
I have a continuous dependent variable and a categorical independent variable with three groups.
whereas my covariates are continuous and few are categorical as well. Which statistical test can i apply?
can i use linear regression or ancova?
Ranjith Kumar says
Dear Sir,
If both Independent and dependent variables are catagorical variables, Which test you suggest to test variables except Chisquare sir?
Jim Frost says
Hi Ranjith, you can use nominal logistic regression. This type uses categorical dependent variables with categorical and/or continuous independent variables.
Lore says
Hi Jim!
Thank you for excellent explanations. I hav plotted my data and my curve looks more like a logarithmic graph. How do I best calculate the correlation between my two variables?
Lore
Jim Frost says
Hi Lore,
It sounds like you might need a loglog relationship, which you can read about in my post about loglog plots. For details on a variety of other methods you can use, read my post about curve fitting.
Ah shoot, I noticed after replying that you’re asking about correlation rather than how to fit the curve! I need more coffee! Try using the Spearman Correlation which can assess monotonic relationships. That’s a fancy word that means that the variables tend to move in the same relative direction (either a positive or negative correlation) but the rate of change is not constant.
Sunita (@SunitapandeyNep) says
Hi Jim,
Thank you for posting several useful information regarding data analysis. I follow your website regularly for statistical matter.
I have one question. Can we natural log transform independent continuous variable. I have independent variable of different distance points (1 to 180 m with nineteen measured distance points with inconsistent data ranges).
My dependent variable is overdispersed count data (and I am using negative binomial with log link function).
Please could it be possible for you to answer this question.
Thank you.
Jim Frost says
Hi Sunita,
Thanks for reading! I’m happy to hear that you find my site to be helpful!
Yes, it is possible to use the natural log to transform a continuous independent variable. Of course, there’s no way I’d know whether it’ll resolve whatever problem you’re experiencing. I’d recommend fitting the model without the transformation and checking the residual plots. Then, transform your variable and fit the model again and check the residual plots to see if it helped. Transforming the IVs is particularly helpful for fitting specific types of curvature, which you can assess using residual plots.
Using transformations is a bit of a trial by error process. There are some guidelines that can help, but it often comes down to trying them to see if they do the trick. I write about this in detail my regression ebook. Although, I don’t write about it in the context of negative binomial regression.
I hope this helps!
Elsa3335 says
Thank you for your time, Jim. Looks like i have to crack my head for some time.
Elsa
Elsa3335 says
Hi Jim, I’m completely lost when trying to choose the type of regression analysis for my problem. Is it nominal logistic regression, time series, linear (autoregressive model), nonlinear or other? Can you please give me some advise?
345
658
116
380
651
851
709
196
269
071
478
518
451
989
519
843
662
428
794
949
Suppose, everyday I’m given 20 data as above. They’re random numbers generated by three devices, namely Device A, Device B and Device C. Every device is capable to generate random numbers from 0 to 9.
For the above first data of “345”, “3” was generated by Device A, “4” was generated by Device B and “5” was generated by Device C. Similarly, for the last data of “949”, “9” was generated by Device A, “4” was generated by Device B and the last “9” was generated by Device C.
I want to be able to predict the next outcome variable after the last “949”.
I have a total of 300 historical data for 15 days, 20 data per day.
From my rough observation for the recent 300 data, every device tends to produce repeated sequences. For instance, Device A will repeat the sequence “86479” (as in the last 5 data). That means this sequence appeared at least twice within the 300 historical data for Device A. Similarly, for Device B and Device C, each of them tends to produce repeated sequences. For another example, the repeatation of the sequence “51855” (the 2nd to the 6th data) in Device B.
I’d like to predict the next outcome variable after the last “949”. Is it possible to model it with nominal logistic regression, or time series (if i assume that 20 data was taken at successive equally spaced points in time)?
Otherwise, which model is more appropriate?
Thank you in advance.
Elsa
Jim Frost says
Hi Elsa,
Based on the limited knowledge that I have, this problem doesn’t sound like one that regression analysis can solve. If it’s random as you say, then you can’t predict it. However, if there are patterns in the data, it might be more of a pattern finding problem.
If there was some underlying phenomenon that influenced the numbers that the devices display, you might be able measure the phenomenon and link it to outcomes and use regression analysis to model that process.
Sorry I couldn’t be of more help. But it sounds like you’ll need to use something other than regression.
Youssef says
Hi Jim,
Great website. I have a quick question, excuse me if you have covered it before and i did not notice. When you build a regression model, multiple linear regression model or just simple one, do you need to have all variables with the same units to conduct the regression? . For example, if i want to study the relationship between stock market returns which is in % terms and funds flow which is in dollar terms, do i need to transform both variables to the same unit of measure ie lets say percentage % to study the regression? Thank you.
Sophie Holland says
Hi Jim,
Thank you so much for your reply, very helpful but I am just unsure as to how to test for a linear relationship between a categorical IV and continuous DV… Could you please advise?
When trying to do scatter plots you just get a straight line…
Thank you in advance for any help you can offer!
Jim Frost says
Hi Sophie,
When you have a categorical IV, the type of relationship you’re testing for differs than between two continuous variables. When you have a categorical IV and a continuous DV, the analysis tests for differences between group means. The categorical IV defines the groups in your data. It’s performing an ANOVA with your data to see if the DV mean varies between groups. And, you don’t use a scatterplot to graph that type of relationship but either a boxplot or individual value plot.
I don’t want to keep pushing my book, but I go through an example of a regression model with both categorical and continuous IVs in it and show how to interpret the results.
Sophie Holland says
Hi Jim,
I love the way you explain the different types of regression so I was wondering if you could help me on a project I am doing…
My IV is bullying victimisation which is categorical (never, some of the time, all of the time) and my DV is internalising problems which is on a scale from 120. I also have covariates some continuous, some categorical. What statistical method do you suggest I do? I have quite a few outliers in the DV but as I am using the MCS these are hard to remove and due to their being quite a lot I don’t believe I have a logical reason to remove them. I am also not sure on how to test for a linear relationship with my IV being categorical.
Do you have any suggestions for the above?
Look forward to hearing your thoughts!
Thanks!
Sophie
Jim Frost says
Hi Sophie,
In your other message, you clarified that the DV is a continuous variable, although constrained to a range of 120. Consequently, I’d start by using a least squares linear model first. Try that first and see if you can get a good fit–check the residual plots. Specify a variety of models and possible fit curvature and interaction effects. The constricted range of the DV might be a problem if it causes the scores to be extremely skewed. If your DV is very skewed, you might need to use a data transformation. The residual plots will help you determine what you need to do.
As for the outliers, if they’re a regular part of the distribution of outcomes for your population, it’s often best to leave them in as they represent variability that is inherent in the process. However, if they represent scores from people outside your defined population, or some other fluke during measuring, testing, etc., that doesn’t represent the typical process, consider excluding outliers. Just be sure to record the removed outliers and explain why. But, the default position should be to leave them in unless you have a good rational for removing them.
I write more detail about the above in my ebook about regression. You might consider getting because you’re using for your study.
Best of luck with your study!
Syed Abbas says
Hi Jim,
Could you please suggest any source where I can find why ordinal regression is preferable over multiple regression analysis. Even if you have written any thing on it that will be also great. I am in argument with my team to use ordinal but they says opposite so need some strong sources. I will be very thankful to your help!
Jim Frost says
Hi Syed,
When you have an ordinal dependent variable, you should use ordinal logistic regression. Ordinary least squares regression is unlikely to provide value results when you have an ordinal dependent variable. Ordinal logistic regression was designed with ordinal DVs in mind. This decision is based on the type of variable you’re using for the dependent variable.
First, ensure that you have an ordinal dependent variable. If you do, I’m not sure why your team would resist ordinal logistic regression?
I don’t have a reference on hand at the moment, but I will check when I can. However, any advanced regression textbook that covers ordinal logistic regression will state this clearly!
Best of luck with your analysis!
Katy says
Hi Jim,
Thanks for your helpful blog.
I’m hoping you might be able to offer some advice on a project I’m working on to understand customer telephone contact. The sort of questions I’m looking to answer are as follows –
Are customers with certain characteristics more likely to contact us?
Do customers with certain characteristics contact us more frequently?
I have a snapshot from our customer database including customer characteristics such as age, gender, marital status, whether have children, geographical location, etc, as well as the number of telephone calls made in that month. One row for each customer.
My rough plan is to look at the data and run off some descriptive statistics (scatter plots, box plots, etc) to see how frequency of contact varies by different characteristics. Then depending on what that shows, I’m hoping to construct a regression model(s). This is where I’m a little stuck in terms of the type of model.
I’m planning to start by looking at the probability of a customer making a call (a binary logistic regression?) I think this would tell me which characteristics increase/decrease the probability of a customer making a call versus no call.
I’m then thinking an alternative would be to define the dependent variable on the basis of low/medium/high frequency callers and run a regression on that basis. I think an ordinal logistic regression?
Could I also use calls per customer per month as the dependent variable and run a standard linear regression? Or would this be classified as count data and require a different model?
Grateful for any thoughts.
Kate
Mario says
Greetings Jim
I conducted a simple multiple regression. I firstly tested all the assumptions and then created the models. I would like to know how I can validate these models? Do I have to perform additional tests or can I use information from the model analysis outputs? NB. I do not want to run complex tests that are difficult to interpret. Kind regards and many thanks,
Mario
Jim Frost says
Hi Mario,
Unlike most statistical procedures, with regression analysis you need to check the assumptions after you fit a model. You can’t check the assumptions before fitting the model. Sometimes you can get a sense that it’ll be hard to satisfy the assumptions before fitting the model. For example, if your dependent variable is highly skewed, it’ll be harder to satisfy the assumptions. So, fit the models and then check the assumptions. To learn more about that process, read my post about Checking the OLS Assumptions.
When you say “validate” the model, there are several ways I can interpret that. One is that you want to ensure that it satisfies all of the assumptions. If you mean that, read my blog post above. It doesn’t involve performing any complex statistical tests.
However, often people will validate a model when they use it to make predictions. They want to ensure that the model predicts new observations as well as it predict the observations used during the model fitting process. If this is what you mean, you’ll need to have a training dataset and a validation dataset. Use the training dataset to fit the model. Then, apply the model to a separate validation dataset and assess how well the model predicts the new set of observations. This process is known as crossvalidation.
Because your study involves regression analysis, you might consider buying my ebook: Regression Analysis: An Intuitive Guide. My ebook covers the material in more depth than the blog posts.
Best of luck with your analysis!
Min says
Hi Jim,
Thanks a lot for the excellent article!
Can I ask if I have a mix of continuous and categorical dependent variables, can I still use partial least square regression?
Thanks!
Jim Frost says
Hi Min,
As far as I’m aware, you can use PLS only with continuous dependent variables.
Abbas says
Thanks a lot Jim
Abbas says
Hi Jim you did not reply to me? 🙁
Jim Frost says
Hi Abbas,
So sorry about that! Sometimes when I get really busy some questions are accidentally missed. I’ll answer your previous question within a day.
Tanvir Ahammed says
Great post Jim. I really like the way you explain the different types of regression.
But I’m little bit confused here. What if my dependent variable is a score, say having value 1100 & my independent variable is a categorical one? Is poisson regression ok in this case?
Jim Frost says
Hi Tanvir
Thanks for the kind words!
If you working with a score that has at least 10 values, you can often treat it as a continuous variable. Consequently, I’d try using linear regression.
Poisson regression wouldn’t really be the right choice because you’re not counting outcomes. That’s usually what you use Poisson regression for. Even then, if the mean is high enough, the Poisson distribution begins to approximate the normal distribution and you might still be able to use linear regression.
At any rate, I’d start with OLS regression. If the scores are highly skewed, you might need to transform the data.
Best of luck with your analysis!
Anastasia says
Dear Jim,
What type of regression would you recommend in a case when the key independent variable is not regularly reported? My dependent variable is a firm’s investment policy reported every fiscal quarter. The independent variable of the interest is a debt covenant strictness reported just at the moment when the firm issues any loans. I’ve tried to use investment changes over next 4 quarters (8 quarters) after the loan inception as a dependent variable. But I’m not 100% sure that it is a correct empirical design. Is it possible to use as the dependent variable also the investment levels instead?
Thank you!
Best,
Anastasia
Prosper says
hi which one can be the best model when you want to establish the relcatiionship between tax consultant and tax compliance
Jim Frost says
Hi Prosper,
Usually you don’t pick the type of regression model based on the subject area. In other words, there is no model that is best for taxation studies. Take a look at the type of dependent and independent variables you will use in your model and then read through this post to see which models can handle those types of variables.
Best of luck with your study!
Aki says
Hi Jim,
Loved your post.
I wanted a suggestion from you regarding my project work.
Could you please suggest which model I can use for regression.
Following is the brief overview of the problem I am facing:
My dependent variable(y) is a nonnegative integer and independent variables are categorical as well as continuous variables.
There are 3 continuous variables and 4 categorical variables on which(y) would depend.
Could you please suggest which regression models should I test in this case?
Jim Frost says
Hi Aki,
If your nonnegative integer is a count variable, look in the section for Count Dependent Variables for ideas.
Best of luck with your analysis!
Catherine Glover says
Hi Jim,
I have been reading your articles on regression analysis – they have been super helpful for me as I am just learning about regression analysis!
I am writing a research proposal on what factors influence voters to still vote for their preferred party when they have knowledge of that parties involvement in corruption.
previous research has indicated that voters continue to support the part as they personally benefit from the corruption. Whereas other academic conclude that loyalties to certain parties are a result of a clouded vision causing cognitive dissonance. The most frequently argued factor as to why individuals forgive a corrupt official is that if the economy are secure and improving. Finally, the more extreme the individuals party is (far left or far right) the more likely they are to vote for another party due to a lack of viable alternatives.
I wanted to use a MCQ questionnaire to collect data on all these factors and treat them as independent variables and use regression analysis. Is this possible? and if so how should I go about this. I have spent about 2 weeks on the proposal and I am not totally sure
Best wishes
Catherine
Jim Frost says
Hi Catherine,
I’m so happy to hear that my regression articles have been helpful! If you like them, you might consider my ebook, which is all about regression analysis.
Yes, it is possible to include MCQs in an analysis. The manner in which you include them depends on the type of answers. If they’re categorical (nominal) variables, such as, “What type of damage? Scratch Dent Tear,” you’d include it as a categorical IV. If it’s an ordinal variable, such as Likert scale responses, they straddle the line and you can potentially enter them in the model as either categorical variables or continuous variables. It depends on the nature of the data and the goals or your analysis. My ebook talks about all of these aspect, categorical IVs and how to handle ordinal IVs.
Best of luck with your analysis. It sounds interesting!
Mario says
Hello Jim
I trust you are well.
Could you perhaps tell me how I should go about testing the assumptions for generalized linear models.
Thanks,
Mario
Josh says
Hi, does your book cover all the regression analyses you covered in this blog post?
Jim Frost says
Hi Josh,
The ebook focuses on linear regression. I cover many aspects of that including, continuous and categorical IVs, main and interaction effects, modeling curvature, checking assumptions, goodnessoffit, predictions, etc. I offer a few glimpses into the other types of regression, but they’re not the focus.
I can foresee a future book going beyond linear regression and covering the other types in more detail.
To get a better idea of exactly what the book covers, go to my blog post about my ebook. At the bottom you’ll see screenshots of the full TOC.
You can also go to My Store and download a free sample of the ebook, which has the complete first two chapters.
I hope this helps!
Sidney Gueye says
Great thread, this was really informative and has made me rethink my current regression model.
My undergraduate dissertation project is on how perfectionist concerns, strivings, and coping skills (7 sub scales) influence sport acute injury occurrence and frequency. The perfectionism data cam in the form ofa composite score made of two subscales from different questionnaires with different item number (5 and 7 items). Thus, a standardised composite score was created. which regression analysis tests would you recommend for an SPSS newbie?
Thanks
Frank says
Hi Jim,
It would be great to see the kinds of estimates that can be obtained from each of these models, and possibly some of the interpretations that might come with using one of these models. Thanks!
Frank
Jim Frost says
Hi Frank,
In future posts, I’ll do that! For now, you can see examples of nonlinear regression and binary logistic regression. I’m always adding posts and will get to the others at some point.
Thanks for writing!
rksd says
Hi Jim,
I’m having a bit of trouble selecting the right nonlinear regression for my analysis. I have data that looks like an inhibition dose response curve with a variable slope. The X is not dose however it is time and I am trying to compare the rate of closure between multiple samples over time. If I do a nonlinear fit of [inhibitor] vs response with a variable slope, the curve fitting fits my data very well. I was wondering if in this scenario using a generated hill slope would be sufficient to evaluate rate? Would there be a more appropriate regression or should I consider an alternative such as area under the curve?
Thank you so much for any advice!
Jim Frost says
Hi, I often find that in nonlinear regression models, subjectarea knowledge becomes even more important than it usually is. In nonlinear models, you’re often trying to incorporate underlying physical or biological processes into the regression equation itself. I once saw biologists debate over which nonlinear model is best for plant growth. It turns out that the best model depends on a plant’s specific growth pattern. All of this is to say, I don’t feel like I’m qualified to answer your question. You should consult with subjectarea experts and see how similar studies have tackled the problem. Sorry I couldn’t provide a more specific answer!
Paola Gabriela Villa Paro says
Hi Jim,
I’m searching for a regression model for the following: I have a categorical dependent variable which comes from a multiple options question, I mean, the individual was being asked for his preference about six different saving alternatives. He could choose all the alternatives that he prefers (he can choose even all the six alternatives). So, I don’t know which could be the type of regression that I could apply.
In conclusion, my dependent categorical variable is nonexcluding so I can’t apply a multinomial logit or multinomial probit. Which other models could I use?
Thanks in advance.
Lingyun Huang says
Thanks Jim. Very appreciate it
Franco says
Hello Jim
I have a question about my study. Both the IV and DV in my data are categorical. IVs (nominal) are 5 types of behaviors and DVs (ordinal) are 3 types of reasoning. I am interested in how IVs predict DVs. I am wondering if logistic regression fit my study? Thanks in advance for your efforts.
Jim Frost says
Hi Franco,
It sounds like you need to use ordinal logistic regression!
Psy says
Hey Jim,
I got a query.
Suppose we are given voting turnout results (1 for yes, 0 for no) of 4 previous years. And we want to predict it for the next year, and hence we do not have the 2019 result column. Logistic regression need X train and Y train, but in this case we do not have a Y train. How should I proceed with this? Or should I use some other model?
Anu says
Dear Jim, I came across this article of yours. Very informative. I am a student doing chiasma count on chromosomes. Now I want to compare and contrast different varieties just based on this count number. The count range is limited ranging from 2 to 8. I cannot go for Poisson distribution as it is known that the chiasma occurrence on chromosomes is not random. Infact, two pathways occur, one is non random and second is random, but most of the counts are accounted by non random pathway. I do not know this, just based on the count data. Can you suggest any analysis which can be done to find out differences between varieties.
Allie Hicks says
Hi Jim!
Fantastic blog post. Thank you for laying this out so clearly.
For the regression I am trying to run, the independent variable is number of classes attended. The possible options being 1, 2, 3, and 4 classes attended. To me, this seems like an ordinal variable, but I am not sure.
Is it okay to proceed with a regular linear regression? The output variable is change in symptoms of depression (a continuous variable).
Thank you in advance!
Allie
Jim Frost says
Hi Allie,
That’s a count variable. Count variables can have nonnegative integers. This type of variable is different from a ordinal variable.
An ordinal variable does have a natural order. You can represent ordinal variables using numbers. So, that looks similar to a count variable. But, suppose you are recording how runners finish in a race. You have the first, second, and third place runners that you record as 1, 2, and 3. However. the second place finisher doesn’t necessarily take twice as long as the first place finisher even though 2 is twice the value of 1. You only know that 2nd place is after 1st place. 3rd place is after 2nd and 1st, but it’s not necessarily three times as long as 1st.
Contrast that with counts of 1, 2, and 3. A count of 2 is twice a count of 1. A count of 3 is three times 1. The difference between 1 and 2 is the same as the difference between 2 and 3. Etc.
When you have count IVs and ordinal IVs, neither are quite continuous (ratio) nor are they quite categorical (nominal). You’ll have to decide which way to enter them into your model. That depends on the nature of your data and the goals of your analysis. I write about this in my regression ebook that I just released. But, that’s the issue in a nutshell.
It’s often OK to include this type of variable in a linear model. Just be sure, as always, to check those residual plots to be sure that your fitting an adequate model!
Shohreh Kariminezhad says
Hi Jim!
First of all many thanks for your helpful, comprehensive tutorial on regression. I have a question that I really appreciate if you reply to it. What is the best possible way to choose the most relevant features in a linear regression model? How we can check the features?
Thanks in advance for your time
Jim Frost says
Hi Shohreh,
I’m not sure what you mean by “relevant features”? That can range anything from the type of data that a type of regression can model to key statistical output. Can you be more precise what you’d like to know? Thanks!
Abbas says
Hi Jim,
Great and very helpful blog. the way you explain is really helpful.
I want to run a regression on SPSS using customer satisfaction scale (5 point scale) as dependent variable. and other variables as independent variables such as some are on 5 or 10 point scale, some are yes/no. so basically I want to check what are the variables that impact customer satisfaction. Could you please help me to find out which regression analysis I suppose to use?
Jim Frost says
Hi Abbas,
Sorry about the delay in replying!
Because your dependent variable is an ordinal variable, you need to use ordinal logistic regression. In SPSS, you should use either the PLUM (easier to use) or GENLIN procedure. You can find PLUM in the Output Management System Control Panel under Command Identifiers.
Best of luck with your analysis!
Chris says
Hello Jim,
First and foremost, thanks a lot for writing this blog – it’s pure gold.
However, I have a dilemma.
I have the data with 5 variables and with 100 units of observation. Every 10 units in the data encompass specific time frame – so first 10 units encompass one year, then next 10 units encompass the following year etc. For each of those 10 units, I have a distribution of 100% for each variable. This means that each variable in the first 10 units makes 100%, then the next 10 units also 100% etc. My dilemma is that units reflect the distribution of something (which makes a sum of 100% for each year) so I am not even sure can this be considered a continuous variable. Here is an example: https://i.imgur.com/dxCQfHT.png
But, to be concrete, I would like to know whether I can use multivariate regression analysis with this data where I would focus on one variable as DV and the other as IV in order to see which IV impact the most DV.
Jim Frost says
Hi Chris,
Thanks for kind words about my blog! 🙂
It sounds like you want to perform regression analysis with time series data. There are no inherent problems with that but there are special issues you’ll need to address. For example, you’ll need to include time related independent variables in your model, such as the date and lagged variables. You’ll need more specific date information than just the year. You’ll need to know the time between observations. You’ll also need to pay particular attention to correlation between adjacent residuals. See assumption #4 in my post about OLS assumptions.
I don’t see a problem with having the total within a year sum up to 100% of that year. That’s going to be true with all time series data! At least if I understand what you’re saying.
I don’t see an inherent problem with doing what you want to do as long as you know the time between the observations. If there’s a constant time between observations (observations occurred at fixed intervals) you can probably use the ID column as an IV. Just be aware of the issues I mention. In addition to the time related variables, it’s OK to include other IVs. Regression with time series data is not my specialty, so that’s about as far as I can take you. But, it is doable.
If you can fit a good model, here’s a post that can help you determine which IV is the most important.
Bella says
Hello Jim, thank you for your helpful posts. I am trying to find out influence statistics for 32 states in a country and the states are in scale, nominal variables. How do I conduct spss with that kind of variable?
Jim Frost says
Hi Bella,
I’m not completely sure what you’re referring to by influence statistics. Are you referring to variables that influence an outcome? Or, perhaps statistics that identify unusual observations in a regression model that have a large influence on the results if they were to be removed from the dataset?
Bkr says
Thanks a lot
Another questions:
can me use Spearman coefficient to check the correlation between binary variable and continues variable?
And can me use person chi square between nominal and nominal or ordinal variables?
Jim Frost says
Hi, no, you can’t use Spearman’s correlation for that. With binary data, you have two groups. You can use a 2sample ttest to determine whether the difference between the means of the continuous variable for these two groups is statistically significant.
Samuli Junttila says
Hi Jim,
first, thanks for this great website! Very helpful.
I have a question: I have a dependent variable that has been recorded as nominal categories (i.e. defoliation of a tree 025%, 2650%, 5175%, 76100%). Should this be treated as nominal and a nominal logistic regression should be used although the intervals are equal?
I have also computed an tree attack level score based on similar categories (defoliation, discoloration, amount of resin flow etc.) by summing them, resulting in scores between 5 and 16, can this score be treated as a continuous variable and ols be used?
Thank you very much if you have time to answer!
Best,
Samuli Junttila
Jim Frost says
Hi Samuli,
I’m glad to hear my website has been helpful!
Your dependent variable has categories that have a natural order, Consequently, your DV contains ordinal data and you should use ordinal logistic regression.
I’m assuming the tree attack level score is the independent variable? If so, it is most likely fine using it as a continuous predictor. It appears you have a good range of possible values (516) as long the actual scores use most of the values. However, you can’t use OLS because you have an ordinal DV.
Be sure to check those residual plots! That’s always important but even more so when you’re combining noncontinuous variables to form a different continuous variable.
Best of luck with your analysis!
Bkr says
Hi Jim
Have a nice day
Thank you for above great article, I have some questions I hope to give me advice.
I will conducting study about Risk factors of Anemia among children (< 6 years ) in Sudan, the DV is childhood anemia I will classifying according to hemoglobin concentration level (HCL) as anemic (HCL=11 g/dl)
IV’s are: sex of the child, Birth order, child’s birth weight, child’s age in months, mother’s education level, mother’s age at birth …. etc
this data I will analysis in two stage, first stage I will using bivariate correlation to identify the association between DV (childhood anemia) and IV’s to check whether the IV’s are significantly associated with anemia or not.
In the second stage I will using the ordinal logistic regression between DV and IV’s ( which associated with DV in the first stage) to make a model, and chose the best model based on Akaike Information Criteria (AIC) and 2logLikelihood
The End.
My question Is this method it is correct?
Jim Frost says
Hi,
It sounds like your dependent variable is binary–anemic yes or no. If that’s true, you need to use binary logistic regression.
Additionally, Assuming the DV is binary you can’t use correlation to assess the relationship between the independent variables and the dependent variable. If you have the raw continuous HCL data, you can use correlation between the continuous IVs and HCL. Even though you’re using a binary form of HCL, that correlation with the continuous form can provide meaningful information. I would actually use scatterplots to graph the IVs by HCL. That will allow you see whether there is curvature you need to model.
You won’t be able to use correlation with your IVs like education level because that’s a categorical variable. However, you can use graphs like boxplots or individual value plots to see whether there are differences between levels.
Finally, assuming your DV is binary, use binary logistic regression. Assess the CIs for the odds ratio for each DV and determine whether it excludes 1. Check the residual plots!
Best of luck with your analysis!
Skip says
Jim,
If you have say 6 IVs and they are all categorical, doesn’t it limit the conclusions somewhat? Wouldn’t a mix of continuous and categorical be better? In other words, isn’t there more information in using interval variables or at least keeping the number of categorical variables to a minimum?
thanks!
Skip
Jim Frost says
Hi Skip,
In general, yes, continuous data provide more information than categorical data. However, you need to determine which variables actually explain your dependent variable. If for some reason categorical variables explain your DV better, well you’re pretty much stuck using categorical predictors!
You might have other reasons to use categorical variables or indicator variables rather than continuous data. For example, if you have a model that you’re using mainly for prediction and you need to use data that are easy to collect, you might use a series of indicator variables (aka dummy variables). This type of variable is simply a binary variable that indicates the presence or absence of a characteristic. This type of data might be easier/cheaper to collect for predictive purposes rather than gathering potential difficult/expensive to obtain continuous measurements.
Consequently, it depends on your subject area and the purpose/context for your model.
However, when analysts have continuous data and they are considering converting it to a categorical variable (perhaps based on ranges), I’d recommend against that unless there is a very good reason to do so.
I hope this answers your question.
Mario says
Hi Jim.
I have a mixture of categorical and continuous independent variables. My dependent variable is continuous based on a score. I have 4 dependent variables measuring quality of life. The 4 dependent variables are correlated so I can’t use general linear models. Hope this helps
Jim Frost says
Hi Mario, having a mix of categorical and continuous IVs is not a problem for OLS. And, if you mean that the IVs are correlated, generalized linear models won’t fix that problem. Run your model using OLS and check the VIFs. If VIFs are high, you might need to use Ridge or LASSO regression. Read my post on multicollinearity for more information.
Mario says
Hi Jim
I’m having a slight problem. I ran regression using Generalized linear models. How do I interpret the deviance or Pearson chisquare statistic for Goodness of fit. Are there ranges? My deviance statistic is quite high = 151.233.
Thanking you in advance
Jim Frost says
Hi Mario, first, help me understand why you’re using Generalized Linear Models rather than OLS? Thanks!
Rui Fang says
Hi Jim,
I have some questions:
1) Let say I have dependent variable which consists of ordinal data, 3 independent variable consists of ordinal data and 1 independent variable consists of nominal data, can I use ordinal logistic regression? Besides ordinal logistic regression, what is the regression analysis that I can use?
2) Before conducting the regression analysis, do I need to do any test on my data? For example the collinearity between independent variables.
Jim Frost says
Hi Rui,
Because you have an ordinal dependent variable, you’ll probably need to use ordinal logistic regression. For the ordinal independent variables, you’ll need to enter them in the model as either categorical (nominal) variables or continuous variables, depending on which form provides the best fit and the goals of your study.
As for collinearity, I usually fit the model and check the VIFs. However, VIFs only work for continuous data. If you enter the ordinal variables as categorical variables, you might need to use something like the chisquared test of independence to determine if they’re correlated. Only strong correlations are problematic.
Best of luck with your analysis!
shahd says
Hi
I need to compare two curves to find if they are similar or not. My question is what is the most appropriate statistical procedure to compare them. I used the compare function in SAS. What about comparative density or kernel distribution can I use that for my comparison. Could you please advise with this
Mario says
Hello, Jim! Thank for this great post.
I am conducting a study which predicts happiness (continuous dv based on a score). I have a multitude of IV’s (20) which are both categorical and continuous. My readings point to using ‘generalized linear models’ which can accommodate both types of IV’s, or, should I firstly try and reduce the amount of IV’s (can I use univariate regression?) and then only use variables which were found to be significant in the GLM? Am I on the right track?
Thanks Jim
Jim Frost says
Hi Mario, unless you have count data or binary data for the dependent variable, I’d think that OLS linear regression would be your first choice. If your DV is continuous start with OLS. OLS can accommodate both continuous and categorical independent variables.
Simple and multiple regression are really the same analysis (OLS regression) but have different names based on the number of IVs. But, it’s the same analysis really.
I’d start by creating a matrix of scatterplots that graphs all of your IVs by the DV. Use those graphs along with subjectarea knowledge to identify candidates. The scatterplots will also show you whether you need to fit curvature as well. Then, include your candidates in the model and go from there. For help on this subject, read my post about choosing the correct regression model.
Thanuja Mallikaarachchi says
Thank you, this article was really helpful for me.
Doe Godwin says
This is great and it has been of good use to me. Thank you for sharing. I have a concern, how will you interpret estimation done by using the PLS procedure.
Jim Frost says
Hi Doe,
PLS reduces the number of independent variables down to fewer number of components that are not correlated. Then, it performs OLS on these components rather than the original data. Typically, you use PLS more for prediction rather than understanding the role of specific variables because the estimate relates to the components rather than the variables. If your goal is to understand the role of the independent variables using an approach more akin to least squares regression when you have problematic levels of multicollinearity, try using Ridge regression.
Susan H. says
Hello Jim,
Great article. I am attempting to measure the influence of culture strength on institutional performance. Two questions: 1) My IV is currently a percent change measure, and ranges from negative to positive, less than zero and greater than one. Is there any regression I can run or do I need to recode into a categorical variable for logistic regression? Although that would be challenging as well since I have three values and not two values – negative, no change, positive.
2) Can my DV be a measure of variance such as standard deviation?
Thanks,
Susan H.
Abdi says
am glad that I found your page. Thank you so much for this informative post.
Actually, I have questions regarding data analysis;I have both scale and nominal variables as independent and 4 categorical dependent variables,how can i analys by which model?
Thank you!!
Abdi
Jim Frost says
Hi, it sounds like you need nominal logistic regression assuming that your DVs don’t have a natural order. If they have a natural order, then ordinal logistic regression. You’ll need to fit separate models for each DV.
Best of luck with your analysis!
Harriet noble says
Hi jim,
Thank you for sharing your statistical Knowledge its very helpful indeed.
I just wondered is there an easy was to tell from reading a research article if a statisticians has used a linear or logistic regression model? and if so how?
Thank you
Jim Frost says
Hi Harriet,
The key giveaway would be the dependent variable. If the dependent variable is binary, ordinal, or nominal, the analyst should be using logistic regression. Also, if the article refers to odds ratios, link functions, or the study uses deviance Rsquared, pseudo Rsquared, or McFadden’s Rsquared, they’re likely using logistic regression. Also, hopefully they’re transparent about which analysis they’re using!
shahd says
Hi Jim
How can I perform jacknife regression in SPSS and what is the difference between jacknife and bootstrapping
Sylvia Burgess says
Thanks Jim this is very helpful
Sylvia Burgess says
JIm I was reading your comments and they seem helpful…so here goes. I have ELLESL Training as an ordinal variable and Math Scores as a continuous variable should I run linear regression or logistical regression.
Jim Frost says
Hi Sylvia,
I’m not sure which variable is your dependent and independent variable. It makes all the difference! From a similar comment you made, I’ll assume that the ELLESL training is the independent variable and you want to determine the impact on math scores as the dependent variable.
Assuming that is true, you’ll want to use linear regression because your dependent variable is continuous. You have an ordinal independent variable, which can be tricky to use. Ordinal variables are a bit like continuous variables and a bit like categorical (nominal) variables. Yet, it’s not quite either. You’ll need to include the ELLESL training as either a continuous or categorical independent variable. If you included it as a continuous variable you might need to use either a polynomial or log transformation to fit curvature that might be present.
My suggestion is to start by using linear regression and fit different models that include the training variable as a continuous variable and then as a categorical variable and see which provides the best fit. Categorical variables use more degrees of freedom than a continuous variable, which can be problematic depending on your sample size and the number of levels in your categorical variable.
Best of luck with your analysis!
Charles says
Jim thank you for making statistics easy to learn. However, do you also have one for discrete time survival analysis?
Emma Kooij says
Hi Jim,
I was hoping you might help me shed some light on a math paper I’m doing for my Calculus class. I’m trying to find a regression that might predict the shape of a circle based on a rainbow arc.
Rainbows are actually circles, we just can’t see them fully from our perspective and I wanted to provide more evidence that a circle is in fact the best way to predict a rainbow’s curve using a regression. However, when I tried several regressions based on the points I had found using an outline of the visible arc, the end result looks more like very wide parabola rather than the narrow curvature of the actual circle it’s supposed to be. My professor is trying to push the point of having our paper sound as if we were explaining it to a high school freshman and that makes it a lot harder to explain and the mathematics somewhat tedious as we have to explain every step and justify our choices as well.
Is there any light you can shed on how I can make this a little more feasible?
Thank you,
Emma
Jim Frost says
Hi Emma,
You’ll most likely need to use a different type of regression analysis, such as nonlinear regression and then choose the correct form of the expectation function. At that point the analysis can fit the data to the model. I think that’s the answer to your problem. Use nonlinear regression and then find the correct expectation function–which I’d guess would be the formula for a circle.
I hope this helps!
Seun Opaleye says
Hi Jim, a quick one.
I sent a paper to a scopus index journal.
My model is a panel data with n=4 & T=21
I used fixed effects model to analyse the data.
The journal sent it back saying we have to test for unit root and stationarity because the data is 21years x 4countries. And one reviewer said try ardl.
What is your advise and how do I take care of this?
Jim Frost says
Hi Seun,
I’m not expert in time series regression model. However, I can confirm that those are legitimate concerns. I have not used ARDL (autoregressive distributed lag) myself. I have used ARIMA (autoregressive integrated moving average)a bit. I don’t know enough about the analyses or you subject area enough to make a recommendation, but yes, you’ll probably need to use something like those analyses. The problem is that you need to account for time order effects and avoid serially correlated residuals.
shahd says
so does the multiple regression model differ from the least square mode ? because both gave me the same results
Jim Frost says
There are different types of multiple regression, but, yes, it usually refers to OLS. If the results match, that’s what you’re using.
Sam says
ah ok thanks for all your help!
Sam says
For my positive data, it is split into ‘yes’ and ‘unsure’ and ‘no’ so I assumed that is a nominal variable? I may be wrong though!
Jim Frost says
Those are ordinal data. Twoway ANOVA might still work, but be sure to check the assumptions on the residual plots.
Sam says
Hi Jim,
thank you for your feedback. I have double checked and my variables are nominal! I am a bit confused whether it is possible to carry out a two way anova on just two nominal variables as I have looked online and the assumptions say I need two independent variables and a dependent variable?
If so, I would do the two way anova and then the post hoc test?
Jim Frost says
If they are nominal, then your comment about “more positive reviews” is not consistent with that.
Yes, twoway ANOVA is designed exactly for that scenario. Your two independent variables are your two nominal/categorical variables. The dependent variable is your continuous outcome.
Yes, do the twoway ANOVA. If you find that one or both of the independent variables are significant, perform the post hoc test.
Shahd says
Hi Jim,
I have a salary data set with 10 variables. I checked the multicolinearity and there is no multicolinearity. I need to use advance modeling technique other than regression analysis to get higher grade in my assignment. Could you please advise with this? What about principle component analysis will this improve my prediction model?
Jim Frost says
Hi Shahd,
It all depends on the nature and characteristics of your data. There is no inherently better analysis. In fact, if a linear model fits your data, then OLS is the best possible analysis to use. To see why, see my post about the GaussMarkov Theorem and OLS Estimates.
However, if your data has specific problems, such a multicollinearity, other methods can be better. Typically, you’d use PCA when you have a large number of correlated predictors often in conjunction with a small sample size. In that scenario, you could also use Partial Least Squares (PLS) regression.
OLS is a great starting place and if you find that it somehow fails to fit your data adequately, use that information about how/why it fails to find another analysis that addresses the problem. And, I wouldn’t say the alternatives are more advanced, they just address various problems/characteristics of your data. So, I really can’t make a recommendation. It depends on the specifics of your dataset.
Sam says
Also, I am a bit unsure how I would analyse the data once the anova tests have been run because even if it tells me there is a difference, I don’t think it tells you the relationship between the two variables e.g. I am trying to find out if those with more positive views buy more Fair Trade. I am assuming the test would tell me there is a difference between views and goods bought, but not whether those with positive view bought more? I may be wrong!
Thanks again for your help!!
Jim Frost says
Hi Sam,
For two nominal variables, you’d need twoway ANOVA. Typically, you’d perform ANOVA first and that tells you whether there is a statistically significant difference between group means. However, as you indicate, it doesn’t tell you the nature of those differences. To determine whether differences between specific pairs of groups are significant, you’ll need to perform a post hoc test, such as Tukeys. I need to write a post about post hoc tests! I don’t have one yet!
Now, one thing I notice in what you wrote. You mention “more positive reviews.” That sounds like it’s potentially not a nominal variable. Possibly ordinal or continuous? If so, that changes things. ANOVA is for nominal (categories). Although, it might possibly work for groups based on ordinal data. It can be tricky figuring out how to analyze data when you have ordinal data as a predictor/IV.
Sam says
Hi Jim,
That’s very helpful thank you! Would I be able to use a one way anova for two nominal variables?
Thanks again!
Sam
Sam says
Hi Jim!
I would be very grateful for your help with my data analysis.
I have a dependent continuous variable and an independent nominal variable. I wish to use correlation analysis to examine the relationship between the two variables but I do not think this is possible. How else could I analyse the two variables to see if there is a correlation?
Thank you!
Sam
Jim Frost says
Hi Sam,
It sounds like you need to use oneway ANOVA. This analysis will tell you how whether the mean of your continuous DV is significantly different across the levels of your nominal IV.
Because the IV is nominal, regular correlation is not possible in the normal sense. In other words, you can’t increase or decrease a nominal variable to see how the other variable tends to change. Nominal variables are a difference in type. Such as type of damage: scratch, dent, and tear. ANOVA will tell you whether the different nominal values (types) are associated with different mean values of your DV.
I hope this helps!
Ellie Sharaki says
Hi, Jim.
If my parameter estimates in ordinal regression show negative values, do I need to report them one by one or does it suffice if I report the statistical (in)significance in the sig. column?
Thanks,
Ellie
shahd says
I need to use advance modeling to get A in this class. Can I use lasso regression method. If i didn’t include the interaction term, is this will be a problem ?
Jim Frost says
Hi, they might if you can show that lasso fits a better model than OLS. Typically, you’d use lasso when you have multicollinearity. It does introduce a bit of bias to reduce the variance. That can be worthwhile in some scenarios. I have no idea if that will produce a better model for your data, but you can give it a try! If it doesn’t produce a better model, it’s not worthwhile doing.
As for the interaction term, again, that depends on your data and study area. If an interaction effect actually occurs in the study area, not including it in the model can produce invalid results. However, not all study areas have interaction effects. Read the link I shared with you in my previous reply.
Best of luck with your analysis!
shahd says
I did that and I got a good fit. And I need to show what other possible regression model for the salary data set as I was wondering if I have to include interaction term because 3 of those variables are dummy. could you please advise with this
Jim Frost says
Hi Shahd,
Fitting the correct model is a balance between statistics and subjectarea knowledge. There’s no way I can tell you exactly what you need to do. However, I write about the things you need to consider in my post about specifying the correct regression model. I think that post will be particularly helpful.
If you need to learn more about interaction effects, I wrote about that as well. You can include interaction terms for dummy variables. Would that make sense theoretically for your study area?
Best of luck with your analysis!
shahd says
Hi
What if I have a salary data set and my dependent variable is continuous and I have 10 independent variables one of them is not linear and I transformed it to be exponential
Jim Frost says
Sounds like you should start with OLS linear regression and see if you can get a good fit. That’s where I’d start.
Elisa says
Sorry I forgot to mention that v