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Statistics By Jim

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Basics

Principal Component Analysis Guide & Example

By Jim Frost Leave a Comment

What is Principal Component Analysis?

Principal Component Analysis (PCA) takes a large data set with many variables per observation and reduces them to a smaller set of summary indices. These indices retain most of the information in the original set of variables. Analysts refer to these new values as principal components. [Read more…] about Principal Component Analysis Guide & Example

Filed Under: Basics Tagged With: analysis example, choosing analysis, conceptual, interpreting results, multivariate

Percent Change: Formula and Calculation Steps

By Jim Frost Leave a Comment

Percent change is the relative difference between an old value and a new value. Positive values represent an increase over time, while negative numbers indicate a reduction.

For example, if the price of a candy bar changes from $1 to $1.10, it’s a 10% increase. [Read more…] about Percent Change: Formula and Calculation Steps

Filed Under: Basics

Simpsons Paradox Explained

By Jim Frost Leave a Comment

What is Simpsons Paradox?

Simpsons Paradox is a statistical phenomenon that occurs when you combine subgroups into one group. The process of aggregating data can cause the apparent direction and strength of the relationship between two variables to change. [Read more…] about Simpsons Paradox Explained

Filed Under: Basics Tagged With: bias sources, conceptual

Weighted Average: Formula & Calculation Examples

By Jim Frost Leave a Comment

What is a Weighted Average?

A weighted average is a type of mean that gives differing importance to the values in a dataset. In contrast, the regular average, or arithmetic mean, gives equal weight to all observations. The weighted average is also known as the weighted mean, and I’ll use those terms interchangeably. [Read more…] about Weighted Average: Formula & Calculation Examples

Filed Under: Basics

Concurrent Validity: Definition, Assessing & Examples

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What is Concurrent Validity?

Concurrent validity is the degree to which assessment scores correlate with a criterion variable when researchers measure both variables at approximately the same time (i.e., concurrently). This method validates an assessment instrument by comparing its scores to another test or variable that researchers had validated previously. [Read more…] about Concurrent Validity: Definition, Assessing & Examples

Filed Under: Basics Tagged With: conceptual, experimental design

Criterion Validity: Definition, Assessing & Examples

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What is Criterion Validity?

Criterion validity (aka criterion related validity) is the degree to which scores from a construct assessment correlate with a manifestation of that construct in the real world (the criterion). [Read more…] about Criterion Validity: Definition, Assessing & Examples

Filed Under: Basics Tagged With: conceptual, experimental design

Predictive Validity: Definition, Assessing & Examples

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What is Predictive Validity?

Predictive validity is the degree to which a test score or construct scale predicts a criterion variable measuring a future outcome, behavior, or performance. Evaluating predictive validity involves assessing the correlation between the pre-test score and the subsequent criterion outcome. [Read more…] about Predictive Validity: Definition, Assessing & Examples

Filed Under: Basics Tagged With: conceptual, experimental design

Retrospective Study: Definition & Examples

By Jim Frost 1 Comment

What is a Retrospective Study?

A retrospective study looks back in time and assesses events that have already occurred. The researchers already know the outcome for each subject when the project starts. Instead of recording data going forward as events happen, these studies use participant recollection and data that were previously recorded for reasons not relating to the project. These studies typically don’t follow patients into the future. [Read more…] about Retrospective Study: Definition & Examples

Filed Under: Basics Tagged With: experimental design

Political Polls and Their Challenges

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Political polls provide crucial information during elections. When done correctly, small samples can predict the outcomes. We’re approaching election time here in the U.S., and campaigns are switching into high gear along with political polls!

Image of a button that supports voting.You’d think that conducting a poll is straightforward. You ask a bunch of people who they’ll vote for and count the results, right? But it’s not quite that simple.

For starters, political polls face the same challenges as other polls. However, a poll taken for political reasons also has some unique issues. When pollsters do everything correctly, the small, sampled group accurately reflects the entire population. The trick is to do it correctly!

In this post, learn how statistical concepts apply to political polls and the extra difficulties they face. [Read more…] about Political Polls and Their Challenges

Filed Under: Basics Tagged With: experimental design, sampling methods

Survivorship Bias: Definition, Examples & Avoiding

By Jim Frost 4 Comments

What is Survivorship Bias?

Survivorship bias, or survivor bias, occurs when you tend to assess successful outcomes and disregard failures. This sampling bias paints a rosier picture of reality than is warranted by skewing the mean results upward. [Read more…] about Survivorship Bias: Definition, Examples & Avoiding

Filed Under: Basics Tagged With: experimental design, sampling methods

Sampling Bias: Definition & Examples

By Jim Frost 2 Comments

What is Sampling Bias?

Sampling bias in statistics occurs when a sample does not accurately represent the characteristics of the population from which it was drawn. When this bias occurs, sample attributes are systematically different from the actual population values. Hence, sampling bias produces a distorted view of the population. Sampling bias often involves human subjects, but it can also apply to samples of objects and animals. Medical researchers refer to this problem as ascertainment bias. [Read more…] about Sampling Bias: Definition & Examples

Filed Under: Basics Tagged With: bias sources, conceptual, experimental design, sampling methods

Purposive Sampling: Definition & Examples

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What is Purposive Sampling?

Purposive sampling is a non-probability method for obtaining a sample where researchers use their expertise to choose specific participants that will help the study meet its goals. These subjects have particular characteristics that the researchers need to evaluate their research question. In other words, the researchers pick the participants “on purpose.” [Read more…] about Purposive Sampling: Definition & Examples

Filed Under: Basics Tagged With: experimental design, sampling methods

Snowball Sampling: Definition and Example

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What is Snowball Sampling?

Snowball sampling is a non-probability method for acquiring a sample that uses participants to recruit additional participants. Researchers call it snowball sampling because if the initial participant recruits two more, and those two recruits each bring in two more, and so on, the number of participants can grow exponentially like a rolling snowball. This method is also known as chain sampling or network sampling. [Read more…] about Snowball Sampling: Definition and Example

Filed Under: Basics Tagged With: conceptual, experimental design, sampling methods

Quota Sampling: Definition & Examples

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What is Quota Sampling?

Quota sampling is a non-random selection of subjects from population subgroups that the researchers define. Researchers use quota sampling when random sampling isn’t feasible, and they want more control over who they select compared to other non-probability methods, such as convenience sampling. [Read more…] about Quota Sampling: Definition & Examples

Filed Under: Basics Tagged With: conceptual, experimental design, sampling methods

Qualitative vs Quantitative Data Differences

By Jim Frost 2 Comments

Qualitative vs quantitative data is a fundamental distinction between two types of information you can gather and analyze statistically. These types of variables seem diametrically opposed, but effective research projects will use them together.

In this post, I’ll explain the difference between qualitative and quantitative data and show effective ways to graph and analyze them for your research. [Read more…] about Qualitative vs Quantitative Data Differences

Filed Under: Basics Tagged With: conceptual, data types

Nominal Data: Definition & Examples

By Jim Frost Leave a Comment

What is Nominal Data?

Nominal data are divided into mutually exclusive categories that do not have a natural order, nor do they provide any quantitative information. The definition of nominal in statistics is “in name only.” This definition indicates how these data consist of category names—all you can do is name the group to which each observation belongs. Nominal and categorical data are synonyms, and I’ll use them interchangeably.

For example, literary genre is a nominal variable that can have the following categories: science fiction, drama, and comedy. [Read more…] about Nominal Data: Definition & Examples

Filed Under: Basics Tagged With: conceptual, data types

Factor Analysis Guide with an Example

By Jim Frost 3 Comments

What is Factor Analysis?

Factor analysis uses the correlation structure amongst observed variables to model a smaller number of unobserved, latent variables known as factors. Researchers use this statistical method when subject-area knowledge suggests that latent factors cause observable variables to covary. Use factor analysis to identify the hidden variables. [Read more…] about Factor Analysis Guide with an Example

Filed Under: Basics Tagged With: analysis example, conceptual, interpreting results, multivariate

Ordinal Data: Definition, Examples & Analysis

By Jim Frost 4 Comments

What is Ordinal Data?

Ordinal data have at least three categories that have a natural rank order. The categories are ranked, but the differences between ranks may not be equal. These data indicate the order of values but not the degree of difference between them. For example, first, second, and third places in a race are ordinal data. You can clearly understand the order of finishes. However, the time difference between first and second place might not be the same as between second and third place. [Read more…] about Ordinal Data: Definition, Examples & Analysis

Filed Under: Basics Tagged With: data types

What is K Means Clustering? With an Example

By Jim Frost 10 Comments

What is K Means Clustering?

The K means clustering algorithm divides a set of n observations into k clusters. Use K means clustering when you don’t have existing group labels and want to assign similar data points to the number of groups you specify (K). [Read more…] about What is K Means Clustering? With an Example

Filed Under: Basics Tagged With: analysis example, interpreting results

Experimental Design: Definition and Types

By Jim Frost Leave a Comment

What is Experimental Design?

An experimental design is a detailed plan for collecting and using data to identify causal relationships. Through careful planning, the design of experiments allows your data collection efforts to have a reasonable chance of detecting effects and testing hypotheses that answer your research questions. [Read more…] about Experimental Design: Definition and Types

Filed Under: Basics Tagged With: experimental design

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