Statistics By Jim

Making statistics intuitive

data types

Data Collection Methods: Step-By-Step Guide with Examples

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What Are Data Collection Methods?

Data collection methods are organized processes for gathering observations and measurements to accurately answer research questions. Whether you study the environment, health, public opinion, or medicine, selecting the appropriate data collection methods ensures that your results are accurate and meaningful. For example, in environmental research, sound methodology helps scientists uncover valuable insights about ecosystems, pollution, wildlife, and climate change. [Read more…] about Data Collection Methods: Step-By-Step Guide with Examples

Data Aggregation: Strengths & Weaknesses of Aggregated Data

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What is Data Aggregation?

Data aggregation is a crucial process that involves collecting data and summarizing it in a concise form. This method transforms atomic data rows—sourced from diverse origins—into comprehensive totals or summary statistics. Aggregated data, typically housed in data warehouses, enhances analytical capabilities and significantly speeds up querying large datasets. [Read more…] about Data Aggregation: Strengths & Weaknesses of Aggregated Data

Likert Scale: Survey Use & Examples

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What is a Likert Scale?

The Likert scale is a well-loved tool in the realm of survey research. Named after psychologist Rensis Likert, it measures attitudes or feelings towards a topic on a continuum, typically from one extreme to the other. The scale provides quantitative data about qualitative aspects, such as attitudes, satisfaction, agreement, or likelihood. [Read more…] about Likert Scale: Survey Use & Examples

Box Plot Explained with Examples

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What is a Box Plot?

A box plot, sometimes called a box and whisker plot, provides a snapshot of your continuous variable’s distribution. They particularly excel at comparing the distributions of groups within your dataset. A box plot displays a ton of information in a simplified format. Analysts frequently use them during exploratory data analysis because they display your dataset’s central tendency, skewness, and spread, as well as highlighting outliers. [Read more…] about Box Plot Explained with Examples

Covariates: Definition & Uses

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What is a Covariate?

Covariates are continuous independent variables (or predictors) in a regression or ANOVA model. These variables can explain some of the variability in the dependent variable.

That definition of covariates is simple enough. However, the usage of the term has changed over time. Consequently, analysts can have drastically different contexts in mind when discussing covariates. [Read more…] about Covariates: Definition & Uses

Qualitative vs Quantitative Data Differences

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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

Nominal Data: Definition & Examples

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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

Ordinal Data: Definition, Examples & Analysis

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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

Nominal, Ordinal, Interval, and Ratio Scales

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The nominal, ordinal, interval, and ratio scales are levels of measurement in statistics. These scales are broad classifications describing the type of information recorded within the values of your variables. Variables take on different values in your data set. For example, you can measure height, gender, and class ranking. Each of these variables uses a distinct level of measurement. [Read more…] about Nominal, Ordinal, Interval, and Ratio Scales

Discrete vs. Continuous Data: Differences & Examples

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Discrete vs continuous data are two broad categories of numeric variables. Numeric variables represent characteristics that you can express as numbers rather than descriptive language. These are quantitative data.

When you have a numeric variable, you need to determine whether it is discrete or continuous.

In broad strokes, the critical factor is the following:

  • You count discrete data.
  • You measure continuous data.

[Read more…] about Discrete vs. Continuous Data: Differences & Examples

Pareto Chart: Making, Reading & Examples

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What is a Pareto Chart?

A Pareto chart is a specialized bar chart that displays categories in descending order and a line chart representing the cumulative amount. The chart effectively communicates the categories that contribute the most to the total. Frequently, quality analysts use Pareto charts to identify the most common types of defects or other problems.

Learn how to use and read Pareto charts and understand the Pareto principle and the 80/20 rule that are behind it. I’ll also show you how to create them using Excel. [Read more…] about Pareto Chart: Making, Reading & Examples

Pie Charts: Using, Examples, and Interpreting

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Use pie charts to compare the sizes of categories to the entire dataset. To create a pie chart, you must have a categorical variable that divides your data into groups. These graphs consist of a circle (i.e., the pie) with slices representing subgroups. The size of each slice is proportional to the relative size of each category out of the whole. [Read more…] about Pie Charts: Using, Examples, and Interpreting

Bar Charts: Using, Examples, and Interpreting

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Use bar charts to compare categories when you have at least one categorical or discrete variable. Each bar represents a summary value for one discrete level, where longer bars indicate higher values. Types of summary values include counts, sums, means, and standard deviations. Bar charts are also known as bar graphs. [Read more…] about Bar Charts: Using, Examples, and Interpreting

Line Charts: Using, Examples, and Interpreting

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Use line charts to display a series of data points that are connected by lines. Analysts use line charts to emphasize changes in a metric on the vertical Y-axis by another variable on the horizontal X-axis. Often, the X-axis reflects time, but not always. Line charts are also known as line plots. [Read more…] about Line Charts: Using, Examples, and Interpreting

Dot Plots: Using, Examples, and Interpreting

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Use dot plots to display the distribution of your sample data when you have continuous variables. These graphs stack dots along the horizontal X-axis to represent the frequencies of different values. More dots indicate greater frequency. Each dot represents a set number of observations. [Read more…] about Dot Plots: Using, Examples, and Interpreting

Empirical Cumulative Distribution Function (CDF) Plots

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Use an empirical cumulative distribution function plot to display the data points in your sample from lowest to highest against their percentiles. These graphs require continuous variables and allow you to derive percentiles and other distribution properties. This function is also known as the empirical CDF or ECDF. [Read more…] about Empirical Cumulative Distribution Function (CDF) Plots

Contour Plots: Using, Examples, and Interpreting

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Use contour plots to display the relationship between two independent variables and a dependent variable. The graph shows values of the Z variable for combinations of the X and Y variables. The X and Y values are displayed along the X and Y-axes, while contour lines and bands represent the Z value. The contour lines connect combinations of the X and Y variables that produce equal values of Z. [Read more…] about Contour Plots: Using, Examples, and Interpreting

Spearman’s Correlation Explained

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Spearman’s correlation in statistics is a nonparametric alternative to Pearson’s correlation. Use Spearman’s correlation for data that follow curvilinear, monotonic relationships and for ordinal data. Statisticians also refer to Spearman’s rank order correlation coefficient as Spearman’s ρ (rho).

In this post, I’ll cover what all that means so you know when and why you should use Spearman’s correlation instead of the more common Pearson’s correlation. [Read more…] about Spearman’s Correlation Explained