Data Analysis in Industrial/Organizational Psychology

Lesson Transcript
Instructor: Daniel Murdock

Daniel has taught Public Health at the graduate level and has a Ph.D. in Behavioral Sciences & Health Education.

In this lesson, we'll examine some of the most commonly used statistical methods in industrial-organizational psychology, including how they are used and how to differentiate them from one another.

Variables in I/O Psychology Research

Data analysis is an integral part of the research process in industrial and organizational psychology. There are many different kinds of statistical methods that are used in the field. So many, in fact, that sometimes it can feel a little overwhelming to try to differentiate between them.

However, one way we can differentiate between statistical methods is by looking at the specific types of variables involved in each method. To do this, we first need to define some basic terminology when it comes to variables.

A predictor variable is a variable that is used to predict another variable in statistical analyses. It is sometimes referred to as an independent variable when it is manipulated in an experiment, rather than just measured. An outcome variable, or dependent variable, is a variable whose value depends on the predictor variable.

Imagine that a business manager, Sheila, gives her employees a survey. The survey asks employees to rate their job satisfaction as low, medium, or high. Sheila wants to understand whether job satisfaction is related to employee age. In this example, employee age is a predictor variable, and job satisfaction is an outcome variable. In general, all variables are either measured on a continuous scale, or they measure categorical or discrete characteristics. In our example, age is a continuous variable and job satisfaction is a categorical variable.

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Models & Observed Predictor Variables

The most frequently used statistical methods in I/O psychology involve observed, or directly measured, predictor variables, like the age variable in our example. Let's take a look at a few of them.

Analysis of variance is a statistical method that we can use to analyze the relationship between a categorical predictor variable and a continuous outcome variable. Analysis of variance provides a statistical test of whether or not the means of a continuous outcome variable are equal across different groups, or categories.

We can use multiple regression to analyze the relationship between two or more predictor variables and a continuous outcome variable. Unlike analysis of variance, multiple regression can be used with either continuous or categorical predictor variables.

Logistic regression is another type of regression model that we can use when our outcome variable is categorical. Logistic regression uses one or more predictor variables, which can be either continuous or categorical.

Models & Unobserved Predictor Variables

Sometimes we want to know about variables that we cannot see or directly measure. For example, imagine that our business manager, Sheila, wants to understand whether her employees' job satisfaction is related to their commitment to the organization. Organizational commitment is somewhat of an abstract concept that has several different dimensions to it, which makes it difficult to measure directly.

In statistics, latent variables are variables that are measured indirectly using other observed variables. Latent variables can be inferred through advanced statistical modeling. Factor analysis is a statistical method that helps us find underlying (latent) constructs that describe variability among observed, correlated variables.

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