Research in Industrial/Organizational Psychology

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  • 0:04 Generalities and Specifics
  • 0:58 Building on the Foundation
  • 2:28 Samples and How to…
  • 4:04 Here in the Real World
  • 5:27 Lesson Summary
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Lesson Transcript
Instructor: Lisa Millraney

Lisa has 27 years of experience treating speech, language, memory and swallowing disorders. She has a master's degree in speech pathology from Vanderbilt University.

In this lesson, you'll learn how to apply principles of experimental research in the specific area of industrial/organizational psychology. We'll discuss how to design a study that utilizes correct sampling, reliability, validity, data collection and analysis, and how to draw conclusions borne out by our data.

Generalities and the Specifics

Experimental research procedures differ somewhat depending on the field of study. Imagine how an anthropologist might study rainforest tribal culture. Now imagine a speech pathologist's study on treatment approaches to improve swallowing in stroke patients. These approaches would be quite different. However, the basic principles and components are the same.

In this lesson, we will talk about how those principles are applied in designing a study in industrial/organizational psychology (I/OP). First, let's look at the research process. In its simplest form, it comprises five steps:

  1. Define the problem you are trying to solve or question that needs answering
  2. Design a study to address it
  3. Collect data through measuring the variables you identify as being relevant
  4. Analyze your data
  5. Draw a conclusion

Building on the Foundation

The research process is the base on which a study is built, with many other elements forming the building blocks. They include reliability (results can be reproduced), validity (results are believable), sampling techniques, statistical analysis, and cause/effect relations.

Numerous types of studies can be conducted in industrial/organizational psychology. In this lesson, we'll focus on true experiments and quasi-experiments. A true experiment is conducted in a controlled environment. Independent variables are manipulated, and dependent variables are measured for changes.

A quasi-experiment assesses the same categories of variables but is less rigid. It may not be completely random. Some variables may not be managed. It is more commonly used in I/OP because of other people, like managers, having influence over things the researcher does not.

Reliability and validity are established through multiple trials and comparing data to previous records. Internal validity is how far we can go in saying two variables have a cause and effect relationship. External validity is how far we can go in generalizing findings outside our research setting. Similarly, reliability is divided into internal reliability (a measure is consistent within itself) and external reliability (a measure is consistent from one use to the next).

A valid test or measurement tool is always reliable, but a reliable one isn't necessarily valid; it may yield the same results every time, but if it isn't designed well, it may not be measuring what it is intended to.

Samples and How to Analyze Them

In choosing sampling techniques, we may use a probability sample (random, by chance) or a nonprobability sample (non-random, such as volunteers). Random samples are generally preferable. The sample size is important, as a small sample can't always be generalized to the larger population, so external validity may be questionable.

Two types of data may be collected in various ways. Quantitative data are numerical, like surveys or observations, and qualitative data are descriptive, like interviews or focus groups. Once data is collected, it must be analyzed objectively.

We can use descriptive analysis, which describes what's observed, and inferential analysis, which goes further toward detecting cause and effect. A commonly used form of data analysis is the t-test. It compares the average from two groups to determine if a significant difference is present. In other lessons, we learned about t-tests and they give a full description and explain how to perform one.

Two other means often used to analyze data are ANOVA and ANCOVA. ANOVA, or analysis of variance, compares multiple groups' means to look for a statistically significant difference. ANCOVA, or analysis of co-variance, is its more complex cousin. ANOVA usually looks at one factor that distinguishes the groups, such as education level, while ANCOVA looks at one factor and one independent variable, such as education level and job experience.

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