1. Organizational Psychology: A Scientist-Practitioner Approach Jex, S. M., & Britt, T. W. (2014) Prepared by: Christopher J. L. Cunningham, PhD University of Tennessee at Chattanooga Kelsey-Jo Ritter Bowling Green State University Kristen S. Jennings Clemson University 8

2. Chapter 2: Research Methods and Statistics 9

3. Part 1: Research Methods: Observational Methods Focus on behavior Multiple methods exist –Simple –Participant Reactivity Pros and cons –Comment 2.1 10

4. Archival Data and Surveys Archival research uses previously collected data Common in organizations because data are available –Employee records Advantages and disadvantages 11

5. Survey Research Survey research is the most common form of data collection in organizations Major format/administration considerations Proper sampling is critical –Using probability to help Analyses should allow you to describe results and give meaningful feedback 12

6. Experimentation Highly controlled study of cause-and-effect relationships Core features: 1.An IV that can be manipulated 2.A DV that can be measured 3.Random assignment of participants to conditions 4.Control over the variables by the experimenter 13

7. Quasi-Experimentation Lacks one or more features of a true experiment Practicality within organizations Importance of statistical control Importance of ruling out alternative explanations 14

8. Computer Simulations/Computational Modeling Using mathematical models Monte Carlo simulations 15

9. Choosing the Best Method There are advantages and disadvantages to each method To make the best possible choice, be familiar with each technique: –Figure 2.2: Comparison of advantages and disadvantages for each method When possible, use multiple methods –Comment 2.4 16

10. Self-Report Methods Widely used, poorly understood Assumptions: 1.Respondents know the information we request 2.Truthful responses will be given Common method variance Practical guidelines 17

11. Generalizing Lab Findings Can research findings from the lab be applied in the real world? Question of realism –Problems with using student samples –Influence of limited contexts Good research design and sampling are must-haves! 18

12. Gaining Access to Organizations Network Sell the project and its benefits Negotiate details up front Comment 2.5: Real examples of gaining access to organizations 19

13. Ethics in Data Collection Informed consent Voluntary participation Confidentiality 20

14. Cross-Cultural Research Many benefits Many challenges –Translation (of text and meaning) –Proper sampling –Cultural idiosyncrasies 21

15. Part 2: Statistical Methods 22

16. Descriptive Statistics Looking for general trends/themes Common statistics (multiple forms of each): –Central tendency (Figure 2.3) –Measures of dispersion –Reliability Good to keep analyses as simple as possible –Comment 2.6 23

17. Tests of Mean Differences Testing specific hypotheses (expected differences) Most common statistical tests for differences: –t-test –Analysis of variance (F-test) 24

18. Correlation and Regression For identifying covariation among variables Common statistics: –Pearson product-moment correlation coefficient, r –Multiple linear regression 25

19. Meta-Analysis Quantitative summary of existing research findings –Averaging effect sizes while controlling for statistical artifacts (sample size, range restriction) Common statistics involved: –Overall effect size estimate –Variation in effect sizes after controlling for artifacts 26

20. Statistical Power in Research Is your test sensitive enough to detect meaningful effects? Limiting factors –Sample size –Effect size –Alpha level Balance Type I, Type II error (Comment 2.6) –Measurement error 27

21. Detecting Moderator Variables Moderator changes relationship between two other variables Detection: multiple regression analysis –Figure 2.3: Moderated relationship –Comment 2.7: The elusive moderator effect 28

22. Causal Modeling Simultaneously testing a set of predicted relationships among several variables Common techniques: –Path analysis (Figure 2.4) –Structural equation modeling (Figure 2.5) May be overused when simpler techniques would suffice 29

23. Multiple Levels and Aggregation Considering variables and their relationships across multiple levels Aggregation groups multiple values together into a single unit –Individual ratings of group morale  group- level morale Conceptual and practical challenges 30

24. Aggregation Appropriate when: 1.Theoretically justified aggregate variable 2.Methodological justification for aggregate unit 3.Measures that focus on the aggregate unit 4.Statistical justification (agreement among ratings from members within a group) Requires advance statistical analyses 31