Chapter 2 Research Methods in Organizational Psychology SOP6669 Dr. Steve

1. Chapter 2Research Methods in Organizational PsychologySOP6669Dr. Steve

2. Research Methods Methods: • Experiment / Quasi-Experiment • Questionnaire/Survey • Naturalistic Observation • Case Study • Meta-Analysis

3. Research MethodsExperiment Study conducted in a contrived environment • Benefits: • Provides more safety • Cause and effect relationships • Manipulate I.V. (e.g., leadership style) • Measure D.V. (e.g., task performance) • Control extraneous variables (e.g., experience) • Disadvantages: • Time consuming Quasi-Experiment – not randomized or unable to manipulate IV (e.g., gender)

4. Research MethodsQuestionnaire/Survey Self-report to obtain data on attitudes/behaviors conducted by phone, mail, interviews, electronically • Benefits: • Can collect a large quantity of data • Disadvantages: • Accuracy of reporting • Representativeness of sample • Return rate

5. Research MethodsNaturalistic Observation Observe overt behaviors over time • Systematic sampling at various times • Representative sample • Benefits: • Use to generate hypotheses • Disadvantages: • Experimenter bias • Obtrusiveness • Frequency of behavior occurring

6. Research MethodsCase Study In depth view of past events using interviews and archival records • Benefits: • Detailed account of why particular event occurred • Disadvantages: • Little generalizability

7. Data AnalysisMeta-analysis Meta-analysis – statistical procedure that combines the results of many independent research findings on a single topic • Used to estimate true relationship • Measures effect size of findings • Uses archival data

8. Research StepsStatistical Analysis Descriptive vs. Inferential Statistics • Descriptive stats merely describe data • Frequency • Central tendency • Variability • Inferential stats used to test hypotheses • T-Test • Analysis of variance • Correlation • Regression • Non-parametrics

9. _ • Mean – average: X = ∑X / N • Mean = 72 / 8 = 9 • 2. Median – middle score (when placed in order) • use when outliers exaggerate the mean • Median = 8.5 • 3. Mode – most often occurring score • Mode = 6 Data AnalysisCentral Tendency • example scores = 5, 6, 6, 8, 9, 10, 11, 17 * In a normal distribution, Mean = Median = Mode

10. Data AnalysisVariability • Range - distance between highest and lowest score • (Range = High score – Low score) • Range = 17 – 5 = 12 • Standard Deviation – average distance from the mean • S= Σ(x – x)2 / n – 1 S = (5-9) 2 + (6-9) 2 + (6-9) 2 + (8-9) 2 + (9-9) 2 + (10-9) 2 + (11-9) 2 + (17-9) 2 / 7 S = 3.85

11. Data Analysis Skewed Frequency Distributions Normal or Bell-shaped Distribution Negatively Skewed Distribution Positively Skewed Distribution

12. Data AnalysisCorrelation Correlation ( r ) – Degree of relationship between two variables • Used for prediction • Cannot be used to infer causation • Range from –1 to +1 • Negative r – as one variable increases the other decreases • Positive r – as one variable increases so does the other • Zero r – no relationship between the two variables

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14. Correlation Examples • IQ scores of identical twins: r = +.86 • Phases of the moon & # acts of violence: r = .00 • Economic conditions & # lynchings: r = -.43 • Amount of ice cream sold & # drownings: r = +.60 • Price of rum in Cuba & priests salaries in New England: r = +.38 • Number of cigarettes smoked per day & incidence of lung cancer: r = ???

15. Statistical MethodsRegression Regression Variables (used for prediction) Yi = ß0 + ß1Xi1 + ß2Xi2 (Y = a + b1X1) • Predictor Variable (X) – measure used to predict an outcome (similar to independent variable) • Example: selection test scores, years of experience, education level • Criterion Variable (Y) – outcome to be predicted • Example: work performance, turnover, sales, absenteeism, promotion, etc. • Example: AFOQT scores as predictors of pilot training performance

16. Statistical Pitfalls:Bias • Representative Sampling • Selecting a sample that parallels the population • Might use covariates to account for differences • Statistical Assumptions • ANOVA assumes a normal distribution and independence • Lack of normality is only minor problem, but may want to identify distribution shape and why • Observations may not be independent, may need to aggregate (e.g., class instead of student)

17. Statistical Pitfalls:Errors in Methodology • Statistical Power – probability of detecting a true difference of a particular size • Type I error – falsely reject null hypothesis when a true difference does not exist • Type II error – fail to reject null hypothesis when a true difference does exist • Power affected by • Sample size • Effect size (e.g., Cohen’s D) • Type I error rate selected (alpha) • Variability of sample • (F ratio = var between group / var within group)

18. Statistical Pitfalls:Errors in Methodology • Multiple Comparisons – if you compare enough variables, will find a relationship by chance alone • Bonferroni correction – family-wise adjustment (alpha = .05 / #comparisons) • Replicate • Cross-validate (holdout sample) • Measurement Errors • Reliability: Consistency of Measure • Validity: Measures what it was designed to measure

19. Statistical Pitfalls:Problems with Interpretation • Confusion over significance • P value does not reflect effect size – could have a small effect, but a lot of power • Precision vs. Accuracy • More decimals not necessarily more accurate • Causality • Correlations are not causal, but ANOVA may not be either

20. Statistical Pitfalls:Problems with Interpretation • Graphs • May not provide accurate portrayal of data

21. ResearchCritical Thinking Always think critically about the research you read • Who were the participants in the study? • How strong of a relationship was found? • Was it causal or correlational? • Was it a field study or laboratory study? • How was data collected and analyzed? • Do you agree with the conclusions based on the analyses provided?

22. Ethical Principles of Research • Privacy: • Participants have the right to limit the amount of information they reveal about themselves. If they decide to withdraw from the experiment at any time, they have the right to do so • Confidentiality: • Participants have the right to decide to whom they reveal confidential information. By ensuring confidentiality, researchers may be able to obtain more honest responses • Protection from Deception: • Deception can only be used if the value of the research must outweigh the harm imposed on participants and the phenomenon cannot be measured any other way