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Chapter 2

Chapter 2. Methods and Statistics in I-O Psychology. Royalty-Free/CORBIS. Module 1: Science. What is science? Science has common methods Science is a logical approach to investigation Based on a theory, hypothesis or basic interest Science depends on data

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Chapter 2

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  1. Chapter 2 Methods and Statistics in I-O Psychology Royalty-Free/CORBIS

  2. Module 1: Science • What is science? • Science has common methods • Science is a logical approach to investigation • Based on a theory, hypothesis or basic interest • Science depends on data • Gathered in a laboratory or the field

  3. Common Methods (cont'd) • Research must be communicable, open, & public • Research published in journals, reports, or books 1) Methods of data collection described 2) Data reported 3) Analyses displayed for examination 4) Conclusions presented

  4. Common Methods (cont'd) • Scientists set out to disprove theories or hypotheses • Goal: Eliminate all plausible explanations except one • Scientists are objective • Expectation that researchers will be objective & not influenced by biases or prejudices

  5. Role of Science in Society • Expert witnesses in a lawsuit • Permitted to voice opinions about practices • Often a role assumed by I-O psychologists

  6. Daubert Challenge • Challenging testimony of an expert on the grounds it is not scientifically credible • Daubert v. Merrill-Dow, 1993 • Resulted in introduction of a method for distinguishing between “legitimate science” & “junk science”

  7. Scientific Testimony in Court • Theories presented in court must: • Be recognized by particular scientific area as worthy of attention • Be peer reviewed or subjected to scientific scrutiny • Have a known “error rate” • Be replicable or testable by other scientists

  8. Module 1 (cont'd) • Why do I-O psychologists engage in research? • Better equip HR professionals in making decisions in organizations • Provide an aspect of predictability to HR decisions

  9. Module 2: Research • Research design • Experimental • Random assignment of participants to conditions • Conducted in a laboratory or the field • Quasi-experimental • Non-random assignment of participants to conditions

  10. Research Design (cont'd) • Non-Experimental • Doesn’t include “treatment” or assignment to different conditions • 2 common designs: • Observational design • Survey design

  11. Methods of Data Collection • Quantitative methods • Rely on tests, rating scales, questionnaires, & physiological measures • Yield results in terms of numbers C. Borland/PhotoLink/Getty Images

  12. Methods of Data Collection • Qualitative methods • Include procedures like observation, interview, case study, & analysis of written documents • Generally produce flow diagrams & narrative descriptions of events/processes

  13. Quantitative & Qualitative Research • Not mutually exclusive • Triangulation, (Rogelberg & Brooks, 2002) • Examining converging information from different sources (qualitative and quantitative research).

  14. K Lewin • B = f (p*e) • Behavior is a function of • Person X environmental influences

  15. Experimental v. Corr research • “I” side: focus on Individual differences • Person attributes: • E.g. Personality, behaviors, cognitive ability • “O” side: focus on Environmental influences • Situation variables: • E.g. work conditions, leadership style, pay for performance

  16. I v. O • Which is most likely to use • Experimental designs? • Correlational designs? • Why?

  17. Generalizability in Research • Application of results from one study or sample to other participants or situations • Benefit of using theory • Every time a compromise is made, the generalizability of results is reduced

  18. Sampling Domains for I-O Research Figure 2.1: Sampling Domains for I-O Research

  19. Observational Unit • Worker • Team • Department • Organization • Industry • Others?

  20. Measurement Unit(one of something) • Identify a measurement unit for: • Worker’s performance score • Years of experience • Absenteeism • Motivation • Sales performance • Cognitive ability

  21. Control in Research • Experimental control • Influences that make results less reliable or harder to interpret are eliminated • Statistical control • Statistical techniques used to control for influences of certain variables

  22. Ethics • Ethical standards of the APA • Collection of 61 cases endorsed by SIOP • Illustrates ethical issues likely to arise in I-O psychology (Lowman, 1985a,1998)

  23. Module 3: Data Analysis • Descriptive statistics • Summarize, organize, describe sample of data Frequency Distribution: • Horizontal axis = Scores running low to high • Vertical axis = Indicates frequency of occurrence

  24. Describing a Score Distribution • Measures of central tendency • Mean • Mode • Median Ryan McVay/Getty Images

  25. Describing Score Distribution (cont'd) • Variability • Standard deviation • Lopsidedness or skew Ryan McVay/Getty Images

  26. Descriptive Statistics:Two Score Distributions (N = 30) Figure 2.2 Two Score Distribution (N=30)

  27. Two Score Distributions (N = 10) Figure 2.3

  28. Inferential Statistics • Aid in testing hypotheses & making inferences from sample data to a larger sample/population • Include t-test, F-test, chi-square test

  29. Statistical Significance • Defined in terms of a probability statement • Threshold for significance is often set at .05 or lower • p < .05 (likelihood of this effect size would occur less than 5 times in a hundred)

  30. Statistical Power • Likelihood of finding statistically significant difference when true difference exists • Smaller the sample size, lower the power to detect a true or real difference

  31. Concept of Correlation Positive Linear Correlation Figure 2.4 Correlation between Test Scores and Training Grades

  32. Concept of Correlation (cont'd) • Scatterplot • Displays correlational relationship between 2 variables • Regression • Straight line that best fits the scatterplot

  33. Correlation Coefficient • Statistic or measure of association • Reflects magnitude (numerical value) & direction (+ or –) of relationship between 2 variables

  34. Correlation Coefficient • Positive correlation → High values of one variable are associated with high values in the other variable (& vice versa) • Negative correlation → High values of one variable are associated with low values in the other (& vice versa)

  35. Figure 2.6: Scatterplots of Various Degrees of Correlation

  36. Curvilinear Relationship • Although correlation coefficient might be .00, it can’t be concluded that there is no association between variables • A curvilinear relationship might better describe the association (eta η) • SPSS can provide η with F test

  37. Curvilinear Correlation Figure 2.7 An Example of a Curvilinear Relationship

  38. Multiple Correlation • Multiple correlation coefficient • Overall linear association between several variables & a single outcome variable (R) • R2 = Proportion of variance in DV (outcome) accounted for all preditors (several vars)

  39. Meta-Analysis • Statistical method for combining results from many studies to draw a general conclusion • Statistical artifacts • Characteristics of a particular study that distort the results • Sample size is most influential

  40. Module 4: Interpretation • Reliability • Consistency or stability of a measure • Test-retest reliability • Calculated by correlating measurements taken at Time 1 with measurements taken at Time 2

  41. High and LowTest-Retest Reliability Figure 2.8 Examples of High and Low Test-Retest Reliability: Score Distributions of Individuals Tested on Two Different Occasions

  42. Reliability (cont'd) • Equivalent forms reliability • Calculated by correlating measurements from a sample of individuals who complete 2 different forms of same test • Internal consistency (Cronbach alpha α) • Assesses how consistently items of a test measure a single construct

  43. Reliability (cont'd) • Inter-rater reliability • Can calculate various statistical indices to show level of agreement among raters • Intraclass correlation ICC • Rwg • Generalizability theory • Simultaneously considers all types of error in reliability estimates

  44. Validity • Whether measurements taken accurately & completely represent what is to be measured • Predictor • Test chosen or developed to assess identified abilities • Criterion • Outcome variable describing important aspects or demands of the job

  45. Figure 2.9: Validation Process from Conceptual and Operational Levels Figure 2.9

  46. Criterion-Related Validity • Correlate a test score with a performance measure (validity coefficient) • Predictive validity design • Time lag between collection of test data & criterion data • Test often administered to job applicants

  47. Criterion-Related Validity (cont'd) • Concurrent validity design • No time lag between collection of test data & criterion data • Test administered to current employees, performance measures collected at same time • Disadvantage: No data about those not employed by the organization

  48. Content-Related Validity • Demonstrates that content of selection procedure represents adequate sample of important work behaviors & activities or worker KSAOs defined by job analysis

  49. Construct-Related Validity • Investigators gather evidence to support decisions or inferences about psychological constructs • Construct - concept or characteristic that a predictor is intended to measure; examples include intelligence and extraversion

  50. A Model for Construct Validity Figure 2.10 A Model for Construct Validity

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