1 work in the 21 st century chapter 2 methods and statistics in i-o psychology
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Work in the 21st CenturyChapter 2
Methods and Statistics
in I-O Psychology
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Module 2.1: Science
• What is science?– Approach that involves the understanding, prediction,
and control of some phenomenon of interest• 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
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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
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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
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Role of Science in Society
• Expert witnesses in a lawsuit– Permitted to voice opinions about
organizational practices
– Often a role assumed by I-O psychologists
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Module 2.1 (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
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Module 2.2: Common Research Designs in I-O Psychology
Table 2.1 Common Research Designs in I-O Psychology
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Research Designs (cont'd)
– Experimental• Random assignment of participants to conditions• Conducted in a laboratory or the workplace
– Non-experimental• Does not include manipulation or assignment to different conditions
– 2 common designs:
• Observational design: Observes and records behavior
• Survey/Questionnaire design (most common)
– Quasi-experimental• Non-random assignment of participants to conditions
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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
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Methods of Data Collection: Qualitative & Quantitative Research
• Qualitative methods– Include procedures like observation, interview, case
study, & analysis of written documents– Generally produce flow diagrams & narrative
descriptions of events/processes
• Quantitative methods– Rely on tests, rating scales, and physiological
measures– Yield numerical results
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Quantitative & Qualitative Research (cont’d)
• Not mutually exclusive
• Triangulation– Examining converging information from
different sources (qualitative and quantitative research).
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Generalizability in Research
Generalizability:
• Application of results from one study or sample to other participants or situations
• The more areas a study includes, the greater its generalizability
• Every time a compromise is made, the generalizability of results is reduced
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Sampling Domains for I-O Research
Figure 2.1: Sampling Domainsfor I-O Research
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Control in Research
• Experimental control– Eliminates influences that could make results
less reliable or harder to interpret
• Statistical control– Statistical techniques used to control for the
influence of certain variables
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Ethical Behavior inI-O Psychology
• Ethical standards of the APA
• SIOP book of 61 cases (Lowman, 1998)– Cases illustrate ethical issues that are likely to
arise in I-O psychology
• Joel Lefkowitz (2003) published a recent book on values and ethics in I-O psychology
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Table 2.2 Potential Roles Available to the I-O Psychologist and Other HR Managers with Respect to Ethical Problems
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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
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Describing a Score Distribution
• Measures of central tendency
• Mean • Mode• Median
Ryan McVay/Getty Images
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Describing Score Distribution (cont'd)
• Variability– Standard deviation
• Lopsidedness or skew– Mean is affected by high or low
scores, median is not– Mean pulls in direction of skew
Ryan McVay/Getty Images
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Descriptive Statistics:Two Score Distributions (N = 30)
Figure 2.2 Two Score Distribution (N = 30)
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Two Score Distributions (N = 10)
Figure 2.3. Two Score Distributions (N = 10)
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Inferential Statistics
• Aid in testing hypotheses & making inferences from sample data to a larger sample/population
• Include t-test, F-test, chi-square test
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Statistical Significance
• Defined in terms of a probability statement
• Threshold for significance is often set at .05 or lower
• Significance refers only to confidence that result is NOT due to chance, not strength of an association or importance of results.
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Statistical Power
• Likelihood of finding statistically significant difference when true difference exists
• The smaller the sample size, the lower the power to detect a true or real difference
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Concept of Correlation
Positive Linear Correlation
Figure 2.4Correlation betweenTest Scores andTraining Grades
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Concept of Correlation (cont'd)
• Scatterplot– Displays correlational relationship between 2
variables
• Regression– Straight line that best “fits” the scatterplot and
describes the relationship between the variables in the graph
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Correlation Coefficient
• Statistic or measure of association
• Reflects magnitude (numerical value) & direction (+ or –) of relationship between 2 variables
• Ranges from 0.00 and 1.00
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Correlation Coefficient
• Positive correlation → As one variable increases, other variable also increases & vice versa
• Negative correlation → As one variable increases, other variable decreases & vice versa
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Scatterplots of Various Degrees of Correlation
Figure 2.6. Scatterplots of Various Degrees of Correlation
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Curvilinear Relationship
• If correlation coefficient is .00, one cannot conclude that there is no association between variables
• A curvilinear relationship might better describe the association
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Curvilinear Correlation
Figure 2.7An Example ofa CurvilinearRelationship
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Multiple Correlation
• Multiple correlation coefficient– Overall linear association between
several variables & a single outcome variable
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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 typically the most influential
statistical artifact
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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
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High and LowTest-Retest Reliability
Figure 2.8Examples of High and Low Test-Retest Reliability: Score Distributions of Individuals Tested on Two Different Occasions
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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– Assesses how consistently items of a test
measure a single construct
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Reliability (cont'd)
• Inter-rater reliability– Can calculate various statistical indices to
show level of agreement among raters• Values in the range of .70 to .80 represent
reasonable reliability
• Generalizability theory• Simultaneously considers all types of error
in reliability estimates
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Validity
• Whether measurements taken accurately & completely represent what is to be measured
• Predictor– Test chosen or developed to assess identified abilities or
other characteristics (KSAOs)
• Criterion– Outcome variable describing important performance
domain
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Figure 2.9: Validation Process from Conceptual and Operational Levels
Figure 2.9
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Criterion-Related Validity
• Correlate a test score (predictor) with a performance measure; resulting correlation often called a validity coefficient
• Predictive validity design– Time lag between collection of test data &
criterion data– Test often administered to job applicants
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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
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Content-Related Validity
• Demonstrates that content of selection procedure represents adequate sample of important work behaviors & activities or worker KSAOs defined by job analysis
• I-O Psychologists can use incumbents/SMEs to gather content validity evidence
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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, extraversion, and integrity
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A Model for Construct Validity
Figure 2.10.A Model forConstruct Validity
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Construct Validity Model of Strength and Endurance Physical Factors
Figure 2.11