Limit collection of categorical data Age – – – – – & Above Income ,000 10,001 – 25,000 25,001 – 35,000 35,001 – 50,000 50,001 – 75,000 75,001 – 100, ,000 & Above Age in Years: _______ Income: ____________ ~ I-O Research ~ Measurement

~ I-O Research ~ Measurement (cont.) Yes _____ No _____ _____ _____ _____ _____ _____ Highly Disagree Agree Limit collection of dichotomous data

Data Analysis ~ I-O Research ~ Limit use of ANOVA approach High Low Leads to less statistical power, effect sizes, and reliability Best to use some form of regression analysis

~ I-O Research ~ Measurement (cont.) Restrict possibility of missing data Scale Questions Missing Computed score for scale or subscales containing questions #5 and #48 will also be missing

Absolute versus Relative (Comparative) Assessments Absolute: “How many hours of TV did you watch last year? “Is this drink sweet?” or “How sweet is this drink?” Relative: Did you watch TV more hours than you spent reading the local paper? “Which of these five drinks is the sweetest?” Generally, it is easier for people to make relative vs. absolute judgments (more accuracy and consistency exists) People rarely make absolute assessments in everyday activities (most choices are basically comparative) Limitation with relative assessments and the instances when absolute judgments are vital ---

Scales of Measurement 1) Nominal -- Indicates categories, classification (e.g., gender, race, yes/no) Stats: N of cases (e.g., chi-square), mode 2)Ordinal -- Indicates relative position; greater than, less than (e.g., rank ordering percentiles) Stats: Median, percentiles, order statistics 3) Interval -- Indicates an absolute judgment on an attribute (equal intervals) No absolute zero point (a score of 80 is not twice as high as a score of 40) Stats: Mean, variance, correlation 4) Ratio -- Possesses an absolute zero point (e.g., number of units produced) All numerical operations can be performed (add, subtract, multiply, divide) 1 st 2 nd 3rd Does not indicate how much of an attribute one possesses (e.g., all may be low or all may be high) Does not indicate how far apart the people are with respect to the attribute

~ I-O Research ~ Interesting fact: Substantial amount of I-O studies are non-experimental (about 50%) Overall Point: Best for research to be driven by theories and problem-solving approaches not by methodology/statistics Much research efforts in I-O focus on rather trivial questions that can be studied with “fancy” techniques Bulk of research has limited applied significance

Safety in work vehicles: A multilevel study linking safety values and individual predictors to work-related driving crashes. Beyond change management: A multilevel investigation of contextual and personal influences on employees' commitment to change. The development of collective efficacy in teams: A multilevel and longitudinal perspective. Some Recent Articles in the Journal of Applied Psychology Study Variables Multi-level analysis (or hierarchical linear modeling; HLM). Allows for the assessment of variance in outcome variables to be investigated at multiple, hierarchical levels. Related analyses include structural equation modeling and latent class modeling Geographic location (region, country) Work team (or job category) Employee ~ I-O Research Trends ~

Predicting workplace aggression: A meta-analysis. The good, the bad, and the unknown about telecommuting: Meta- analysis of psychological mediators and individual consequences. Some Recent Articles in the Journal of Applied Psychology (cont.) Meta-analysis: Statistical approach that allows the combination of results from multiple independent studies on a given topic. It allows a better estimate of the true “effect size,” giving more “weight” to larger studies. ~ I-O Research Trends ~

Some Recent Articles in the Journal of Applied Psychology (cont.) Moderating variable (or 3 rd variable): A variable that affects the strength and/or direction of the relationship between two variables. Mediating variable: Variable that accounts for (explains) the relationship between two variables Job enrichment strategies Job Satisfaction Age (as moderator) (The relationship may be stronger for older individuals) Job enrichment strategies Job Satisfaction Growth need strength (as mediator) (When growth need strength is considered the relationship between job enrichment and satisfaction goes away) ~ I-O Research Trends ~

Data Analysis Usage: Approximately 10% of papers published in Journal of Applied Psychology employ factor analysis ✖ Avoid: Varimax rotation Principle components analysis Automatically keep factors with eigenvalues greater than 1.0 Use: Iterative principle factors (least squares, or maximum likelihood) Oblique rotation (no assumption of factor independence) ~ I-O Research ~ Factor Analysis --- ✔

~ I-O Research (cont.) ~ Suggestions 1)More use of “archival” data (many are of high quality with large sample sizes; e.g., government statistics on unemployment rates) 2)Longitudinal studies (assessment of change over time) 3) Report confidence intervals and effect sizes in addition to significance levels (e.g., p <.01)

Common Research Designs Used in I-O One-Shot Case Study X O X = Treatment or Intervention O = Observation or Collection of Data One-Group Pretest-Posttest Design O X O 13

Math Pretest Math Posttest English Pretest English Posttest week training program between tests Did the program work to increase scores? 14

% increase MathEnglish “Lying” with numbers 15

An organization reports that accidents have decrease substantially since they began a drug testing program. In 1995, the year before drug testing, the number of accidents was 50. In 1996, the year testing began, the amount dropped to 40. In 1997, the year after drug testing the number of accident dropped to 29. What do you make of this? 1995Drug Testing * * * 16

Given the illustration below, now what do you make of the effectiveness of the drug testing program? * * * * * * * * * 17