I-O Psychology Notes 1
I-O Psychology Notes 1 PSYC 2544
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This 13 page Class Notes was uploaded by Freddi Marsillo on Monday February 1, 2016. The Class Notes belongs to PSYC 2544 at George Washington University taught by Blacksmith, N in Fall 2015. Since its upload, it has received 82 views. For similar materials see Industrial/Organizational Psychology in Psychlogy at George Washington University.
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Date Created: 02/01/16
Industrial/Organizational Psychology Notes – Intro & History/Statistics/Research Methods 02/02/2016 00.50.00 What is Industrial and Organizational Psychology? The application of psychological principles, theory, and research to the work setting The importance of I-O psychology emphasizes the importance of work in people’s lives Evidence-Based Consulting I-O psychologists are focused on making evidence-based decisions in their work in organizations This includes using a decision-making process that combines critical thinking with use of the best available scientific evidence I-O psychologists are well-positioned to develop and utilize evidence-based practices as they have adopted the scientist- practitioner model to guide the field Where I-O Psychologists are Employed Statistics in I-O Psychology Qualitative and quantitative research Theory testing o Significance o Hypotheses Meta-analysis Common Areas of Concentration for I-O Psychologists Recruitment and organizational attraction Employee selection Individual differences Training Onboarding and P-O Fit – integrating individuals into culture; interaction Performance management Leadership and management Teams Motivation Job attitudes Psychometrics Org culture Research methods Brief History – Changes in the Workplace since 1980 Personal computing Telecommuting and virtual teams Videoconferencing Providing a service vs. manufacturing “goods” Nature of work is more fluid Teams vs. the individual Family-friendly workplaces Greater diversity Methods and Statistics in I-O Psychology What is science? An approach that involves the understanding, prediction, and control of some phenomenon of interest Science has common methods Science is a logical approach to investigation o Based on a theory, hypothesis, or basic interest Science depends on data (gathered in a laboratory or the field) Steps of Research Statement of problem Design of research study use theory Analysis of data Interpretation of data Publicize Feedback loop next research question Goals of Science Scientists set out to disprove theories or hypotheses Goal: Eliminate all plausible explanations except one You can never “prove” a theory Publicize findings Scientists are objective Expectation that researchers will be objective and not influenced by biases or prejudices Disseminating Research Research must be communicable, open, and public Research is published in journals, reports, or books 1) Methods of data collection are described 2) Data is reported 3) Analyses are displayed for examination 4) Conclusions are presented Developing Theory – What is Theory? Theory = idea(s) intended to explain something, based on general principles independent of the thing to be explained Describes and explains relationships between psychological constructs (variables) Helpful or not helpful (not good or bad) A “good” theory offers novel insights, is interesting and focused, is relevant to important topics, provides explanations, and is practical Research Designs 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: o Observational design: observes and records behavior o Survey/questionnaire design (most common) Quasi-experimental Non-random assignment of participants to conditions Research Design Questions Where will the research be conducted (e.g. lab)? Who will participate? How will participants be recruited and assigned to conditions? What variables will be measured? How will data be collected (e.g. survey)? How will data be analyzed? Methods of Data Collection Quantitative methods Rely on tests, rating scales, questionnaires, and physiological measures Yield results in terms of numbers Qualitative methods Include procedures like observation, interview, case study, and analysis of written documents Generally produce flow diagrams and narrative descriptions of events/processes Triangulation: examining converging information from different sources (qualitative and quantitative 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 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 Developing Hypotheses Direction and strength of relationship between variables Example: the number of hours an individual watches college football is related to increase in amount of cheeseburgers eaten Prediction of an outcome Example: decreased hours of sleep leads to increase in life satisfaction Differences across groups Example: the number of parties attended in the last six months is higher for students who watch True Blood compared to students who watch The Walking Dead Statistics in I-O Reliability and Validity Data analysis Summarize Organize Describe sample of data Measures of central tendency Mean Mode Median Describing Score Distribution Variability Standard deviation Lopsidedness or skew Mean is affected by high or low scores, median is not Mean pulls in direction of skew Inferential Statistics Aid in testing hypotheses and making inferences from sample data to a larger sample/population Includes t-test, F-test, chi-square test Statistical Significance Defined in terms of a probability statement Threshold for significance is often set a .05 or lower Significance refers only to confidence that the result is NOT due to chance, not strength of an association or importance of results Statistical significance is the low probability of obtaining at least as extreme results given that the null is true 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 Correlation Coefficient Statistic or measure of association Reflects magnitude (numerical value) and direction (+ or –) of relationship between two variables Ranges from 0.00 and 1.00 This graph shows a positive linear correlation: Correlation DOES NOT = causation Scatterplot o Displays correlational relationship between two variables Regression o Straight line that best “fits” the scatterplot and describes the relationship between the variables Positive correlation as one variable increases, the other variable also increases ad vice versa Negative correlation as one variable increases, the other variable decreases and vice versa Scatterplots of Various Degrees of Correlation: 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 See this graph as an example: Meta-Analysis: Statistical method for combining results from many studies to draw a general conclusion Reliability Consistency or stability of a measure Test-retest reliability o Calculated by correlating measurements taken at Time 1 with measurements taken at Time 2 Equivalent forms reliability o Calculated by correlating measurements from a sample of individuals who complete two different forms of the same test Internal consistency o Assesses how consistently items of a test measure a single construct Inter-rater reliability o Can calculate various statistical indices to show level of agreement among raters o Values in the range of .70 to .80 represent reasonable reliability Validity The degree to which evidence and theory support the interpretations of test scores for proposed uses of tests Validity is, therefore, the most fundamental consideration in developing tests and evaluating tests Whether measurements taken accurately and completely represent what is to be measured Predictor o Test chosen or developed to assess identified abilities or other characteristics (KSAOs) Criterion o Outcome variable describing important performance domain A UNITARIAN concept o There are not “types” of validity o Only evidence to support validity Construct Criterion Convergent and divergent Content Nomological networks o Need multiple forms of validity evidence Criterion-Related Validity: correlate a test score (predictor) with a performance measure; resulting correlation often called a validity coefficient Predictive validity design o Time lag between collection of test data and criterion data o Test often administered to job applicants Concurrent validity design o No time lag between collection of test data and criterion data o Test administered to current employees, performance measures collected at the same time o Disadvantage: no data about those not employed by the organization Demonstrates that content of selection procedure represents adequate sample of important work behaviors and activities or worker KSAOs defined by job analysis I-O psychologists can use incumbents/SMEs to gather content validity evidence Construct-Related Validity Investigators gather evidence to support decisions or inferences about psychological constructs Construct: a concept or characteristic that a predictor is intended to measure; examples include intelligence, extraversion, and integrity 02/02/2016 00.50.00 02/02/2016 00.50.00
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