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I-O Psychology Notes 1

by: Freddi Marsillo

I-O Psychology Notes 1 PSYC 2544

Freddi Marsillo
GPA 3.55
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About this Document

These notes cover the materials that were taught during the first three weeks of class.
Industrial/Organizational Psychology
Blacksmith, N
Class Notes
Industrial Organizational Psychology




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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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