Psychometric Instrument Development - Multilinear Regression - Types of EFA - Management Assessment Answer

Internal Code: 1AFHGF

Management Assessment Answer

TASK: Psychometric instrument development Conduct an EFA of a multidimensional construct (you can choose which one – either personality or perfectionism). If you’re unsure how to do this analysis, check your notes from the Psychometrics tutorial. There are several things to report from these analyses:

•  Indicate the type of EFA you used (type of extraction? Rotation?)
• Where the assumptions of EFA met? Consider sample size (including cases:variables ratio), linearity (e.g. check scatterplots, paying attention to outliners or nonlinear relations), factorability of the correlation matrix
•  Focus on the final model but summarise the steps taken to get there (e.g., How many factors were extracted initially? What models/factors structures were examined? To what extent was the expected structure evident?)
•  % of variance explained (for the initial and final model(s))
•  Label and describe each factor
• Which items were retained and/or dropped and the reasons why
• Table of factor loadings (sorted by size) and commonalities (for the final model)
•  Reliability analysis (Internal consistency/Cronbach's alpha) for each factor
• Calculation of composite scores to represent each factor
•  Table of descriptive statistics for the composite scores
• Table of correlations between composite scores

Multiple linear regression Conduct an MLR to test the hypotheses from your introduction. Remember that this needs to have at least three predictors, which can be any variables in, or derived from, the supplied data set, providing that they meet the assumptions for MLR and one dependent variable. Again, there are several things you need to include when you report this analysis:

• Present the correlations between the items, and demonstrate an understanding of the directions of any relationships (e.g., if there is a positive correlation between X and Gender, what does this mean? Are higher values of X associated with males or females?)
• Reiterate why you are running the MLR (what hypotheses are you testing?)
•  Mention the type of MLR (e.g., standard, hierarchical, or stepwise)
•  Describe the IVs and DVs, and any manipulations of the variables (e.g., recording or creating an interaction term). If it’s not already clear from the Method section, clarify the direction of scoring
•  Explain the extent to which assumptions were met (e.g., sample size, multicollinearity, multivariate outliers)
•  Report the amount of variance explained: R 2  and Adjusted R 2, and the R 2  change at each step if a hierarchical MLR is being conducted, along with inferential tests
•  Report the significance, size, direction and relative contribution of each IV. Make sure to explain what the direction of the relationships mean in plain English, not just stats talk!
•  Include a table showing both the correlations and MLR coefficients, including B for intercept & IVs and Beta (?), and the statistical significance (e.g., t, p), and semi-partial correlations squared (sr 2 ) for each IV and explain the direction and size of the results.
•  Consider the shared and unique percentages of explained variance.

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