We have the exact same Beta, std error, and wald z-stat for the main effect of gender and the two interaction terms, but our main effects of casual sex attitudes and sexual script endorsement are different. Youll also need to dummy code any categorical/nominal predictors. I attached my R studio syntax and output. We also want to run a second model with the same moderator and predictors, but with actual engagement as the outcome (second outcome: engagement.yes_no). Please know I have spent HOURS trying to figure this out… and could really use your help!įor this study, we are researching how gender (moderator: Gend) moderates the effect of people’s endorsement of sexual scripts (first predictor: sexualscriptendorsementsmean) and attitudes towards casual sex (second predictor: casualsexscore) on their desire to engage in consensual non-monogamy (outcome: desire.yes_no). In our experience, the most important of these for statistical analysis are the SPSS Advanced Modelsand SPSS Regression Models add-on modules.
REGRESSION /MISSING LISTWISE /STATISTICS COEFF OUTS CI(95) R ANOVA /CRITERIAPIN(.05) POUT(.10) /NOORIGIN /DEPENDENT performance /METHODENTER iq /SCATTERPLOT(ZRESID ,ZPRED) /RESIDUALS HISTOGRAM(ZRESID). The core program is called SPSS Baseand there are a number of add-on modules that extend the range of data entry, statistical, or reporting capabilities.
I think that I am writing the glm() code wrong, but I am brand new to R and not sure how to fix it. SPSS Simple Linear Regression Syntax Simple regression with residual plots and confidence intervals. Many computations are facilitated with the RLM macro for SPSS and SAS introduced and documented in this book.
However, we seem to be getting different results for some the main effects, but the same results for the interaction terms. Output and code using SPSS, SAS, and STATA is emphasized, with an appendix dedicated to regression analysis using R. Dummy coding gets around this assumption. Hello, I am currently trying to learn how to run a logistic regression with three main effects and two interaction terms in R studio for a study while comparing my results to my mentor’s results in SPSS. Descriptive Statistics: 24.4150 9.78835 20 12.0500 4.47772 20 12.6500 5. Used in techniques like Regression where there is an assumption that the predictors measurement level is scale.