Lab 3_Advanced Statistics_6000
Creators: name: Rebeiro, Joe
Publisher: name: Open Science Framework
Year: 2023
Subjects: subject: Social and Behavioral Sciences
subject: Social Psychology
subject: Psychology
subject: FOS: Psychology

description: Power Analysis Task 1: PI happened to conduct a study and do a regression-based analysis, and there will be six variables, and among them, five will be independent variables, and one will be a dependent variable. So, for this study, there will be five predictors. So, PI wants to conduct a power analysis to know the sample size when the power level of the study will be 0.80, and the level of significance will be 0.05. To do this regression-based analysis, G Power software will be used, and the statistical test will be Linear multiple regression: Fixed models, R2 deviation from zero. In the input parameter, the correlation coefficient ρ2 will be set at 0.30 to know the required sample size. Task 2: PI also wants the ability to detect individual regression coefficient effects for a 5% variance in the outcome. For this, the G power software will be used again, and this time the statistical test would be Liner multiple regression: Fixed model, single regression coefficient, and in the input, parameters section the partial R2 would be set at 0.05 to know the sample size having the power of 0.80 and level of significance of 0.05. Correlation Correlation will be conducted with the 273 participants' data on age, productivity at work, ability to have fun, openness to experience, happiness, and their self-reported gender. Details will also be provided on the correlation of happiness with all other quantitative variables of concern. Regression Analysis Regression analysis will be conducted to understand happiness using all other variables like age, productivity at work, ability to have fun, openness to experience, and self-reported gender as predictor variables. At first, assumptions of normality would be conducted using tests such as Shapiro-Wilks, Kolomogorov-Smirnov, and Anderson Darling and also with the visual inspection of QQ plots and residual plots. There will also be an attempt to understand if the data have any multicollinearity or not using the collinearity statistics, it would be known from VIF and tolerance level. There will also be a correlation matrix done to see if the predictor variables do not have any overlapping variance. A heteroscedasticity test will also be done to test any serious violation of normality. The three tests would be Breush-Pagan, Goldfeld-Quandt, and Harrison-McCabe. Outliers will also be detected by performing cook's distance, and the cut of score would be 1. In case there are any outliers, then they will be filtered out before running the regression analysis. Regression analysis will be done in the Jamovi software, and the dependent variable will be happiness, and covariates will be age, work productivity, having fun, and openness to new experiences. Gender will be in the factors as it's a categorical variable, and the model coefficient table will be explained to detect which predictors are significant. For the hierarchical regression model, two models will be built in the model builder section in the first block, there would be age, gender, and work productivity, and in the second block, there would be having fun and openness to new experiences. The variability of R2 will be explained from the model comparison's table.