Rotation Types in Exploratory Factor Analysis (EFA)

Rotation Types in Exploratory Factor Analysis  Rotation causes factor loadings to be more clearly differentiated, which is often necessary to facilitate interpretation. Several types of rotation are available for your use. Orthogonal Varimax (most common) minimizes number of variables with extreme loadings (high or low) on a factor makes it possible to identify a variable with […]

 Detecting Mulitcollinearity

 Detecting Mulitcollinearity Multicollinearity is not desirable. It means that the variance our independent variables explain in our dependent variable are are overlapping with each other and thus not each explaining unique variance in the dependent variable. The way to check this is to calculate a Variable Inflation Factor (VIF) for each independent variable after running […]

Heteroscedasticity Test in Regression Analysis

Heteroscedasticity Test in Regression Analysis Homoscedasticity is a nasty word that means that the variable’s residual (error) exhibits consistent variance across different levels of the variable. There are good reasons for desiring this. For more information, see Hair et al. 2010 chapter 2. 🙂 A simple way to determine if a relationship is homoscedastic is to […]

Linearity Test Using SPSS 

Linearity Test Using SPSS  Linearity refers to the consistent slope of change that represents the relationship between an IV and a DV. If the relationship between the IV and the DV is radically inconsistent, then it will throw off your SEM analyses. There are dozens of ways to test for linearity. Perhaps the most elegant […]

Normality Tests – Detecting Normality Issues

Normality Normality refers to the distribution of the data for a particular variable. We usually assume that the data is normally distributed, even though it usually is not! Normality is assessed in many different ways: shape, skewness, and kurtosis (flat/peaked). Shape: To discover the shape of the distribution in SPSS, build a histogram (as shown in […]

Detecting Univariate Outliers

Outliers Outliers can influence your results, pulling the mean away from the median. Two types of outliers exist: outliers for individual variables, and outliers for the model. Univariate To detect outliers on each variable, just produce a boxplot in SPSS (as demonstrated in the video). Outliers will appear at the extremes, and will be labeled, […]

Steps to Address Missing Data

Steps to Address Missing Data Addressing missing data is an important step in the data preprocessing phase of any data analysis or machine learning project. Here are some common strategies for handling missing data: Identify Missing Data: Begin by identifying which features have missing values and the extent of the missing data. Understanding the pattern […]

How to Address Missing Data in SPSS

Missing Data Using SPSS If you are missing much of your data, this can cause several problems. The most apparent problem is that there simply won’t be enough data points to run your analyses. The EFA, CFA, and path models require a certain number of data points in order to compute estimates. This number increases […]

Understanding Mediation Analysis

Introduction to Mediation Analysis This post intends to introduce the basics of mediation analysis and does not explain statistical details. For details, please refer to the articles at the end of this post. What is mediation? Let’s say previous studies have suggested that higher grades predict higher happiness: X (grades) → Y (happiness). (This research […]

How to do Nonparametric kernel regression in Stata

Today we Learn how to do Nonparametric kernel regression in Stata Nonparametric series regression (NPSR) estimates mean outcomes for a given set of covariates, just like linear regression. Unlike linear regression, NPSR is agnostic about the functional form of the outcome in terms of the covariates, which means that NPSR is not subject to misspecification […]