When to use the chi-square goodness of fit test – tables, formulas and examples
The chi-square goodness of fit test is a statistical test used to determine whether there is a significant difference between the expected and observed frequencies in one or more categories. It is often applied when dealing with categorical data and is useful for assessing whether the distribution of observed categorical data differs from a theoretical […]
How to Write an Argumentative Essay: Examples & Tips
How to Write an Argumentative Essay: Examples & Tips Writing an argumentative essay involves presenting a well-supported argument in favor of a particular thesis statement. Whether you encounter this task in high school or at the university level, mastering the art of argumentation is crucial. This article provides a guide on how to write an […]
Exploratory Factor Analysis (EFA) Results Interpretation
Exploratory Factor Analysis (EFA) Results Interpretation Appropriateness of data (adequacy) KMO Statistics Marvelous: .90s Meritorious: .80s Middling: .70s Mediocre: .60s Miserable: .50s Unacceptable: <.50 Bartlett’s Test of Sphericity Tests hypothesis that correlation matrix is an identity matrix. Diagonals are ones Off-diagonals are zeros A significant result (Sig. < 0.05) indicates matrix is not an identity […]
Factoring Methods in Exploratory Factor Analysis (EFA)
Factoring Methods in Exploratory Factor Analysis (EFA) There are three main methods for factor extraction. Principal Component Analysis (PCA) Use for a softer solution Considers all of the available variance (common + unique) (places 1’s on diagonal of correlation matrix). Seeks a linear combination of variables such that maximum variance is extracted—repeats this step. Use when […]
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, […]
