This article introduces the steps of ordered logistic regression analysis, including how to set variables, run the test of parallel lines, derive the regression equation, and interpret results, helping readers understand model fit and the effects of independent variables on the dependent variable.
This article explains the steps for multinomial logistic regression analysis, including how to set variables, choose reference categories, calculate regression equations, and interpret model goodness of fit and pseudo R-squared, helping readers master practical application and interpretation of logistic regression.
This article explains how to perform binary logistic regression analysis, covering chi-square tests, regression-equation setup and interpretation, and model goodness-of-fit and significance testing, helping readers understand regression-model applications in data analysis.
This article introduces three common logistic regression models: binary logistic regression, multinomial logistic regression, and ordered logistic regression. It explains their applications and differences for different dependent-variable types, helping readers understand when to use these statistical models in real analysis.
This article introduces the main differences between linear regression and logistic regression, and explains the steps, result interpretation, and common statistics in regression analysis, such as R-squared, the Durbin-Watson value, and regression coefficients, helping readers understand how to use regression models for data analysis.
This article introduces how to use SPSS for correlation analysis and how to interpret correlation matrices and heatmaps, helping readers quickly master correlation-analysis methods and result interpretation among scale dimensions.
This article explains independent-samples t tests and one-way analysis of variance (ANOVA) in difference analysis, including SPSS operation steps, homogeneity-of-variance testing, t-value analysis, and result interpretation, helping readers understand how to compare mean differences between groups in data analysis.
