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    1. Measures of Central Tendency

    Measures of Central Tendency are ways of working out the most representative or central value within a data set. In statistics, there are three common measures of central tendency: The mean, The median, The mode. Please click the following SPSS Video Tutorials to see how to run Measures of Central Tendency Tests in SPSS.

    Descriptive Statistic Test

    Normality Test

    2. Parametric Tests

    Parametric tests are used when data follow a particular distribution (e.g., a normal distribution—a bell-shaped distribution where the median, mean, and mode are all equal). These tests are generally more powerful. Please click the following SPSS Tutorial Videos to see how to run Parametric Tests in SPSS.

    One Sample t Test

    Paired Sample t Test

    Independent Sample t Test

    One-Way ANOVA Test

    3. Non-parametric Tests

    Nonparametric tests are used when a particular distribution cannot be assumed; they rank data rather than taking absolute differences into account.  Please click the following SPSS Video Tutorials to see how to run Nonparametric Tests in SPSS.

    Chi Square Test

    how to run chi square test in spss

    Wilcoxon Signed Rank Test

    Mann Whitney U Test

    Kruskal Wallis Test

    Friedman Test

    4. Analysis of Variance

    We use the Analysis of variance (ANOVA) in statistics that splits an observed aggregate variability found inside a data set into two parts: systematic factors and random factors. The systematic factors have a statistical influence on the given data set, while the random factors do not. Analysts use the ANOVA test to determine the influence that independent variables have on the dependent variable in a regression study. Please click the following Free SPSS Video Tutorials to see how to run Analysis of Variance Tests in SPSS.

    Repeated ANOVA Test

    Two Way ANOVA Test

    One way Manova Test

    Two Way Manova Test

    5. Correlation Analysis

    Correlation analysis is a method of statistical evaluation used to study the strength of a relationship between two, numerically measured, continuous variables. There are two types of correlation analysis. The Pearson correlation evaluates the linear relationship between two continuous variables while the Spearman correlation coefficient is based on the ranked values for each variable rather than the raw data. Please click the following free SPSS Tutorials to see how to run Correlayin Analysis Tests in SPSS.

    Spearman’s Rank Correlation Test

    Pearson Correlation Test

    6. Regression Analysis

    Regression analysis is a powerful statistical method that allows you to examine the relationship between two or more variables of interest. While there are many types of regression analysis, at their core they all examine the influence of one or more independent variables on a dependent variable. Please click the following SPSS Video Tutorials to see how to run Regression Analysis in SPSS.

    Simple Linear Regression Test

    Multiple Regression Test

    Binary Logistic Regression Test

    7. Exploratory Factor Analysis

    Factor analysis is a technique that we can use to reduce a large number of variables into fewer numbers factors. While Reliability analysis provides a measure of how well a group of observed variables goes together. Please click the following SPSS Video Tutorials to see how to Factor and Reliability Analysis in SPSS.

    Exploratory Factor Analysis

    How to run exploratory factor analysis in spss

    Reliability Analysis Test

    8. Survival Analysis

    Survival analysis is a set of statistical approaches for data analysis where the outcome variable of interest is time until an event occurs. Please click the following SPSS Video Tutorials to see how to run Survival Analysis in SPSS.

    Survival Analysis

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