QEM WS24_Wdh
Page 1
Copyright Notice
Any form of reproduction and dissemination requires explicit permission from the author.
Contact: Dr. Franziska Pfannerstill MSc, fpfannerstill.lba@fh-salzburg.ac.at
Page 2 - Descriptive Statistics Overview
Descriptive Statistics
Key focus on statistical measures of frequency and distribution.
Important terms include Percentiles, Quartiles, and various Statistical Methods.
Components Listed
Descriptive Statistics for the Population
Exploratory Data Analysis
Cross Tables
Measures of Central Tendency: Mean, Median, Mode
Variability: Standard Deviation, Variance, Range
Percentiles: Essential for understanding distribution.
Page 3 - Normal Distribution Testing
Normal Distribution Analysis
Tests for normality: Kolmogorov-Smirnov and Shapiro-Wilk tests used to assess data normality.
Statistical measures include degrees of freedom (df) and significance levels (Statistik).
Page 4 - IBM SPSS Data Editor Features
Data Editing Features
Sorting and transposing data
Identifying and validating unusual cases
Defining variable properties and attributes
Page 5 - Analysis Techniques Overview
Key Analysis Techniques
Differences:
Independent T-Test, Dependent T-Test, ANOVA
Non-parametric Tests: Mann-Whitney, Friedman’s ANOVA
Regression and Correlation methods such as Pearson’s r
Page 6 - Differences in Group Scores
Testing Differences
Focus on comparing means and variances between groups/ time points using tests such as T-Test or ANOVA.
Always report Mean and Standard Deviation.
Page 7 - Reporting Results Example
Statistical Reporting Template
Example structure for reporting differences among groups:
Includes M (Mean), SD (Standard deviation), T-Statistic, degrees of freedom (df), and p-values.
Page 8 - General Statistical Models
Comparative Models
Various statistical methods to compare means and proportions such as T-Tests and General Linear Models (GLM).
Incorporates both independent and dependent samples testing.
Page 9 - Mann-Whitney U Test Results
Mann-Whitney U Test Statistics
Provides specific values such as Mann-Whitney U, Wilcoxon W, and significance.
Page 10 - Test Assumptions Overview
Requirements for Testing Procedures
Covers conditions such as data distribution (normality) and homogeneity of variances for T-Tests and ANOVA.
Page 11 - ANOVA Assumptions
Anova Testing
Reiterates requirements: normal distribution and homogeneity of variance using Kolmogorov-Smirnov and Levene's Test.
Page 12 - Analyzing Differences and Relationships
Analytical Procedures
Focuses on correlation and regression analysis to establish relationships between variables.
Page 13 - Relationship Testing Techniques
Correlation and Chi-Square Analysis
Describes correlation coefficients and their significance levels using statistical measures.
Page 14 - Correlation Statistics Overview
Correlations and their Interpretation
Notes on significant bivariate correlations, including confidence intervals.
Page 15 - Summary of Relationships
Reporting Weak Relationships
Example on presenting correlational findings and Chi-Square results with relevant statistics.
Page 16 - Relationship Testing Requirements
Statistical Requirements for Relationships
Discusses conditions for Pearson’s r and contingency analysis affects correlation results.
Page 17 - Implications of Regression Analysis
Purpose of Regression
Explains the impact of independent variables on a dependent variable including significance of results.
Page 18 - Model Summary in Regression
Regression Model Example
Core statistics such as R Square to indicate variance explained by predictors.
Page 19 - Reporting Regression Results
Template for Regression Reporting
Includes structure for stating the contribution of predictors in influencing the dependent variable.
Page 20 - Regression Assumptions
Assumptions for Regression Analysis
Continuation of normality and variance homogeneity as fundamental requirements for meaningful results.