STATISTICAL DATA ANALYSIS USING SPSS

Introduction

SPSS (Statistical Package for the Social Sciences) is a software program for statistical analysis, data management, and data visualization, originally designed for social science research but now used in many fields. It features an easy-to-use interface that allows users to perform both basic statistics and advanced analysis, like regressions,  chi-square tests, and correlations, without extensive coding. The software is
used by market researchers, health researchers, and government agencies to
analyze survey data and make data-driven decisions. 

Based on analysis of typical course outlines from training providers and academic institutions, a comprehensive SPSS course content covers topics from introductory basics to advanced statistical modeling. 

 Course Contents

Module 1: Introduction to SPSS and Data Management

• Overview of the SPSS environment: The Data View, Variable View, Syntax Editor, and Output Viewer.
• Data entry and file management: Manually entering data, importing data from various file types (e.g., Excel, CSV), and managing SPSS file types.
• Data cleaning and manipulation: Identifying and handling missing data, recoding and computing new variables, sorting and merging data files, and filtering cases.
• Data visualization: Creating and editing basic charts and graphs, such as bar charts, histograms, pie charts, and scatterplots. 

Module 2: Descriptive Statistics

• Exploring data distributions: Calculating measures of central tendency (mean, median, mode) and dispersion (range, standard deviation, variance).
• Summarizing data: Generating frequency tables, cross-tabulations, and descriptive reports for both categorical and continuous variables.
• Testing assumptions: Checking for normality (e.g., Kolmogorov-Smirnov test) and identifying outliers. 

Module 3: Inferential Statistics (Comparing Means)

• Hypothesis testing: Understanding fundamental concepts like null vs. alternative hypotheses, p-values, and confidence intervals.
• T-tests: Conducting and interpreting one-sample, independent samples, and paired samples t-tests.
• Analysis of Variance (ANOVA): Performing and interpreting one-way ANOVA for comparing more than two group means, including post-hoc analysis.
• Repeated measures ANOVA: Analyzing data from within-subject designs.
• Multivariate Analysis of Variance (MANOVA): Testing for differences among multiple dependent variables. 

Module 4: Inferential Statistics (Associations and Prediction)

• Correlation analysis: Calculating and interpreting correlation coefficients (e.g., Pearson’s, Spearman’s) to examine relationships between variables.
• Regression analysis:
  • Simple linear regression: Modeling the relationship between one
    independent and one dependent variable.
  • Multiple linear regression: Using multiple independent variables to predict
    a single dependent variable.
  • Logistic regression: Predicting a binary or categorical outcome.
• Chi-square test: Analyzing the relationship between categorical variables. 

Module 5: Advanced Multivariate Analysis

• Factor analysis: Identifying underlying, unobservable variables (factors) from a set of observed variables.
• Cluster analysis: Grouping similar cases or objects into meaningful clusters.
• Survival analysis: Modeling time-to-event data. 

Module 6: Non-Parametric Tests

• Analyzing non-normally distributed data: Using non-parametric tests like the Mann-Whitney U, Wilcoxon signed-rank, and Kruskal-Wallis tests. 

Module 7: Automation and Reporting

• SPSS syntax: Automating and reproducing data manipulation and analysis processes using command syntax.
• Interpreting and reporting output: Presenting statistical findings in professional or academic formats (e.g., APA style)

Duration: Three (3) days        Fee: N250,000