Training course on Advanced Statistical Analysis Using Statistical Package for Social Sciences (SPSS)

Workshop Title: Advanced Statistical Analysis Using Statistical Package for Social Sciences (SPSS)

Dates and registration links

5/12/2022 to 9/12/2022:https://bit.ly/3MuonTn

6/2/2023 to 10/2/2023: https://bit.ly/3Cyj9Bw

15/05/2023 to 19/05/2023: https://bit.ly/3yCx9cc

21/08/2023 to 25/08/2023: https://bit.ly/3VFcTkj

20/11/2023 to 24/11/2023: https://bit.ly/3yHVVb3

INTRODUCTION

SPSS means Statistical Package for the Social Sciences, and it is appropriate for complex statistical data analysis of social science data. SPSS software allows for thorough data management in terms of organizing and managing data and also offers the user a lot of control. Other benefits of the software in data analysis includes it is good in data screening and cleaning, Better organization of the output in a separate window with the data editor window which avoid any data being deleted. This 5 days course intends to equip participants with advanced techniques using SPSS software.

DURATION

5 days

TARGET PARTICIPANTS

Researchers, students, anyone who interested in enhancing his/ her data analysis skills

COURSE OBJECTIVES

By the end of the training, you will be able to:

Understand both descriptive and inferential statistics

Understand various data collection techniques and data processing methods

Use basic functions and navigation within Stata and SPSS software

Create and manipulate graphs and figures in SPSS software

Handle statistical data analysis tasks in SPSS software

Export the results of your analyses.

TOPICS TO BE COVERED

Module 1: Statistical Concepts

Statistical Concepts

Types of data

Data Structures and Types of Variables

Overview of SPSS

Working with the SPSS software (file management, editing functions, viewing options, etc)

Output Management

Basics programming of SPSS

Module 2: Data Entry/Management

Entering categorical and continuous data

Defining and labeling variables

Validation and Sorting variables

Transforming, recording and computing variables

Restructuring data

Replacing missing values

Splitting files, Selecting cases and weighing cases

Syntax and output

Module 3:

Statistical Inference and Descriptive Statistics

Tests of Association

Tests of Difference

Hypothesis testing

Measures of Variability and Central Tendency

Describing quantitative data

Describing qualitative data

Graphics in Data Analysis

Graphing quantitative data

Graphing qualitative data

Advanced graphics options

Correlation

Correlation of bivariate data

Subgroup Correlations

Scatterplots of Data by Subgroups

Overlay Scatterplots

Module 4: Test statistics and tests of associations

Comparing Means

One Sample t-tests

Paired Sample t-tests

Independent Samples t-tests

Comparing Means Using One-Way ANOVA

MANOVA: multivariate analysis of variance

Repeated Measures ANOVA

Tests of associations

Goodness of Fit Chi Square All Categories Equal

Goodness of Fit Chi Square Categories Unequal

Chi Square for Contingency Tables

Pearson Correlation

Spearman Correlation

Nonparametric Statistics

Mann-Whitney Test

Wilcoxon’s Matched Pairs Signed-Ranks Test

Kruskal-Wallis One-Way ANOVA

Friedman’s Rank Test for k Related Samples

Comparing Means Using Factorial ANOVA

Factorial ANOVA Using GLM Univariate

Simple Effects

Module 5:

Comparing Means Using Repeated Measures ANOVA

Using GLM Repeated Measures to Calculate Repeated Measures ANOVAs

Multiple Comparisons

Other topics

Multiple Regression Analysis

Logistic regression (Binary & Multinomial)

Cluster Analysis

Factor analysis