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 intend 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
• Merging files and restructuring
• 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

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