Research Methodology, Data Management, Analysis and Reporting using SPSS

INTRODUCTION

Research, Data Management, Graphics & statistical analysis has always been integral parts of development work and has been playing a critical role towards achieving of Sustainable development Goals (SDGs). As a result, knowledge of research methodologies and application of statistical application software's to support data analysis in this era is very important. Statistical Packages for Social Sciences (SPSS) has proved to be quite useful for the purpose of data management, graphical representation, and statistical analysis of data. SPSS application is user-friendly, reduces the time and efforts of the researcher employed in the process research. This course aims at equipping participants with knowledge and vast skills which will enable them to use SPSS in Data Management, Graphics & statistical analysis.

DURATION

TOPICS TO BE COVERED

Module 1: Statistical Concepts and Overview of SPSS

Statistical Concepts

• Descriptive Statistics
• Common inferential statistics

Overview of SPSS

• SPSS window-dialog boxes

• File Extensions (new, Open, Open Datasets)
• Save, Save As, Import and exporting datasets
• Creating and editing a data file

Module 2: Data Entry/Management, Descriptive Statistics and Graphics

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

 Descriptive Statistics: Measures of Variability and Central Tendency  

• Describing quantitative data
• Describing qualitative data

Graphics using SPSS

• Chart Builder
• Graphing quantitative and qualitative data

Module 3: Quantitative Data Analysis (Part I)

Correlation  

• Correlation
• Subgroup Correlations
• Scatterplots of Data by Subgroups
• Overlay Scatterplots

Regression Analysis

• Assumptions of selected types of regression
• Linear regression; Binary logistic regression; ordered logistic regression; multinomial logistic regression and Poisson regression

Comparing Means

• One Sample t-tests
• Paired Sample t-tests
• Independent Samples t-tests
• Comparing Means Using One-Way ANOVA 

Comparing Means Using Factorial ANOVA 

• Factorial ANOVA Using GLM Univariate
• Simple Effects

Comparing Means Using Repeated Measures ANOVA 

• Using GLM Repeated Measures to Calculate Repeated Measures ANOVAs
• Multiple Comparisons

Module 5: Quantitative Data Analysis (Part III)

 Chi-Square 

• Goodness of Fit Chi Square All Categories Equal
• Goodness of Fit Chi Square Categories Unequal
• Chi Square for Contingency Tables

Nonparametric Statistics 

• Mann-Whitney Test
• Wilcoxon’s Matched Pairs Signed-Ranks Test
• Kruskal-Wallis One-Way ANOVA