Training Course in Advanced Statistical Analysis Using Statistical Package for Social Sciences (SPSS)

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

•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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TRAINING CUSTOMIZATION

This training can also be customized for your institution upon request. You can also have it delivered to your preferred location. For further inquiries, please contact us through Mobile: +254 732 776 700 or +254 759 285 295. You can also email us on: [email protected]

 

REQUIREMENTS

Participants should be reasonably proficient in English.  During the trainings, participants should come with their own laptops.

 

TRAINING FEE

The course fee covers the course tuition, training materials, two break refreshments, lunch, and study visits.

 

ACCOMMODATION

Accommodation is arranged upon request. For reservations contact us through Mobile: +254 732 776 700/+254 759 285 295 or Email: [email protected]