Data Management and Statistical Data Analysis using SPSS Course

Introduction

The training is essential in the development of better understanding of the concepts of statistics. It will provide the participants with a general idea of computer assisted data analysis. Additionally, the training will also focus on developing skills that are crucial to the transformation of data using SPSS.

Statistical Package for Social Sciences (SPSS) is one of the most user friendly statistical software for researchers providing visualization and data analytical tools. This course provides the participants with a practical application of the statistical component of IBM® SPSS® Statistics. Participants will review several statistical techniques, gain an understanding of when and why to use these various techniques as well as how to apply them with confidence, interpret their output, and graphically display the results.

Course Objective:

o   Perform data analysis tasks with SPSS

o   Perform simple to complex data management tasks using SPSS

o   Performing operations with data: define variables, recode variables, create dummy variables, select and weight cases, split files

o   Building charts in SPSS: column charts, line charts, scatterplot charts, boxplot diagrams

o   Performing the basic data analysis procedures: Frequencies, Descriptive, Explore, Means, Crosstabs

o   Testing the hypothesis of normality

o   Detecting the outliers in a data series

o   Transform variables

o   Performing the main one-sample analyses: one-sample t test, binomial test, chi square for goodness of fit

o   Performing the tests of association: Pearson and Spearman correlation, partial correlation, chi square test for association, loglinear analysis

Duration

5 days

Who should attend?

The course targets project staff, researchers, managers, decision makers, and development practitioners who are responsible for projects and programs in an organization. Those working in the corporate world, public sector, research institution and NGOs.

Course content

Introduction to Statistical Analysis

• Explain the basic steps of the research process
• Explain differences between populations and samples
• Explain differences between experimental and non-experimental research designs
• Explain differences between independent and dependent variables

Introduction to SPSS statistical software

• SPSS interface and features
• Key terminologies used in SPSS
• Views: Variable, Data views, Syntax editor
• Data file preparation
• Data entry into SPSS
• Data manipulation: merge files, spit files, sorting files, missing values

Basic Statistics using SPSS

• Descriptive statistics for numeric variables
• Frequency tables
• Distribution and relationship of variables
• Cross tabulations of categorical variables
• Stub and Banner Tables

Graphics using SPSS

• Introduction to graphs in SPSS
• Graph commands in SPSS
• Different types of Graphs in SPSS

Statistical Tests using SPSS

• One Sample T Test
• Independent Samples T Test
• Paired Samples T Test
• One-Way ANOVA

Statistical Associations in SPSS

• Chi-Square test
• Pearson's Correlation
• Spearman's Rank-Order Correlation
• Bivariate Plots and Correlations for Scale Variables

Predictive Models using SPSS

• Linear Regression
• Multiple Regression
• Logistic Regression
• Ordinal Regression

Nonparametric Tests

• Describe when non-parametric tests should and can be used
• Describe the options in the Nonparametric Tests procedure dialog box and tabs
• Interpret the results of several types of nonparametric tests

Longitudinal Analysis using SPSS

• Features of Longitudinal Data
• Exploring Longitudinal data
• Longitudinal analysis for continuous outcomes

Time Series and Forecasting using SPSS

• The basics of forecasting
• Smoothing time series data
• Regression with time series data
• ARIMA models
• Intervention analysis

SPSS Decision Trees

• Introduction to SPSS Decision Trees
• Application of SPSS Decision Trees
• Overview of decision tree based methods (CRT Decision Trees CRT Regression Trees Quest Analysis)