Training on Quantitative Data Management and Analysis using SPSS

In the socio-economic and business context, conducting research, data management, and data analysis are imperative for informed decision making. IBM SPSS Statistics is a powerful statistical software platform. It delivers a robust set of features that lets your organization extract actionable insights from its data. The software is more popular in social sciences. Sound knowledge about the methodology of conducting research and the use of SPSS is very beneficial for to researchers. Upon completion of workshop, participants will develop competence in quantitative techniques in research design, data collection, and management, statistical data analysis, interpretation and reporting of results.

Learning outcomes

By the end of this course the participants will be able to:

•      Easily collect high quality data using mobile devices such as tablets and phones.
•      Clean their data for use in subsequent statistical analysis.
•      Identify and fix errors in datasets.
•      Analyze and better understand their data, and solve complex business and research problems through a user-friendly interface.
•      More quickly understand large and complex data sets with advanced statistical procedures that help ensure high accuracy and quality decision-making.
•      Gain high level skills on statistical results interpretation and report writing.

Who should enroll?

The course is useful for professionals who use data as part of their work and who need to make decisions from data analysis. This course does not assume previous knowledge and competency in using SPSS software.

Why train with us

Vital Extra Learning guarantees our clients:

•      State-of-the-art facilities and training infrastructure
•      Extended tradition of hand-holding during post engagement
•      Service delivery through highly seasoned industry experts.
•      Value for money

Module 1: Introduction

Introduction to Statistical Data Analysis

•      Introduction to statistical concepts
•      Descriptive and inferential statistics
•      The research/survey process
•      Research designing

Introduction to SPSS statistical software

•      Installing the software (key consideration and procedures)
•      SPSS interface and features
•      SPSS terminologies
•      SPSS views
•      Data entry into SPSS
•      Data manipulation: merge files, spit files, sorting files, missing values

Basic Statistics using SPSS

•      Introduction to descriptive and inferential statistics
•      Descriptive statistics – Measures of centres, distribution, dispersion
•      Frequency distribution tables

Module 2: Data/Output Management and Graphics

Data Management

•      Defining and labeling variables
•      Cleaning data
•      Sorting data
•      Transforming, coding and computing variables
•      Restructuring data
•      Dealing with missing values
•      Merging files
•      Splitting files
•      Selecting cases 
•      Weighing cases
•      Key syntax in SPSS
•      Output management in SPSS

Graphics using SPSS

•      Introduction to graphs in SPSS
•      Graph commands in SPSS
•      Types of SPSS graphs (Bar graph; Scatter plot; Line chart; Histogram; Box plot; Pie chart; Q-Q plot; P-P plot)

Module 3: Inferential Statistics (Statistical Tests) using SPSS

Test of differences in means

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

Test of associations

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

Module 4: Regression Analysis and Non-Parametric Tests in SPSS

Regression Models using SPSS

•      Linear regression (simple and multiple regression)
•      Binary logistic regression
•      Multinomial logistic regression
•      Ordinal regression
•      2-stage least square regression

Nonparametric Tests

•      Application of non-parametric tests
•      Options available in Nonparametric Tests procedure dialog box and tabs
•      Interpretation of nonparametric tests results

Module 5: Longitudinal and Time-Series Data Analysis

Longitudinal Analysis using SPSS

•      Introduction to panel data
•      Benefits of panel data
•      Problem with panel data
•      Features of Longitudinal Data
•      Exploring Longitudinal data
•      Regression models with panel data (random effects; fixed effects; between-within models)

Time Series and Forecasting using SPSS

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