Getting started with SPSS

Handling statistical data is an essential part of many research. However, many people find the idea of using statistics, and especially statistical software packages, extremely daunting.

This course, Getting started with SPSS, takes a step-by-step approach to statistics software through seven interactive activities.

Target Participants

The training is designed for participants who intend to learn the use of SPSS for data management and descriptive data analysis. Those working in the corporate world, public sector, research institution and NGOs.

Course learning outcomes

After studying this course, you should be able to:

• understand how to start SPSS
• define a variety of statistical variables
• enter basic data into SPSS
• editing data
• plot diagrams and graphs
• carry out a descriptive statistical analysis

Course content

1. Introduction to Statistics, Data and SPSS

• Introduction to Statistics
• About Data
• About Big Data
• Learn about SPSS
• SPSS Vs. Excel

2. Getting Started

• Open SPSS
• Review the layout of SPSS
• Become familiar with Menus and Icons
• Exit SPSS

3. Creating and Editing a Data File

• Research Concerns and Structure of the Data File
• Entering Data
• Step by Step Self-Practice
• Editing Data
• Examples and Exercises

4. Managing Data

• Step By Step: Manipulation of Data
• The Case Summaries Procedure
• The Compute Procedure: Creating Variables
• The Recode into Different Variables Procedure Creating New Variables
• The Select Cases Option
• The Sort Cases Procedure
• Merging Files Adding Blocks of Variables or Cases
• Printing Results
• Examples and Exercises

5. Frequencies, Graphs and Charts: Creating and Editing

• Comparison of the Two Graphs Options
• Types of Graphs Described
• The Sample Graph
• Producing Graphs and Charts
• Specific Graphs Summarized
• Frequencies
• Step by Step Self-Practice
• Printing Results
• Examples and Exercises

6. Coding, Missing Values, Conditional and Arithmetic Operations

• Coding of Data
• Defining Missing Values
• Types of Missing Value
• Arithmetic Operations
• Conditional Transforms
• Examples and Exercises

7. Descriptive Statistics

• Statistical Significance
• The Normal Distribution
• Measures of Central Tendency
• Measures of Variability Around the Mean
• Measures of Deviation from Normality
• Measures for Size of the Distribution
• Measures of Stability: Standard Error
• Printing Results
• Interpret Outputs
• Examples and Exercises

Prerequisites

No prior working knowledge of SPSS software is required for this course.

Certification

Upon successful completion of this course, participants will be issued with a certificate.

Course Duration (classroom-based)

3 Days (8 hours a day)

Payment

1500 ETB

Course Level

Beginner level

Training Language

English (Optional - Amharic)

Download the Course Outline

Download this course outline for use offline or for other devices.

Data Management and Statistical Analysis using SPSS

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.

Target Participants

The training is designed for participants who intend to learn the use of SPSS for data management and data analysis. Those working in the corporate world, public sector, research institution and NGOs.

What you will learn

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

• Understand and appropriately use statistical terms and concepts
• Perform data analysis tasks with SPSS
• Perform simple to complex data management tasks using SPSS
• Statistical tests using SPSS

Course Outline

1. 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

2. 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

3. 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

4. Graphics using SPSS

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

5. Statistical Tests using SPSS

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

6. Statistical Associations in SPSS

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

7. Predictive Models using SPSS

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

8. 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

9. Longitudinal Analysis using SPSS

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

10. Time Series and Forecasting using SPSS

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

11. 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)

Training Approach

This course is delivered by our seasoned trainers who have vast experience as expert professionals in the respective fields of practice. The course is taught through a mix of practical activities, theory, group works and case studies.

Training manuals and additional reference materials are provided to the participants.

Certification

Upon successful completion of this course, participants will be issued with a certificate.

Prerequisites

Familiarity with basic statistical knowledge. No prior working knowledge of SPSS software is required for this course.

Course Duration (classroom-based)

6 Days (8 hours a day)

Payment

2900 ETB

Course Level

Intermediate level

Training Language

English (Optional - Amharic)

Download the Course Outline

Download this course outline for use offline or for other devices.

Advanced Statistical Analysis using SPSS

In the socioeconomic and business context, conducting research, data management and data analysis are imperative for informed decision making. The availability of several datasets and research techniques open the gateway of conducting systematic research which will be helpful for consumers, businesses and organizations.

A sound knowledge about the use of SPSS Software as a data management and analysis tool is very beneficial for the researchers. This course introduces you to a range of advanced statistical modelling techniques within SPSS Software Statistics and covers how and when they should be used.

Target Participants

Anyone who has worked with IBM SPSS Statistics Software for Windows and wants to become better versed in the more advanced statistical capabilities of SPSS Statistics Software.

Anyone who has a solid understanding of statistics and wants to expand their knowledge of appropriate statistical procedures and how to set them up using SPSS Statistics.

What you will learn

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

• A broad range of advanced statistical models
• Issues best addressed by certain statistical techniques
• Data considerations for choice of optimal techniques
• Evidence based modeling and reporting

Course Outline

1. Regression Analysis

• Introduction
• Simple Linear Regression
• Simple Linear Regression Assumptions
• Requesting Simple Linear Regression
• Simple Linear Regression Output
• Procedure: Simple Linear Regression
• Demonstration: Simple Linear Regression
• Multiple Regression
• Multiple Linear Regression Assumptions
• Requesting Multiple Linear Regression
• Multiple Linear Regression Output
• Procedure: Multiple Linear Regression
• Demonstration: Multiple Linear Regression
• Learning Activity

2. Logistic regression (Binary & Multinomial)

• Binary Logistic Regression
• Multiple Logistic Regression
• Assumptions of Logistic Regression
• Fitting Logistic Regression Models
• Example - Diabetes Dataset
• Multiclass Case (K ≥ 3)

3. Discriminate analaysis

• Class Density Estimation
• Linear Discriminant Analysis
• Optimal Classification
• Binary Classification
• Estimating the Gaussian Distributions
• Example - Diabetes Data Set
• Simulated Examples
• Quadratic Discriminant Analysis
• Connection between LDA and logistic regression

4. Factorial ANOVA and ANCOVA

• Introduction to ANOVA and ANCOVA
• One-Way Between Subjects ANOVA
• One-Way Within Subects ANOVA
• Two-Way Between Subjects ANOVA
• One-Between One-Within ANOVA
• Post Hoc Analysis of a Significant Interaction
• Analysis of Covariance (ANCOVA)

5. MANOVA: Multivariate Analysis of Variance

• One-way MANOVA
• Two-way MANOVA
• MANOVA assumptions
• MANCOVA
• MANOVA for Latin-square designs
• MANOVA for nested designs
• MANOVA for mixed designs
• MANOVA for repeated measures

6. Survival Analysis

• Survival Analysis Model
• Kaplan-Meier Plot
• Cox Model
• Multiple Cox Model
• Proportionality Assumption
• Example - Diabetes Data Set

7. Cluster Analysis

• Introduction
  • Introduction to unsupervised machine learning methods
  • Introduction to clustering
  • Overview of clustering uses for learning analytics
• Overview of k-means and hierarchical clustering methods
  • K-means clustering theory
  • K-means full example
  • Hierarchical clustering theory
  • Hierarchical clustering full example
• Practical considerations
  • Practical considerations
  • How to interpret clustering results
  • Overview of more advanced clustering methods

8. Principal Components Analysis

• Singular Value Decomposition (SVD)
• Principal Components
• Principal Components Analysis (PCA)
• Geometric Interpretation
• Example - Data Set

9. Factor analysis

• Introduction to Exploratory Factor Analysis
• Factor Analysis Applications
• CFA and Path Analysis with SPSS

10. Log-Linear Models

• Log-Linear Models for Two-way Tables
• Log-linear Models for Three-way Tables
• Example: Data Set

Training Approach

This course is delivered by our seasoned trainers who have vast experience as expert professionals in the respective fields of practice. The course is taught through a mix of practical activities, theory, group works and case studies.

Training manuals and additional reference materials are provided to the participants.

Certification

Upon successful completion of this course, participants will be issued with a certificate.

Prerequisites

Basic statistical knowledge and prior working knowledge of SPSS software are required for this course.

Tailor-Made Course

We can also do this as tailor-made course to meet organization-wide needs. A training needs assessment will be done on the training participants to collect data on the existing skills, knowledge gaps, training expectations, and tailor-made needs.

Course Duration (classroom-based)

15 Days (8 hours a day)

Payment

6900 ETB

Course Level

Advanced level

Training Language

English (Optional - Amharic)

Download the Course Outline

Download this course outline for use offline or for other devices.

Research Design, Data Management and Statistical Analysis using SPSS

Upon completion of this SPSS short course on research design, data management and statistical Analysis , the participants will develop competence in quantitative techniques through hands-on practices in study design, data collection, and management, as well as the analysis and interpretation of data.

Target Participants

This SPSS short course is designed for participants who intend to learn how to plan, implement effective research studies including data management analysis. Those who are working in the private sector, government institutions, research institutions and NGOs.

What you will learn

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

• Understand and appropriately use statistical terms and concepts
• Design and Implement universally acceptable research
• Develop of functional research protocol
• Design both quantitative and qualitative data collection tools
• Perform data analysis tasks with SPSS
• Perform simple to complex data management tasks using software
• Statistical tests using SPSS software
• Writing reports from survey data

Course Outline

1. Introduction to research

• Introduction to research
• Different types of research
• Formulation of research problem statement
• Formulation of research hypothesis

2. Overview of Evaluation

• Evaluation Objectives
• Evaluation Criteria
• Evaluation Questions

3. Research Design

• Quantitative Research Approaches
• Qualitative Research Approaches

4. Sampling

• Sampling Techniques
  • Probability
  • Non-probability
• Sample size determination

5. Data Collection Methods in Research

• Quantitative data collection methods
• Qualitative data collection
• Creating an evaluation framework

6. Data Collection tools in Research

• Survey Questionnaire design
• FGD guide design
• KII guide design

7. Developing Research Protocol

• What is a research protocol?
• Basic concepts of a research protocol
• Structure of a research protocol

8. Mobile Data Collection and Processing (ODK)

• Introduction to mobile data gathering
• Design of survey forms using ODK build and XLSForm
• Use ODK collect to gather data
• Use ODK aggregate to upload data to the server
• Work with spatial data (GPS coordinates)

9. 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

10. 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

11. Graphics using SPSS

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

12. Statistical Tests using SPSS

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

13. Statistical Associations in SPSS

• Chi-Square test
• Pearson’s Correlation
• Spearman’s Rank-Order Correlation

14. Predictive Models using SPSS

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

15. Longitudinal Analysis using SPSS

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

16. Qualitative Data Analysis using NVivo

• Introduction to NVivo
• NVivo workspace
• Uploading qualitative data into NVivo
• Coding and making nodes
• Use of queries
• Project visualization

17. Survey Report writing and Dissemination

• Survey report format
• Survey report content
• Survey findings dissemination
• Use of survey findings for decision making

Training Approach

This SPSS short course is delivered by seasoned trainers who have vast experience as expert professionals interacting with SPSS software. The course is taught through a mix of practical activities, theory, group works and case studies.

Training manuals and additional reference materials are provided to the participants.

Certification

Upon successful completion of this course, participants will be issued with a certificate.

Prerequisites

Basic statistical knowledge and prior working knowledge of SPSS software are required for this course.

Tailor-Made Course

We can also do this as tailor-made course to meet organization-wide needs. A training needs assessment will be done on the training participants to collect data on the existing skills, knowledge gaps, training expectations, and tailor-made needs.

Course Duration (classroom-based)

21 Days (8 hours a day)

Payment

9900 ETB

Course Level

Specialization (for researchers)

Training Language

English (Optional - Amharic)

Download the Course Outline

Download this course outline for use offline or for other devices.