SPSS is short for Statistical Package for the Social Sciences, and it’s used by various kinds of researchers for complex statistical data analysis. The SPSS software package was created for the management and statistical analysis of social science data. It was was originally launched in 1968 by SPSS Inc., and was later acquired by IBM in 2009. Officially dubbed IBM SPSS Statistics, most users still refer to it as SPSS. As the world standard for social science data analysis, SPSS is widely coveted due it’s straightforward and English-like command language and impressively thorough user manual. SPSS is used by market researchers, health researchers, survey companies, government entities, education researchers, marketing organizations, data miners, and many more for the processing and analyzing of survey data.
This course is intended for Data Analysts, Data Scientists, Business Intelligence Analysts and any other professional who want to explore the vast range of analytical and graphical capabilities of SPSS.
1. Perform univariate and bi-variate data analyses including hypotheses testing using IBM SPSS Statistics
2. Master R and R Studio user interface
3. Interpret the output and draw appropriate conclusions from the data
4. Produce high quality output (e.g. charts & tabulations) to report your findings and transfer this output to word processing applications
5. Define the time series data in preparation for analysis
6. Apply “Pure” time series models & Apply ARIMA modelling
- open Source
- R provides exemplary support for data wrangling. The packages like dplyr , readr are capable of transforming messy data into a structured form.
- With packages like Shiny and Markdown, reporting the results of an analysis is extremely easy with R. You can make reports with the data, plots and R scripts embedded in them. You can even make interactive web apps that allow the user to play with the results and the data.
R is a programming language and environment commonly used in statistical computing, data analytics and scientific research.
Background in Statistics is an added advantage but any one with passion to be a top quantitative researcher is encouraged to apply.
This course aims to build the capacity of quantitative research in quantitative data management and analysis using SPSS. This software has many features that make it the most endearing quantitative data analysis software available, given it’s open source nature.
We shall be looking at the basic steps of the research process
1. Explain the basic steps of the research process .
2. Explain differences between populations and samples .
3. Explain differences between experimental and non-experimental research designs.
4. Explain differences between independent and dependent variables .
5. Overview of the SPSS Software Interface.
6. Key Terminologies, Views, Data Preparation and Data entry.<
7. Data Manipulation i.e Merging, Sorting, Spliting data, working with missing values.
8. Descriptive statistics for numeric variables
9. Frequency tables
10. Distribution and relationship of variables
11. Cross tabulations of categorical variables
12. Stub and Banner Tables
13. Identifying Duplicates and Restructuring Data
13. Computing with Date and Time Variables
14. Analysing Multiple Response Questions with crosstabs and frequencies
17. Comparing categorical variables.
18. Mean differences between groups: T test.
Drawing and Interpreting Graphs
1. Introduction to Graphs
2. Graph commands in SPSS
3. Different types of Graphs in SPSS
4. Working with Pivot Tables and charts
6. One Sample T Test
7. Independent Samples T Test
8. Paired Samples T Test
9. Introduction to ANOVA
10. One-factor ANOVA
11. Multi-way univariate ANOVA
12. Analysis of covariance
Descriptive statistics, Inferential statistics
1. The learner will be taken through S’s powerful commands for calculation of measures of central tendency (mean, mode and median) and their confidence intervals
2. Skewness and kurtosis, proportions and frequencies and interpretations of the same.
3. Introduction to parametric and non-parametric methods of data analysis and the concepts underlying each. This will be accompanied by a rigorous hands-on insight into the various forms of regressions (linear, logistic, and probit) and their related concepts including model goodness of fit
4. R square, marginal effects and sensitivity and specificity, odds ratios for the case of binary logistic regression
5. Correlations (Pearson’s r, Spearman’s rho, Kendall’s tau, Cramer’s v and phi)
6. Tests of associations (Chi-square, Fisher’s exact, McNemar)
7. Tests of hypothesis (ANOVA and its post hoc tests, t-tests, Kruskal-Wallis, Mann-Whitney U, and Cochrane’s Q)
learn to analyze longitudinal data using survival analysis approaches and be able to visualize the analysed data using various charts
The participants will learn to analyze longitudinal data using survival analysis approaches that include non-parametric (Kaplan-Meier, Nelson-Aalen); semi-parametric (cox-proportional hazards) and its assumptions; parametric (lognormal, exponential, Weibull, gaussian) and interpretations of Hazard ratios and risk ratios obtained from generalized estimating equations (GEE).
The participant will be able to visualize – continuous variables in histograms, box plots, scatter plots, density plots and line plots; categorical variables in the form of bar graphs and pie-charts; sensitivity and specificity in the form of Receiver Operating Characteristic (ROC) curves.
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.
Accommodation will be arranged for our participants upon request.
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