This Course 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.
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
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
Descriptive statistics for numeric variables
Frequency tables
Distribution and relationship of variables
Cross tabulations of categorical variables
Stub and Banner Tables
Introduction to graphs in SPSS
Graph commands in SPSS
Different types of Graphs in SPSS
One Sample T Test
Independent Samples T Test
Paired Samples T Test
One-Way ANOVA
Chi-Square test
Pearson's Correlation
Spearman's Rank-Order Correlation
Bivariate Plots and Correlations for Scale Variables
Linear Regression
Multiple Regression
Logistic Regression
Ordinal Regression
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
Features of Longitudinal Data
Exploring Longitudinal data
Longitudinal analysis for continuous outcomes
The basics of forecasting
Smoothing time series data
Regression with time series data
ARIMA models
Intervention analysis
Introduction to SPSS Decision Trees
Application of SPSS Decision Trees
Overview of decision tree based methods (CRT Decision Trees CRT Regression Trees Quest Analysis)
Perform data analysis tasks with SPSS
Perform simple to complex data management tasks using SPSS
Performing operations with data: define variables, recode variables, create dummy variables, select and weight cases, split files
Building charts in SPSS: column charts, line charts, scatterplot charts, boxplot diagrams
Performing the basic data analysis procedures: Frequencies, Descriptive, Explore, Means, Crosstabs
Testing the hypothesis of normality
Detecting the outliers in a data series
Transform variables
Performing the main one-sample analyses: one-sample t test, binomial test, chi square for goodness of fit
Performing the tests of association: Pearson and Spearman correlation, partial correlation, chi square test for association, log linear analysis
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