SPSS Analytics for Research

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SPSS (Statistical Package for the Social Sciences) is 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. Most top research agencies use SPSS to analyze survey data and mine text data so that they can get the most out of their research projects.

Main Features

  • Master the operation of SPSS and use it confidently
  • Use Measures of Central Tendency and Dispersion in SPSS
  • Build the most useful charts in SPSS: column charts, line charts, scatterplot charts, boxplot diagrams
  • Learn The Different Types of Inferential Statistics in SPSS
  • Understand the assumption of normality and no outlier and the graphical and statistical method to check them in SPSS
  • Perform the main one-sample analyses: one-sample t test, binomial test, chi square for goodness of fit
  • Execute the analyses for means comparison: t test, between-subjects ANOVA
  • Use the main grouping techniques(Cluster Analysis, Factor Analysis)
  • Perform the regression analysis (simple and multiple regression, logistic regression, Time Series)

Introducing IBM SPSS Statistics

1
Introduction to Excel
2
Conditional Formatting
3
General formatting Options
4
SPSS Environment

Familiarization with the SPSS Data Editor

1
Entering Data into the Data Editor
2
Creating a Variable
3
Saving Data Files: SPSS Data File, SAS Data File, Excel File, CSV File
4
Retrieving Data Files: Excel Files, CSV Files, Access File, Database Connection (ODBC)

Summarizing Individual Variables

1
The ‘Variable View’
2
Attributes of a Variable: Name, Type, Width, Decimals, Label
3
Applying Value Labels and Changing the values from the Data View
4
Missing Values: Identification of various missing values
5
Changing the scale of the measurement

Modifying Data Values

1
Recode into Same Variables
2
Recode into Different variables
3
Compute Variables: Type
4
Function Groups

Graphical Presentation of Data

1
Preparing the Data for creating Graphs
2
The Chart Builder: Gallery, Element Properties
3
Legacy Dialogs: Bar, 3DBar, Boxplot, Histogram
4
The Chart Editor: Modifying the color, Data Labels, Interpolation Line

Understanding Output in the Viewer

1
Tree Diagram of Output: The Log and Output
2
Exporting Report to Excel, PDF, PowerPoint, Word
3
Copy Special: Plain Text, Rich Text, Image, Metafile, Excel Worksheet

Helpful Data Management Features

1
Numeric format
2
Date-Time format
3
Identifying Duplicates
4
Aggregating Data

Statistical Analysis and Tests on SPSS

1
Generating Descriptive Statistics: Frequencies Module
2
Descriptive Module, Explore
3
Checking the distribution: P-P Plots, Q-Q Plots
4
Chi Square test of Independence: Understanding the association between two categorical variables
5
Independent Samples T test: Comparing the means along with variance comparison
6
Paired Sample T Test: Comparing the means of same subject
7
One way ANOVA: Comparing means for more than two groups
8
Two Way ANOVA: Type I and III Sum of Squares, Post Hoc Tests

Introduction to Regression Analysis

1
Doing Simple Regression on SPSS: Understanding the unilinear causality
2
Interpreting a Simple Regression on SPSS w.r.t. the assumptions of the Regression
3
Example of a Multiple Regression Model
4
Problem of Multicollinearity
5
Methods of Regression: Forced, Forward, Backward, Stepwise
6
Assessing the Regression Model: Goodness of Fit R Square, Adjusted R Square
7
Multiple Regression using SPSS
8
Interpreting Multiple Regression: Parameter Estimate Table
9
Variance Inflation Factor, Collinearity Diagnostics

Introduction to Logistic Regression

1
Problems related to Logistic regression
2
Nature of Logistic Model: Odds, Odds ratio, Logit Link
3
Concept of Maximum Likelihood Estimation
4
Assessing the Model: The Log-Likelihood Statistic, Cox and Snell’s R Square and Negelkerke R Square
5
Hosmer Lemeshow Goodness of fit test
6
Assessing the Contribution of Predictors: The Wald Statistic
7
Methods of Logistic Regression: Forced Entry Method, Forward LR
8
Classification table: Overall Accuracy
9
Interpreting Logistic Regression

Introduction to CHAID Decision Tree

1
What is a Decision Tree
2
Chi Square Test and CHAID
3
Re-Grouping of Categories of a Variable
4
Finding out the best variable for splitting the node
5
Growth limits of the tree
6
Gain, Response and Index statistics
7
Concept of cross validation
8
Working with the Tree Editor

Bivariate Plots and Correlation for Scale Variables

1
Using Scatter Plots to understand the association between two variables
2
Matrix Scatterplot
3
Correlation Coefficients
4
Partial Correlation: Use in detecting the spurious relations
Faq Content 1
Faq Content 2

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Enrolled: 34 students
Duration: 60 hours
Lectures: 66
Video: 9 hours
Level: Intermediate

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Monday 9:30 am - 7.00 pm
Tuesday 9:30 am - 7.00 pm
Wednesday 9:30 am - 7.00 pm
Thursday Closed
Friday 9:30 am - 7.00 pm
Saturday 9:30 am - 7.00 pm
Sunday 9:30 am - 7.00 pm
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