SPSS Training Course Content

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SPSS Training Course Content Details

What is SPSS

SPSS Statistics is a software package used for logical batched and non-batched statistical analysis. Long produced by SPSS Inc., it was acquired by IBM in 2009. The current versions (2015) are officially named IBM SPSS Statistics. Companion products in the same family are used for survey authoring and deployment (IBM SPSS Data Collection), data mining (IBM SPSS Modeler), text analytics, and collaboration and deployment (batch and automated scoring services).

spss training in hyderabad kukatpally

spss training in hyderabad kukatpally

The software name originally stood for Statistical Package for the Social Sciences (SPSS), reflecting the original market, although the software is now popular in other fields as well, including the health sciences and marketing.
SPSS is a widely used program for statistical analysis in social science. It is also used by market researchers, health researchers, survey companies, government, education researchers, marketing organizations, data miners, and others. The original SPSS manual (Nie, Bent & Hull, 1970) has been described as one of “sociology’s most influential books” for allowing ordinary researchers to do their own statistical analysis.In addition to statistical analysis, data management (case selection, file reshaping, creating derived data) and data documentation (a metadata dictionary was stored in the datafile) are features of the base software. For more info click here

SPSS Course Content

01.Developing the familiarity with SPSS Processer

  • Entering data in SPSS editor
  • Solving the compatibility issues with different types of file

02.Inserting and defining variables and cases

  • Managing fonts and labels
  • Data screening and cleaning
  • Missing Value Analysis
  • Sorting
  • Transposing
  • Restructuring
  • Splitting and Merging
  • Compute & Recode functions.
  • Visual Binning & Optimal Binning
  • Research with SPSS (random number generation)

03.Working with descriptive statistics

  • Frequency tables
  • Using frequency tables for analyzing qualitative data Explore
  • Graphical representation of statistical data
  • histogram (simple vs. clustered)
  • boxplot
  • line charts
  • scatterplot (simple, grouped, matrix, drop-line)
  • P-P plots, Q-Q plots

04. Hypothesis Testing

  • Sample & Population, concept of confidence interval
  • Testing normality assumption in SPSS
  • Testing for Skewness and Kurtosis
  • Kolmogorov–Smirnov test
  • Test for outliers Mahalanobis Test
  • Dealing with the non-normal data
  • testing for homoscedasticity (Levene’s test) and multicollinearity.

05. Testing the differences between group means

  • t – test (one sample, independent – sample, paired sample)
  • ANOVA-GLM 1 (one way)
  • Post-hoc analysis, reporting the output in APA format.

06. Correlational Analysis

  • Data entry for correlational analysis
  • Choice of a suitable correlational coefficient
  • non-parametric correlation (Kendall’s tau)
  • Parametric correlation (Pearson’s, Spearman’s)
  • Special correlation (Biserial, Point-biserial)
  • Partial and Distance Correlation

07. Regression

  • The method of Least Squares
  • Linear modeling
  • Assessing the goodness of fit
  • Simple regression
  • Multiple regression (sum of squares, R and R2 , hierarchical, step-wise)
  • Choosing a method based on your research objectives
  • checking the accuracy of regression model. Logistic regression
  • Reporting the output in APA format.

08.Non-parametric tests

  • When to use
  • Assumptions
  • Comparing two independent conditions (Wilcoxon rank-sum test, MannWhitney
  • Several independent groups (Kruskal- Wallis test)
  • Comparing two related conditions (Wilcoxon signed-rank test)
  • Several related groups (Friedman’s anova)
  • Post-hoc analysis in non- parametric analysis
  • Categorical testing: Pearson’s Chi-square test

09.General Linear Models (GLM 1 to 5)

  • Theoretical basis of GLM: Assumptions and practical considerations
  • Comparing several means
  • Factorial Anova
  • Repeated Measure Anova
  • Mixed Design Anova

10.Factor Analysis

  • Theoretical foundations of factor analysis
  • Exploratory and Confirmatory factor analysis
  • testing data sufficiency for EFA & CFA
  • Principal component Analysis
  • Factor rotation
  • factor extraction
  • using factor analysis for test construction
  • Interpreting the SPSS output: KMO & Bartlett’s test
  • initial solutions
  • correlation matrix

SPSS  Training Demo

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Updated: May 4, 2017 — 7:53 am

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