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Unit 1: Developing the familiarity with SPSS Processer
Entering data in SPSS editor. Solving the compatibility issues with different types of file. 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).
Unit 2: 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, scattorplot (simple, grouped, matrix, drop-line), P-P plots, Q-Q plots, Addressing conditionalities and errors, computing standard scores using SPSS, reporting the descriptive output in APA format.
Unit 3: 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.
Unit 4: 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.
Unit 5: 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
Unit 6: Regression (Linear & Multiple)
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.
Unit 7: Logistic regression
Choosing method (Enter, forward, backward) & covariates, choosing contrast and reference (indicator, Helmert and others), predicted values: probabilities & group membership, Influence statistics: Cook, Leverage values, DfBetas, Residuals (unstandardized, logit, studentized, standardized, devaince), Statics and plot: classification, Hosmer-Lemeshow goodness-of-fit, performing bootstrap, Choosing the right block, interpreting -2loglikelihood, Omnibus test, interpreting contingence and classification table, interpreting Wald statistics and odd ratios. Reporting the output in APA format
Unit 8: Non-parametric tests
When to use, Assumptions, Comparing two independent conditions (Wilcoxon rank-sum test, Mann-Whitney test), 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, Fisher’s exact test, Likelihood ratio, Yates’ correction, Loglinear Analysis. Reporting the output in APA format.
Unit 9: 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, anti-image, explaining the total variance, communalities, eigen-values, scree plot, rotated component matrix, component transformation matrix, factor naming
Lab Work & Project:
All the units will include discussion on theoretical concepts followed by practical SPSS demonstration on real/simulated data. Learners are welcome to bring and discuss their actual problems related to quantitative analysis. Our every learner receives personal attentions and we endeavour to equip every learner to develop a sense of professional competency in quantitative data analysis using SPSS.
© Dr. Sanjay Singh, No part all the syllabus should be reproduced without written permission All right reserved
Dr. Sanjay Singh,
Ph.D., Erasmus Mundus-WILLPower Fellow
Email: firstname.lastname@example.org , M - 99538020219953802021
SPSS Learning Page: www.facebook.com/statistics.india
Dr. Sanjay Singh has been recipient of prestigious Erasmus Mundus-WILLPower fellowship awarded by European Union. During the tenure of his fellowship he studied and worked at the University of Padova, Venice, Italy at the Centre for Risk and Decision-making (CeRD).
Dr. Singh has been a meritorious student throughout his student life and has received 3 Certificates of Academic Merit. He has worked for prestigious institutions like University of Delhi & Indian Institute of Technology, Delhi, and is currently a full time faculty at Asia Pacific Institute of Management, Delhi, one of the top 10 B-Schools in North India. Dr. Singh have been involved in academic teaching and training for over 7 years along with training and consulting to different companies for data analysis, psychometric assessment, and development of research and analytical skill that can enhance the productivity and growth of people and organizations.
Dr. Singh has published in journals of national and international repute and contributed chapters in books published with international publishers. He has trained at reputed organizations like Ernst & Young for IBM SPSS/AMOS and has received excellent rating for his training programs. He has consulted analytics, psychometric and human resource organizations in India and abroad for quantitative project planning, developing customized and culture fair psychometric tests and refinement of quantitative decision models. He has also consulted students and faculty from reputed institutions like London School of Economics, UK, University of Tallin, Estonia, University of Sydney, Australia, and Faculty of Management Studies, University of Delhi on research and analytics related projects.
Dr. Singh strongly believes that learning statistics and research methods can be fun and use of technology in learning can make it even more exciting. He is a dataphilic persona and strong supporter of open source movement in research technology and democratization of research education through the use of technology. As a researcher he loves working at the interface where statistics, human behaviour and technology meet.
Other Courses by Trainer
1. Structural Equation Modelling using IBM AMOS
2. AIM Certified Psychometrician Course
3. Sample size determination using IBM Sample Power
For details regarding these courses please contact to the trainer.
The SPSS / Amos Training Program Evalution Summary Report from Research & Analytics Team, Ernst & Young, Trivendrum, Kerala:
Avarage Rating: 4.47/5
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ranked the Institute 9th amongst all B schools
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