Objective of the course The main objective is to teach students how to conduct quantitative data analysis in SPSS for research purposes.


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2 COURSE DESCRIPTION The course Data Analysis with SPSS was especially designed for students of Master s Programme System and Software Engineering. The content and teaching methods of the course correspond to the standards and requirements for similar courses at leading western universities. Objective of the course The main objective is to teach students how to conduct quantitative data analysis in SPSS for research purposes. Goals of the Course As a result of study, the student should know how to: enter data in SPSS and import data from different sources; carry out preliminary screening of the data in SPSS; choose the method of statistical data analysis in SPSS which correspond to the research question; construct models, formulate statistical hypotheses; perform data analysis is SPSS; interpret the results of data analysis; report research results to the audience. The course is aimed at developing the following competences of the students: ability to select and develop suitable methods to analyze the objects of professional activity; ability to organize individual and group research work; ability to realize projects in the professional area using systems approach tools, create and use models to describe and forecast different phenomena; 2
3 ability to present research results to the wide audience. THEMATIC PLAN OF THE COURSE Total Hours in classroom Home hours Theme in Lectures work Seminars theme 1 SPSS environment Working with data in SPSS Descriptive Statistics Investigating relationship between variables. 5 Regression analysis Factor analysis Cluster analysis TOTAL COURSE CONTENT Theme 1. SPSS environment Different versions of SPSS; Data editor; Output viewer; Menu structure; Toolbars; Opening and saving files; Tutorial and help system. 3
4 Readings 1. Field A. Discovering Statistics Using SPSS (2nd edition). London: Sage, Kremelberg D. Practical statistics: a quick and easy guide to IBM SPSS Statistics, STATA, and other statistical software. Los Angeles [etc.]: Sage Publications, Griffith A. SPSS For Dummies (2nd Edition). Wiley Publishing Inc., Wagner III, W. E. Using SPSS for social statistics and research methods. London Pine Forge Press, Morgan, G. A. IBM SPSS for introductory statistics. London; New York Routledge, Theme 2. Working with data in SPSS Exploring data; Entering new data; Coding variables; Types of variables and their characteristics; Preparing data for analysis; Export/import of the data; Visualizing data; Modifying data: recoding and calculating new variables, aggregating and ranking cases; Selecting data; Sorting data; Creating sets of variables; Splitting data. 4
5 Readings 1. Field A. Discovering Statistics Using SPSS (2nd edition). London: Sage, Kremelberg D. Practical statistics: a quick and easy guide to IBM SPSS Statistics, STATA, and other statistical software. Los Angeles [etc.]: Sage Publications, Pete Greasley. Quantitative Data Analysis Using SPSS. An Introduction for Health & Social Science. New York: Open University Press, Robert Ho. Handbook of Univariate and Multivariate Data Analysis and Interpretation with SPSS. Chapman & Hall/CRC Taylor & Francis Group, Wagner III, W. E. Using SPSS for social statistics and research methods. London Pine Forge Press, Morgan, G. A. IBM SPSS for introductory statistics. London; New York Routledge, Theme 3. Descriptive Statistics Frequency analysis; Graphical analysis: histogram, boxplot, pie chart, stem and leaf plot; Calculating statistical characteristics: central tendency estimations, dispersion, standard deviation, standard error of mean, confidence interval, percentile values, measuring symmetry and pointiness of distribution. Normal distribution, Zstandardization, KolmogorovSmirnov test of normality; Working with multiple response questions. Readings 1. Field A. Discovering Statistics Using SPSS (2nd edition). London: Sage, Kremelberg D. Practical statistics: a quick and easy guide to IBM SPSS 5
6 Statistics, STATA, and other statistical software. Los Angeles [etc.]: Sage Publications, Greasley P. Quantitative Data Analysis Using SPSS. An Introduction for Health & Social Science. New York: Open University Press, Morgan, G. A. IBM SPSS for introductory statistics. London; New York Routledge, Robert H. Handbook of Univariate and Multivariate Data Analysis and Interpretation with SPSS. Chapman & Hall/CRC Taylor & Francis Group, Wagner III, W. E. Using SPSS for social statistics and research methods. London Pine Forge Press, Theme 4. Investigating relationship between variables Crosstabs; Formulation and testing hypothesis; Level of significance and first type error; Chisquare test; Scatterplot; Correlation coefficients: bivariate, part and partial; Ttests; ANOVA; Nonparametric tests; Readings 1. Field A. Discovering Statistics Using SPSS (2nd edition). London: Sage, Kremelberg D. Practical statistics: a quick and easy guide to IBM SPSS Statistics, STATA, and other statistical software. Los Angeles [etc.]: Sage Publications, Greasley P. Quantitative Data Analysis Using SPSS. An Introduction for 6
7 Health & Social Science. New York: Open University Press, Morgan, G. A. IBM SPSS for introductory statistics. London; New York Routledge, Robert H. Handbook of Univariate and Multivariate Data Analysis and Interpretation with SPSS. Chapman & Hall/CRC Taylor & Francis Group, Wagner III, W. E. Using SPSS for social statistics and research methods. London Pine Forge Press, Theme 5. Regression analysis Objectives of regression analysis; Graphical representation of regression line; Simple and multiple linear regression; Logistic regression; Regression model limitations and diagnostics. Readings 1. Field A. Discovering Statistics Using SPSS (2nd edition). London: Sage, Kremelberg D. Practical statistics: a quick and easy guide to IBM SPSS Statistics, STATA, and other statistical software. Los Angeles [etc.]: Sage Publications, Robert H. Handbook of Univariate and Multivariate Data Analysis and Interpretation with SPSS. Chapman & Hall/CRC Taylor & Francis Group, Wagner III, W. E. Using SPSS for social statistics and research methods. London Pine Forge Press, Tabachnick B.G., Fidell L.S. Using Multivariate Statistics. Pearson Education. Inc Boston, MA, Vijay Gupta. SPSS for beginners. VJBooks Inc,
8 Theme 6. Factor analysis Steps of performing factor analysis; Evaluating applicability of data for factor analysis; Methods of factor analysis; Factor loading, rotation; Saving factors as new variables; Interpreting factors. Readings 1. Field A. Discovering Statistics Using SPSS (2nd edition). London: Sage, Kremelberg D. Practical statistics: a quick and easy guide to IBM SPSS Statistics, STATA, and other statistical software. Los Angeles [etc.]: Sage Publications, Robert H. Handbook of Univariate and Multivariate Data Analysis and Interpretation with SPSS. Chapman & Hall/CRC Taylor & Francis Group, Theme 7. Cluster analysis Steps of performing cluster analysis; Evaluating applicability of data for cluster analysis; Methods of cluster analysis: hierarchical and kmeans; Saving cluster membership information as new variable; Characterizing clusters. Readings 1. Robert Burns & Richard Burns. Business Research Methods and Statistics using SPSS 2. Marija J. Norušis. IBM SPSS Statistics 19 Statistical Procedures Com 8
9 panion Control questions 1. What are the main characteristics of variables in SPSS? What is the difference between nominal, ordinal and scale variables? 2. What is the difference between a Data View sheet and a Variable View sheet in SPSS? List the main activities that could be done in SPSS with the usage of each sheet. 3. What is the difference between numeric and string variables in SPSS? 4. What is the difference between Label of the variable and Label of the variable s value in SPSS? 5. What is the purpose of identifying Missing Values in SPSS? 6. How to code multiple choice questions in SPSS? 7. What is the purpose of KolmogorovSmirnov test? 8. What is the purpose of Chisquare test? 9. What is the difference between different types of correlation coefficients? 10. What is the difference between bivariate and partial correlation coefficients? 11. For what purposes regression analysis can be used? What are the data requirements for using it? 9
10 12. What types of ttests do you know and what are the differences between them? 13. For what purposes factor analysis can be used? What are the data requirements for using it? 14. For what purposes cluster analysis can be used? What are the data requirements for using it? Control tasks 1. Do the frequency analysis of the variable. 2. Calculate for the variable the following statistical characteristics: mode, median, mean, range, standard deviation, S. E. mean, interquartile range, quartile deviation, decile ratio. 3. Evaluate the symmetry of distribution of the variable. Indicate whether the distribution is positively or negatively skewed and what does it mean in terms of the shape of the distribution. Indicate is the distribution significantly different for the symmetrical distribution and the reason of your conclusion. 4. Evaluate the pointyness of distribution of the variable. Indicate whether the distribution is leptokurtic or platykurtic and what does it mean in terms of the shape of the distribution and is the distribution significantly different for the normal distribution and the reason of your conclusions. 5. Create and interpret a boxplot for the variable. 6. Create a frequency table for a multiple response question. Interpret the results. 10
11 7. Do the KolmogorovSmirnov test to conclude whether the distribution of the variable is significantly different from the normal. Formulate hypothesis. Make conclusions. 8. Create a contingency table between two variables and interpret the results. 9. Select two pairs of categorical variables to run Chisquare statistical test. Formulate hypotheses. Interpret the results of analysis. Make conclusions. 10. Do the bivariate correlation analysis. Calculate Pearson s, Kendall s and Spearman s correlation coefficients. Evaluate the significance of the coefficients. Indicate the coefficient of determination for every coefficient. Interpret the results. 11. Do the partial correlation analysis. 12. Do the simple regression analysis. Select the appropriate variables. Write down the regression equation. Assess the goodnessoffit of the model. Do the diagnostics of the model. Are the residuals normally distributed? Delete the cases than have standardized residuals greater than 3. Will this improve the model? 13. Do the multiple regression analysis using at least two predictors. Select the appropriate variables. Write down the regression equation. Assess the goodnessoffit of the model. Are all the gradients and intercepts of the model statistically significant? Do the diagnostics of the model. Are the residuals normally distributed? Test the multicollinearity. 11
12 14. Do the factor analysis. Interpret the factors and save them as new variables. Use saved factors for cluster analysis. Define the number of clusters. Describe the clusters characteristics. Methods of evaluation Continuous: regular examination of students during the course; return to the given material when needed; Final: analyzing a data set, formulating hypotheses and formulation of conclusions on the obtained results (60 minutes). Grading The grade consists of the three elements: Class attendance and active participation 30% Regular tasks in class 30% Final task 40% 12
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