STATISTICAL COMPUTER APPLICATIONS FOR SOCIAL SCIENCES

Course Description

Entering the data collected to the computer, organizing and getting data ready for statistical analyses; normal distribution and sampling distribution of the mean; establishing confidence intervals; writing research questions, research hypotheses, null hypotheses; hypothesis testing. Descriptive statistics: Measures of central tendency and position measurements (arithmetic mean, median, peak, percentile and quartile); measures of variability (distribution width, variance, standard deviation); display of data. Inferential statistics: t-test applications (single-sample t-test, independent samples t-test, dependent samples t-test), analysis of variance applications (one-way ANOVA, two-way ANOVA, MANOVA, ANCOVA); correlations (Pearson and Spearman correlation coefficients), partial correlation; multiple linear regression; exploratory and confirmatory factor analyses; reliability; non-parametric tests (Wilcoxon signed rank test, Mann-Whitney U test, Kruskal-Wallis test, Chi-Square tests); interpreting and writing results of analyses.

STATISTICAL COMPUTER APPLICATIONS FOR SOCIAL SCIENCES

Evaluation Tools (Active Term)

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Course outcomes

1. 01 have a knowledge of basic concepts and terms related to descriptive and inferential statistics; be able to enter data collected to the computer, organize and get data ready for statistical analyses.
2. 02 comprehend normal distribution and sampling distribution of the mean and be able to establish confidence intervals; be able to write research questions, research hypotheses, null hypotheses; and comprehend hypothesis testing.
3. 03 be able to compute measures of central tendency and position measurements (arithmetic mean, median, mode, percentile and quartile) and measures of variability (range, variance, standard deviation) by using SPSS; be able to display data and edit graphs and charts by using SPSS.
4. 04 be able to use t-test applications on SPSS and conduct one-sample t-test, independent samples t-test and paired samples t-test.
5. 05 be able to use Analysis of Variance applications on SPSS and conduct one-way ANOVA, two-way ANOVA and ANCOVA.
6. 06 be able to plot scatter dot diagram and compute Pearson and Spearman correlation coefficients; be able to conduct multiple linear regression analysis and build models.
7. 07 be able to conduct exploratory factor analysis and reliability analyses.
8. 08 be able to conduct non-parametric tests (Wilcoxon signed rank test, Mann-Whitney U test, Kruskal-Wallis test, Chi-Square tests)
9. 09 be able to interpret and write results of analyses.
10. 10 value statistics as a vital component of doing quantitative research.

Course Syllabus

Reference Books & Course Materials

1. 01 Green, S. B., Salkind, N. J. (2014). Using SPSS For Windows And Macintosh -7TH Edition. Boston: Pearson.
2. 02 Elzey, F. F. (1985). Elementary Statistical Techniques. California: Brooks/Cole.
3. 03 Coursepack prepared by the instructor.

Learning Outcomes

No learning outcomes have been defined.

Program Outcomes

1. Demonstrate mastery of advanced research methodologies (quantitative, qualitative, and mixed methods) by critically analyzing literature, identifying research gaps, and designing original studies that contribute to MIS theory and practice.
2. Conduct and defend an original doctoral dissertation that reflects independent scholarly inquiry, academic rigor, and a significant contribution to the advancement of knowledge in MIS.
3. Exhibit readiness for thesis monitoring and defense by articulating the philosophical foundations of research paradigms, positioning one's research within these frameworks, and responding to scholarly critique.
4. Apply ethical principles, academic integrity, and responsible conduct of research in all phases of the research process, including data collection, analysis, reporting, and publication.
5. Identify and address the social, legal, and ethical implications of information systems research and its applications within organizational and societal contexts.
6. Employ advanced data science techniques, including statistical modeling, machine learning, and AI-based analytics, to examine complex datasets and extract meaningful insights in MIS research.
7. Recognize and evaluate emerging technologies such as artificial intelligence, big data, blockchain, and the Internet of Things, assessing their transformative impact on organizational processes and digital ecosystems.
8. Collaborate and lead in interdisciplinary research environments, establishing productive scientific partnerships and managing research projects that integrate diverse academic perspectives.
9. Publish high-quality research in peer-reviewed journals, present findings at international conferences, and actively engage in academic service such as journal reviewing, conference organizing, and committee participation.
10. Identify challenges and propose innovative, research-based solutions at the intersection of information systems, technology, and organizational strategy.
11. Deliver advanced-level MIS courses, supervise graduate research, and nurture academic development through effective mentorship and scholarly teaching.
12. Develop advanced information systems and decision support systems that align IT capabilities with organizational strategies using systems thinking and innovative design methodologies.

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