Zarqa University

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Zarqa University Faculty of Nursing

Program: Master in Nursing Administration

Course title: Data management and statistical analysis Course number: (0801705)

Credit weight: (3 credit hours)

Course Facilitator:

Lecture’s time:

Semester:

Office Hours:

Course description:

This course deals with the practice of processing and analyzing quantitative data. The course will explore descriptive and inferential statistics with special emphasis on the application of statistics in nursing research. This course combines statistical thinking and understanding, besides theoretical foundations with methodological perspectives, based on practical applications avoiding mathematical demonstrations that would add an unnecessary degree for the goals of the subject.

This course train students the concrete uses of quantitative data analysis in social sciences’

research, and this involves analyzing and evaluating statistical data with a view toward addressing scientific research questions. Students will practice working with and analyzing an actual dataset using SPSS (Statistical Package for the Social Sciences) software. By the end of the course, students should be able to test hypotheses, analyze and interpret statistical results, present data in graphical forms, and perform basic statistical analysis using SPSS.

Aims of the course:

Upon completion of this course learners should be able to:

1. Explain the basic concepts related to statistics 2. Use properly the vocabulary of Statistics.

3. Identify which types of variables are suitable for the quantitative analysis.

4. Identify which statistical tests are suitable for the variables of interest.

5. Describe the scope of statistics in health and nursing.

6. Organize, tabulate and present data meaningfully.

7. Draw conclusions of the study and predict statistical significance of the results.

8. Describe vital health statistics and their use in health related research.

9. Explain the relevance of statistics for research and practice in nursing.

10. Evaluate and use descriptive and inferential statistics.

11. Use descriptive and inferential statistics to predict results.

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12. Analyze and interpret data using the appropriate statistical strategies.

13. Identify statistical tests that may be needed for evaluation of research projects.

14. Collect, analyze and represent quantitative data at analytical and graphical level.

Intended Learning Outcomes (ILOs):

At the end of this course, the student will be able to:

A. Knowledge and Understanding

1. Understand the basic statistical concepts and their application to healthcare and nursing research.

2. Differentiate between parametric and nonparametric tests and comprehend their underlying assumptions.

3. Comprehend the conceptual basis of statistical inferences.

4. Decide what statistical technique will provide the best answer to a given research question.

5. Develop the necessary computer skills using the SPSS in order to conduct basic statistical analyses.

B. Intellectual Skills

1. Discuss the practical importance of key concepts of probability, inference, systematic error, sampling error, measurement error, hypothesis testing, type I and type II errors and

confidence bounds.

2. Realize the roles biostatistics serves in health and nursing research.

3. Comprehend general principles of study design and its implications for valid inference.

4. Identify the importance of data management and data analysis in developing the nursing science.

C. Professional Skills

1. Describe the role of the statistics in nursing research.

2. Assess data sources and data quality for the purpose of selecting appropriate statistical tests for specific research questions

3. Use SPSS package to run statistical tests that most appropriate to the research questions and hypotheses.

4. Interpret the tests results, confidence intervals for population means and proportions.

5. Interpret the statistical significance, and explain the p-values.

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6. Critically analyze and critique selected quantitative research reports and make judgment on the accuracy of the statistical techniques employed on those reports.

7. Interpret output from the SPSS package related to the various estimation and hypothesis testing procedures covered in the course.

D. Transferable Skills

1. Translate research objectives into clear, testable statistical hypotheses.

2. Project possible implications of statistical analysis in supporting the evidence based practice.

3. Critical Appraisal of research study findings and generalizability of findings.

4. Apply strategies that improve understanding the data collected through different measurement tools.

5. Conceptualize the principles and assumptions of using each statistical test.

6. Critique research studies and appraising their data analysis.

7. Design a data management plan for collected data.

Course structures:

Week C.

Hrs ILOs Topics Teaching Procedure Assessment

methods

1 14, Oct.

2018

3 hrs

A:1-5 B: 1-4 C:1-7

 Orientation and

introduction to the course, resources and materials.

 Using Research and Statistics in Health Care.

 Self-study activities

 Group discussion

 Lecturing

 Critical analysis of learning material

 Assigned readings

 Interactive discussion

 Case studies

 Presentations

 Assignments

 Term papers

• Complete readings

2 21, Oct.

2018

3 hrs

A:1-5 B: 1-4 C:1-7

 Introduction to statistics.

 Types of Variables (nominal, ordinal, interval).

 Common Terms (dataset, population sample, parameter, statistic).

 Basic Concepts in Statistics.

 Lecturing

 Seminar &

Presentations

 Weekly assigned readings

 Case studies

 Presentations

 Assignments

 Term papers

 Exams Complete readings

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3 28, Oct.

2018

3 hrs

A:1-5 B: 1-4 C:1-7 D: 1-7

 Introduction to SPSS for Windows

 Data coding and data entry

 Starting an SPSS Session

 Creating a New Dataset

 Using an Existing Dataset

 SPSS application.

 Lecturing

 Seminar &

Presentations

 SPSS application

 Case studies

 Presentations

 Assignments

 Term papers

4 4, Nov.

2018

3 hrs

A: 1-7 B:2,3 C:3,4-7

D: 2-5

 Organizing and presenting data: Tables and frequency distribution, Histograms,

& graphic representations.

 Shapes of Distributions:

Modality, Symmetry, Skewness, & Kurtosis

 The Normal Distribution:

Area Under the Normal Curve

 Lecturing

 Seminar &

Presentations

 Case studies

 Presentations

 Assignments

 Term papers

 Exams Complete reading

5 11, Nov.

2018

3 hrs

A: 2-5 B: 1-4 C:3-5 D: 1-5

 Univariate (descriptive) Statistics

 Range

 Measures of Central Tendency and Dispersion

 Means, medians, modes

 Variance, standard deviation

 Lecturing

 Seminar &

Presentations

 Case studies

 Presentations

 Assignments

 Term papers

6 18, Nov.

2018

3 hrs

A: 1-5 B: 1-4 C:3-7 D: 1-5

 Building Blocks for Using Inferential Statistics

 Inferential Statistics:

Finding Relationships in the Data

 Lecturing

 Seminar &

Presentations

 Case studies

 Presentations

 Assignments

 Term papers

7 25, Nov.

3 hrs

A: 1-5 B:1-4 C:3,4

Comparing the Means of Two Unrelated Groups:

 The Independent t-Test

 Interactive

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2018 D: 2-6

 The Mann-Whitney U- Test

 SPSS application.  Lecturing

 Seminar &

Presentations

discussion

 Case studies

 Presentations

 Assignments

 Term papers

8 2, Dec.

2018

3 hrs

A: 1-5 B: 1-4 C: 3,4 D: 2-5

Comparing the Means of Two Related Groups:

 The Paired t-Test

 The Wilcoxon Matched- Pairs Test

 Lecturing

 Seminar &

Presentations

 Case studies

 Presentations

 Assignments

 Term papers

9 9, Dec.

2018

3 hrs

A: 1-5 B: 2-4 C:1-5 D: 2-5

Comparing the Means of Three or More Unrelated Groups

 The One-Way ANOVA

 Kruskal-Wallis H-Test

 Midterm Exam

 Lecturing

 Seminar &

Presentations

 Case studies

 Presentations

 Assignments

 Term papers

10 16, Dec.

2018

3 hrs

A: 2-5 B: 1-4 C:1-5 D: 1-7

Comparing the Means of Three or More Related Groups:

 The Repeated-Measures ANOVA

 The Friedman’s ANOVA by Rank

 Lecturing

 Seminar &

Presentations

 Case studies

 Presentations

 Assignments

 Term papers

11 23, Dec.

2018

3 hrs

A:1-5 B: 1-4 C:1-5 D: 2-5

Measuring the Association of Two Variables

 The Pearson Correlation Coefficients

 Spearman Correlation Coefficients

 Lecturing

 Seminar &

Presentations

 Case studies

 Presentations

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 Assignments

 Term papers

12 30, Dec.

2018

3 hrs

A: 1-5 B: 1-4 C:1-7 D: 2-7

Examining Relationships Between Categorical Variables

 The Chi-Square Statistic

 The McNemar Test

 Lecturing

 Seminar &

Presentations

 Case studies

 Presentations

 Assignments

 Term papers

13 6, Jan.

2019

3 hrs

A: 1-5 B: 1-4 C:1-7 D: 2-7

Model Building and prediction

 Linear Regression

 Logistic Regression

 Lecturing

 Seminar &

Presentations

 Case studies

 Presentations

 Assignments

 Term papers

14 19, Jan.

2019

3 hrs

A: 1-5 B: 1-4 C:1-7 D: 1-7

Group presentations  Group discussion

 Seminar &

Presentations

 Presentations Complete readings

References:

 Required Textbooks:

- Dancey, C., Reidy, J., & Rowe, R. (2012). Statistics for the Health Sciences: A non- mathematical introduction. Sage Publications.

- Plichta, S. B., & Garzon, L. S. (2009). Statistics for nursing and allied health. Lippincott Williams & Wilkins.

- Bowers, D. (Ed.). (2014). Medical statistics from scratch: an introduction for health professionals. John Wiley & Sons.

 Additional references:

- Heavey, E. (2015). Statistics for nursing: A practical approach. Jones & Bartlett Publishers.

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- Kellar S.P & Kelvin E.A (2013). Munro's Statistical Methods for Health Care Research (6^thedition). Lippincott Williams & Wilkins, New York.

- Warner, R. M. (2012). Applied statistics: from bivariate through multivariate techniques: from bivariate through multivariate techniques. Sage.

- Wagner III, W. E. (2016). Using IBM® SPSS® statistics for research methods and social science statistics. Sage Publications.

- Leech, N. L., Barrett, K. C., & Morgan, G. A. (2014). IBM SPSS for intermediate statistics: Use and interpretation. Routledge.

- Polit, D., & Beck, C. (2017). Nursing Research: Generating and Assessing Evidence for Nursing Practice (10^th ed.). Philadelphia: Lippincott Williams & Wilkins.

- American Psychological Association. (2010). Publication manual of the American Psychological Association (6^th ed.). Washington, DC: Author.

Assessment Methods:

Methods Grade Date

Classroom participation & discussion 15% Each class

Midterm Exam 20% Week 9

Critical appraisal paper & Assignments 20% Week 13 Group assignment & Presentation 15% Week 14

Final Exam 30% Will be announced

Course Requirements:

It is expected that students will come prepared for class by having completed assigned readings.

The facilitator role is to create a learning environment to help the students understand and apply the course content.

Attendance & Class Participation:

Attendance and class participation is a requirement for completion of the course. All students are required to contribute to the discussion. Any absent will lead to lose 2% from professionalism / participation.

Presentation:

The students are required to prepare for a presentation and demonstrate an understanding of the presented material.

Plagiarism Policy:

Plagiarism is copying extensive passages verbatim without adequate referencing to the source of the material, and this is a serious academic offence.

As an academic offence, plagiarism carries a penalty. The severity of the penalty depends upon the case and degree of plagiarism. Penalties include awarding a mark of zero for the manuscript or assignment, or even failure in the module as a whole.

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Assignments:

1. Reading Assignment and class activity: The students are required to complete the reading assignments and participate effectively in class discussions.

2. Critical appraisal paper: The student is expected to critically analyze the data analysis section in a research report. The critical appraisal paper is an individual assignment.

3. Group presentation: Students are required to prepare an assignment related to proposed research question/hypothesis. Then present the results in group. For example: each three students will constitute a presentation group.