# THE UNIVERSITY OF TEXAS AT TYLER COLLEGE OF NURSING COURSE SYLLABUS NURS 5317 STATISTICS FOR HEALTH PROVIDERS. Fall 2013

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1 THE UNIVERSITY OF TEXAS AT TYLER COLLEGE OF NURSING 1 COURSE SYLLABUS NURS 5317 STATISTICS FOR HEALTH PROVIDERS Fall 2013 & Danice B. Greer, Ph.D., RN, BC Office BRB 1115 (903) Office Hours By Appointment

2 COURSE SCHEDULE NURS 5317 Fall Date Chapters Covered Reading (Gravetter & Wallnau)* Assignment for a grade 8/26 Ch. Introduction Ch. 1 Quiz 1 9/1 9/2 Ch. Frequency Distributions Ch. 2 Quiz 2 9/8 9/9 Ch. Central Tendency Ch. 3 Quiz 3 9/15 9/16 Ch. Variability Ch. 4 Quiz 4 9/22 Exam #1 (Chaps. 1-4) 9/22/2013 9/23 Ch. z-scores Ch. 5 Quiz 5 9/29 9/30 Ch. Probability Ch. 6 Quiz 6 10/6 10/7 Ch. Probability and Samples Ch. 7 Quiz 7 10/13 10/14 Ch. Introduction to hypothesis testing Ch. 8 Quiz 8 10/20 Exam #2 (Chaps. 5-8) 10/20/ /21 Ch. Introduction to the t statistic Ch. 9 Quiz 9 10/27 10/28 Ch. t-test for two independent samples Ch. 10 Quiz 10 11/3 11/4 Ch. t-test for two related samples Ch. 11 Quiz 11 11/10 Assignment due date **Every Sunday 11:59pm CST Exam #3 (Chaps. 9-11) 11/10/ /11 Ch. Estimation Ch. 12 Quiz 12 11/17 11/18 Ch. ANOVA Ch. 13 Quiz 13 11/24 11/25-12/1 Thanksgiving Break- ~No quiz or assignments due ~ 12/2 Ch. Correlation and Regression Ch. 15 Quiz 14 12/8 Exam #4 (Chaps. 12, 13, & 15) 12/8/2013 * All readings are required ** Assignments, quizzes, and exams will be due by 11:59 p.m. central time on the date shown. Availability of assignments can be found in Blackboard.

3 3 COURSE DESCRIPTION NURS 5317: Graduate Statistics for Health Professionals is a three credit graduate course with several goals that are intended to help students understand fundamental concepts and methods of statistics as applied to health related fields. The topics of this course include: introduction to basic concepts of descriptive and inferential statistics as they apply to health research; basic and intermediate concepts for testing statistical hypotheses, construction and interpretation of confidence intervals; and applying selected nonparametric techniques. Prerequisite: Undergraduate statistics course Course hours/credits: 3 hours COURSE OBJECTIVES By the end of the course, each student will be able to: 1. Demonstrate familiarity with statistical terminology and the purpose of statistics: When, how, and why they are used. 2. Organize data into regular or grouped frequency distribution tables, construct graphs, histograms, and polygons, and describe the shape of a distribution. 3. Define and compute measures of central tendency and variability including the mean, median, mode, variance, sum of squared deviations and standard deviation for both samples and populations. 4. Describe and understand the purpose for z-scores and the effects of standardizing a distribution through transforming raw scores, and demonstrate the process for transforming X values into z-scores and z-scores into X values. 5. Distinguish the link between z-scores, probability, frequency distributions, and inferential statistics. 6. Exhibit understanding the logic of hypothesis testing including the concepts of error (Type I and Type II), directional and non-directional hypothesis testing, effect size, Cohen s d and what it measures, power for a hypothesis test and factors that influence power. 7. List the assumptions that must be satisfied for parametric statistical tests. 8. Differentiate experimental situations for which an independent-measures t statistic, repeated-measures t statistic, or analysis of variance (ANOVA) is appropriate and the associated advantages and disadvantages of each design. 9. Recognize when post hoc tests are necessary and complete an analysis of variance using Tukey s HSD or the Scheffe post hoc test. 10. Report the results of an ANOVA using a summary table and an F-ratio, and interpret these reports when they appear in scientific literature. 11. Compute the Pearson correlation by the regular and z-score formulas, understand what aspect of a relationship Pearson correlation measures.

7 CONTENT OBJECTIVES 7 Note: Students will be required to read appropriate content, participate in discussion forums for clarification and discussion, and post assignments as directed. Lessons are organized according to the content for each week and include Powerpoint lectures, discussion forums, and quizzes each week. Additionally, exams will cover three to four instructional lessons and allocated to integrated content. Please refer to the annotated course schedule for weekly content. Lesson 1: Course Introduction, Introduction to Statistics Introduce the basic terminology that will be used in statistics Explain how statistical techniques fit into the general process of scientific research Introduce some of the notation that will be used throughout the course differentiation of populations and samples, descriptive versus inferential statistics, parametric versus nonparametric procedures, and parameters and statistics Lesson 2: Frequency Distributions and Tables, Graphs, and Distribution Shapes Introduce the concept of a frequency distribution as a descriptive statistical technique Describe how a frequency distribution can be presented in a regular or grouped table Describe how a frequency distribution can be presented in a graph Transformation of data into percentiles ranks and graphical description of frequencies using stem and leaf and box-and-whiskers displays Lesson 3: Measures of Central Tendency Introduce central tendency as a descriptive statistical procedure Introduce the three basic measures of central tendency: the mean, the median, and the mode Describe how measures of central tendency are related to the shape of a distribution Describe how central tendency is presented in research reports, including presentation in graphs Lesson 4: Variability Introduce the concepts of variability and describe its importance as a descriptive measure and component of inferential statistics Introduce the basic techniques for measuring and describing variability Demonstrate the properties of the standard deviation Demonstrate how the mean and standard deviation, working together, can provide a detailed description for a set of scores Lesson 5: z-scores Introduce z-scores as a method for describing exact locations in a distribution Demonstrate how raw scores (X values) are transformed into z-scores, and how to reverse the transformation Demonstrate the concept of standardizing a distribution by transforming all of the scores into z- scores Demonstrate how z-scores can be used to create a standardized distribution with any predetermined values for the mean and standard deviation. Lesson 6: Probability Introduce the concept of probability Demonstrate how the unit normal table can be used to find proportions of a normal distribution corresponding to any specific z-score location Demonstrate how the combination of z-scores and the unit normal table can be used to find probabilities on any score (X value) from a normal distribution, and how the table can be used to find the score corresponding to any specific proportion Demonstrate how probability will be used as part of the inferential statistics in future chapters

8 8 Lesson 7: The Distribution of Sample Means Introduce the distribution of sample means and identify its parameters Demonstrate how z-scores can be used to identify the location of a specific sample mean within the distribution of sample means Demonstrate how the unit normal table can be used to find probabilities for sample means Introduce the concept of standard error as an integral component of inferential statistics Lesson 8: Hypothesis Testing Introduce the statistical technique of hypothesis testing Introduce the concepts of null hypothesis, alpha level, and critical region Introduce the types of errors that can occur in a hypothesis test Demonstrate the differences between one-tailed and two-tailed tests Introduce the concept of effect size as a supplement to a hypothesis test Introduce the concept of power for a hypothesis test Lesson 9: Introduction to the t Statistic Introduce the t statistic as an alternative to the z-score hypothesis test Demonstrate the calculation of estimated standard error and the t statistic Introduce the t distributions and their relation to degrees of freedom Demonstrate the entire process of hypothesis testing with the t statistic Demonstrate the measurement of effect size for the t statistic Lesson 10: The t Test for Two Independent Samples Introduce the basic characteristics of an independent-measures (or between subjects) research design. Demonstrate the process of hypothesis testing with the independent-measures t statistic Introduce the assumption of homogeneity of variance Demonstrate the measurement of effect size for the independent-measures t Lesson 11: The t Test for Two Related Samples Introduce the basic characteristics of a repeated-measures (or within-subjects) research design Demonstrate the process of hypothesis testing with the repeated-measures t statistic Demonstrate the measurement of effect size for the repeated measures t Discuss the advantages and disadvantages of a repeated-measures design compared to an independent-measures design Lesson 12: Estimation Introduce the statistical technique of estimation as an alternative to a hypothesis test Demonstrate point and interval estimates for the single-sample t, the independent-measures t, and the repeated measures t Demonstrate how the width of a confidence interval is related to the sample size and the level of confidence Lesson 13: Analysis of Variance (ANOVA) Introduce the logical foundation for ANOVA Demonstrate the process of hypothesis testing with ANOVA Demonstrate the use of post hoc test to help interpret the results from an ANOVA Demonstrate the measurement of effect size for an ANOVA Demonstrate the relationship between ANOVA and the independent-measures t test for a study comparing exactly two samples or treatment conditions

9 Lesson 14: Correlation and Regression Introduce the concept of a correlation and what it measures Demonstrate the Pearson correlation to measure linear relationship Demonstrate the Spearman correlation to measure relationships for ordinal data Demonstrate the Point-Biserial Correlation and the Phi-Coefficient Introduce the process of linear regression Demonstrate the analysis of regression to evaluate the significance of a linear regression equation 9

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