This document summarizes the key points from Lecture 1 of the N3318a Fall 2009 Elementary Statistics online course. It introduces the instructor, Abe Oudshoorn, and provides an overview of the course, including expectations, assignments, exams, and a research paper. It also discusses the importance of statistics for nursing and presents sample health data on nurses to illustrate statistical concepts. Key topics covered in the lecture include measurement scales, types of variables, and hypotheses.

1. N3318a Fall 2009 Elementary Statistics ONLINE Course Abe Oudshoorn, School of Nursing, University of Western Ontario Lecture 1 Introduction and Measurement Review

2. Who am I? Abe Oudshoorn RN, PhD(c) Office: H142, Main office suite Phone: x86042 e-mail: aoudshoo@uwo.ca Office Hours: Tues 9:30-10:30 Wed 2:00-3:00

4. Text: Munro, B. H. (2003). Statistical methods for health care research . (5th Ed). Philadelphia: Lippincott. 2003 Course syllabus: - Online on WebCT Course readings: - Online on WebCT Course Supplies

7. Lectures Available online 24/7: Read textbook before looking at lecture Content: 1. Online notes intended to clarify what was read in text in preparation for class 2. Practice interpreting assigned readings

8. Research paper Due: Monday, Dec 7, 2009 by 1600 hrs. The purpose of the 10 page paper is to critique the results and interpretation of results reported in one (1) research article. The reference for the research article is in the course syllabus. (25%) Please review the details in the ONLINE syllabus located under the Course Syllabus icon

9. Exams Mid-term Exam : (ONLINE) October 26, 200 Time 2-5pm (20%) a 3-hour open-book exam includes true/false, multiple choice, matching and (possibly) short answer questions Final Examination: (ONLINE) Date: December 14, 2-5pm (25%) a 3-hour, cumulative, open-book exam with true/false, multiple choice, matching and an abstracted article to interpret

10. Course Evaluation Summary Component Date Due Percent Full Grade Bi-weekly reading assignments Every other Monday by 4pm following unit discussion in previous weeks 30% Scholarly Term Paper - Research Article Critique Monday December 7 th , 2009, 4pm 25% Mid term Exam (3 hrs, open book) (individual) Final Exam (3 hrs , open book) (individual) October 26 th , 2-5pm ONLINE December 14 th , 2-5pm ONLINE 20% 25%

11. enough with the boring stuff …

15. Siegrist’s Effort-Reward Imbalance (ERI) Model efforts demands pressures responsibilities rewards salary support respect e.g. prospective German cohort study found ERI a key factor for IHD rates in blue collar workers

21. How easy was it to follow the presentation of these data? The main aim of the course is to make this type of thing easier for you to understand and even be able to present on your own !! Data Summary

22. Lecture 1 : Measurement Scales, Types of Variables and Hypotheses

23. The Research Process (For Quantitative Studies) N3318 Statistics N3319 Research Methods Identify the Problem Develop a study protocol Collect the data Draw inferences Analyze the data Our focus this term

24. Some Background Terms Descriptive statistics: – for summarizing or describing a sample Inferential statistics: – for making inferences (conclusions) or to generalize from a sample to a population Sample: – part of population (what is used in most studies) Population: – all members of a particular group of interest

26. Measurement Scales Nominal data : – distinct, unordered, qualitative e.g. gender, race ( Can you think of some others ?) Ordinal data : – ordered, distinct, qualitative categories e.g. health status, SES, others? Interval data : – ordered, quantitative categories, known intervals e.g. can be continuous (e.g. Celsius temp.) or discrete (e.g. parity), others? Ratio data : – most precise metric due to useful zero value e.g. BP, weight, height, Kelvin temp., others?

27. Summarizing the Scales (N.O.I.R) Scale Mutually exclusive categories Categories have order Standard unit of measure Useful zero point on scale Nominal X Ordinal X X Interval X X X Ratio X X X X

29. Types of Variables Independent : (exposure) – typically the variable(s) manipulated, controlled (or at least recorded) by the researcher e.g. dietary interventions, others? Dependent : (outcome) – typically the main variable of interest being measured by the researcher e.g. weight loss, others? Study conducted comparing effect of two dietary interventions on weight loss in obese children Scenario

30. Types of Hypotheses - 1 Null hypothesis (H 0 ): - proposes no difference or relationship between variables of interest ( basis for statistical inference ) e.g. There is no difference in weight loss between the two dietary intervention groups. Research hypothesis (H r ): - opposite of the null hypothesis (i.e. states that there is a relationship between variables) - also called alternative hypothesis or H a e.g. There is a difference in weight loss between the two dietary intervention groups Two sides of the same coin !

31. Types of Hypotheses - 2 Directional hypothesis : - proposes a specific direction for effect e.g. Intervention A will reduce weight more effectively than Intervention B Non-directional hypothesis : - no specific direction but an effect is predicted e.g. Intervention A and B will differ in their ability to induce weight loss

36. Don’t forget to do your first reading assignment!