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# Class 1 Introduction, Levels Of Measurement, Hypotheses, Variables

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Lecture for N3318a Class 1

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• 3318a Fall 2009 Elementary Statistics ONLINE Course Abe Oudshoorn, School of Nursing, University of Western O

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### Class 1 Introduction, Levels Of Measurement, Hypotheses, Variables

1. 1. N3318a Fall 2009 Elementary Statistics ONLINE Course Abe Oudshoorn, School of Nursing, University of Western Ontario Lecture 1 Introduction and Measurement Review
2. 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
3. 3. Today’s Class <ul><li>Overview of the course </li></ul><ul><li>Why take a statistics course? </li></ul><ul><li>- a quick look at some real data </li></ul><ul><li>First lecture: </li></ul><ul><li>Levels of measurement, hypotheses and types of variables </li></ul>First Reading Assignment is due Sept 21 st !
4. 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
5. 5. Course Description <ul><li>an introduction to basic statistics </li></ul><ul><li>goal is for students to develop an understanding of statistical concepts and findings in research articles. </li></ul><ul><li>builds upon Nursing 3319 course - Research Methodology </li></ul><ul><li>develop critical analysis skills to enable integration of research into practice </li></ul>
6. 6. Course Website-WebCT Vista http://webct.uwo.ca <ul><li>Syllabus and lectures are there </li></ul><ul><li>Course web pages include reading assignments and research paper submission areas, discussion sites, exams, announcements, etc. </li></ul>
7. 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. 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. 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. 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. 11. enough with the boring stuff …
12. 12. Do Nurses Need a Stats Course? <ul><li>scientific foundation for a constantly evolving professional practice </li></ul><ul><li>shift to evidence-based practice </li></ul><ul><li>need to understand why and how certain statistics are generated to critically appraise what researchers present to you </li></ul><ul><li>course provides a basi c statistical foundation to help you cope with the data “overload” </li></ul>Not trying to turn you into biostatisticians !!
13. 13. <ul><li>What is the health status of acute care hospital RN’s? </li></ul><ul><li>These data were presented at a CFNU Board of Directors meeting </li></ul>A “Real-World” Example for the Use of Statistics in Nursing While viewing slides, be critical and ask yourself are these data convincing or not?
14. 14. Ontario Nurse Survey <ul><li>About 15,000 nurses sent surveys (via CNO) </li></ul><ul><li>About 58% response (N=8,141) </li></ul><ul><li>Targeted to medical/surgical wards </li></ul><ul><li>All acute care hospitals in Ontario (up to 100 nurses sampled) </li></ul><ul><li>Included Siegrist’s ERI scale and MSK pain (back and neck/shoulder) questions plus others </li></ul>Mailed survey of registered nurses in acute care, non-specialty hospitals in Ontario (part of a 5-country study)
15. 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
16. 16. Burnout <ul><li>almost 4 in 10 report high burnout (EE sub-scale) </li></ul><ul><li>may be “internalizing” work stress? </li></ul>38.1 20.4 13.2 0 10 20 30 40 Emotional Exhaustion Personal Accomplishment Depersonalization Percent
17. 17. Back / Neck Pain in Last Week 30.8 25.3 43.9 0 10 20 30 40 50 Pain status None/Low Moderate High Percent Back and/or neck pain frequency in past week <ul><li>1 in 4 nurses have pain most or all of the time </li></ul>
18. 18. Percent <ul><li>is poor work environment a driver of burnout? </li></ul>(Note: Percentages do not add to 100% since they are not cumulative.) Are Burnout and ERI Scores Related? 13.2 31.7 65.7 0 10 20 30 40 50 60 70 None Moderate High Burnout status % with ERI present (by burnout group) None Mod High % with ERI
19. 19. 29.1 40.3 55.8 0 10 20 30 40 50 60 % with ERI present (by MSK pain group) None/Low Moderate High MSK pain status Percent <ul><li>is poor work environment a driver of MSK pain? </li></ul>(Note: Percentages do not add to 100% since they are not cumulative.) Are MSK Pain and ERI Scores Related? None Mod High % with ERI
20. 20. Main Conclusions <ul><li>Nurses report high rates of back/neck pain and burnout (emotional exhaustion only, not personal accomplishment or depersonalization) </li></ul><ul><li>The workplace psychosocial environment is strongly associated with the health of the nurses (physical demands also important though!) </li></ul>
21. 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. 22. Lecture 1 : Measurement Scales, Types of Variables and Hypotheses
23. 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. 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
25. 25. <ul><li>for research studies focus is on inferential statistics (WHY?) </li></ul><ul><li>reliability and validity of inference depends on quality of sample </li></ul><ul><li>good statistics can’t save a bad study ! </li></ul>Statistics is about quantifying the probability of error when making a generalization from a study sample to a population A Few Key Points …
26. 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. 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
28. 28. Some Caveats on Scales <ul><li>Nominal data analysis can be more limited (e.g. counts or frequencies only) </li></ul><ul><li>Ordinal “should”have same limitation but often treated like a continuous scale </li></ul><ul><li>Interval and Ratio data often “collapsed” to an ordinal or even nominal scale </li></ul>(threshold value?) (loss of information, restricts analysis) Why? What is the main problem with doing this?
29. 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. 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. 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
32. 32. Types of Hypotheses - 3 <ul><li>Causal hypothesis : </li></ul><ul><li>- implies that a stated variable is responsible for any observed effect </li></ul><ul><li>e.g. Intervention A will induce greater weight loss in obese children than Intervention B </li></ul><ul><li>Associative hypothesis : </li></ul><ul><li>- proposes no specific direction for effect </li></ul><ul><li>e.g. Dietary intervention may lead to weight loss in obese children </li></ul>
33. 33. Types of Hypotheses - 4 <ul><li>Simple hypothesis : </li></ul><ul><li>- typically involves a cause-effect relationship only between dependent and independent variables </li></ul><ul><li>e.g. Intervention A will induce weight loss in obese children </li></ul><ul><li>Complex hypothesis : </li></ul><ul><li>- typically involves more complicated causal pathway between multiple variables </li></ul><ul><li>- e.g. Intervention A will induce weight loss in obese children who have a family history of obesity </li></ul>
34. 34. Summarizing Hypotheses <ul><li>Null or Research? </li></ul><ul><li>Directional or Non-directional? </li></ul><ul><li>Causal or Associative? </li></ul><ul><li>Simple or Complex? </li></ul>The 4 categories are not mutually exclusive – i.e. hypotheses can be categorized using all 4 levels e.g. Dietary intervention A will induce more weight loss than dietary intervention B in obese children Research Directional Causal Simple
35. 35. Next Week - Lecture 2 : Data presentations and data handling <ul><li>For next week’s class please review: </li></ul><ul><li>Textbook: </li></ul><ul><li>Ch 1, pp 8-27; Ch 2, pp 51-63 </li></ul><ul><li>Research paper: </li></ul><ul><li>Stastny et al. (2004) </li></ul>