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HSS2381A – Quantitative Methods in Health Sciences I Professor Raywat Deonandan ray@deonandan

HSS2381A – Quantitative Methods in Health Sciences I Professor Raywat Deonandan ray@deonandan.com 43 Templeton, Room 111. Class website:. Eventually, all materials will be on Virtual Campus.

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HSS2381A – Quantitative Methods in Health Sciences I Professor Raywat Deonandan ray@deonandan

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  1. HSS2381A – Quantitative Methods in Health Sciences I Professor Raywat Deonandan ray@deonandan.com 43 Templeton, Room 111

  2. Class website: Eventually, all materials will be on Virtual Campus. However, since the University I.T. Department is run by monkeys, for the time being, I will be using my own server temporarily: Classes.deonandan.com

  3. Lectures • Mondays 11:30-1pm • Thursdays 1:00-2:30pm • SMD224 • You are responsible for all material covered in the lectures, whether or not it appears in the slides • i.e., take your own notes and don’t get lazy

  4. Lectures • There are many sections of HSS2381, and ultimately we try to cover the same thing • But the different sections are not interchangeable • And our exams and assignments will also be different • i.e., feel free to study with students from other classes, but they may employ different textbooks, methods, etc.

  5. TEXTBOOKS Required: Data-Analysis & Statistics for Nursing Research by Denise F. Polit, published by Appleton & Lange, Stamford, Connecticut, USA (second edition). $73.98 + tax Agora Books 145 Besserer Recommended (but not required): A Introduction to Statistics for Canadian Social Scientists by Michael Han, published by Oxford Press

  6. Evaluation: Oct 20 Nov 28 Nov 3 Dec 9-22

  7. Lectures • Note that I do not take attendance • Attendance in lectures in labs is voluntary (hey, you’re all grown-ups) • But whether or not you attend, you are still responsible for what is covered in class and in labs

  8. Labs • Each group of 20 has a one-hour lab on Wednesday morning (MNT 140) • Each lab will be supervised by Teaching Assistant Armin Yazdani (arminyazd@gmail.com)

  9. Labs • The purpose of the labs is to: • Introduce you to using computers to do basic statistics • Give you protected time to work on your homework and assignments • Allow you to approach the TA to go over anything that is unclear from the lectures

  10. Labs • In a few of the labs, the TA will have you do specific exercises • In others, you will have free time to explore on your own • Please be respectful of the TA and others, and not use the time for socializing or for activities unrelated to this class

  11. Exams • Both exams (midterm and final) will be entirely multiple choice • The final exam will NOT be cumulative, but will only cover material since the midterm

  12. Assignments • There will be TWO assignments, to be completed individually (not in groups) • They can be completed either by using a computer or by hand. • Details about the assignment will be posted soon

  13. Contacting Me • Of course, I am willing and eager to speak to you about anything • However, I’m pretty hard to get hold of at times • So.....

  14. Contacting Me • For issues relating to the course, especially regarding course content or issues regarding marking, please contact the TA first: • Tiffany will available via email and during office hours (to be posted soon) • Armin will be available via email and during the lab time

  15. Contacting Me • I don’t maintain regular office hours, but I try my best to be in my office on Mondays from 2-4pm • It’s best to email me for an appointment

  16. Rules of Engagement • I don’t require attendance • But if you do come, please pay attention

  17. Rules of Engagement • I do not negotiate marks • TA’s are instructed to not change marks for any reason except when there has been a clear error in marking • All suspected cases of academic fraud are reported to the Dean’s office • this includes cheating on exams and collaborating on assignments (in cases where that has been prohibited)

  18. Rules of Engagement • The 2 exams and 2 assignments are the only ways to earn marks... This means no make-up assignments if you’re doing poorly • The only acceptable excuses for missing an exam or for submitting a late assignment (without penalty) are: • Medical (with documentation) • Family or personal tragedy (with evidence) • This means that the demands of your vacation plans, sporting events and part-time job are not acceptable reasons

  19. As a Result... • I won’t be here Thursday Sep 22 • You will receive a guest lecture that day by the TA’s • I will be here on Monday Nov 28 • However, the TA’s will also be giving that day’s lecture

  20. Let’s Review • I hate statistics • Anyone else? And yet I have a PhD in Biostatistics. So what does this tell us?

  21. The Power of Statistics • If you really understand statistics, then you really understand the fundamentals of modern scientific research • Gives you a grounding to assess the quality of pretty much any quantitative statement • Never be manipulated again!

  22. The Origin of Statistics • What we would call modern statistics began in the 1700s • “statistics” = accounts of the “state” • Obviously, the use of population data goes back centuries before

  23. The Origin of Statistics • There has been a revolution in last 200 years or so... • There was a further computer revolution in past 50 years, that has allowed for rapid advances in multivariable techniques • Statistics has become one of the foundations of all quantitative sciences • It’s one of the defining tools of population health, especially epidemiology

  24. What is statistics? Statisticsis the term for a collection of mathematical methods of organizing, summarizing, analyzing, and interpreting information gathered in a study

  25. (lbs) Math vs Statistics • Is there a difference? • Weights of members of 2000 U.S. Men’s Olympic Rowing team

  26. Data vs Information • 50, 52, 56 • The ages of Barack Obama, Stephen Harper and Nicolas Sarkozy

  27. What is Measurement? • One definition: assigning a quantity to a quality • E.g. How old are you?  25 • E.g. What’s your gender?  female

  28. What is a Variable? • A value that may change within the scope of a problem or situation (vs a “constant”) • A logical set of attributes (gender, age, etc) • A symbolic name given to an unknown quantity Math Research Computers

  29. What is a Variable? • “x” • Age • A$ Math Research Computers

  30. Relationships Between Variables • In research, we can focus on just one variable • Or we can try to describe relationships between 2 or more variables What is the average age of students in this classroom? In this classroom, is the average age of women different from the average age of men?

  31. Relationships Between Variables • In math, we write the relationship between 2 variables as a “function”: e.g. F(x) = 210 - x (Maybe this is the relationship between age and maximum attainable heart rate) F(x) = max heart rate = HR x = age

  32. Relationships Between Variables Independent Dependent HR = 210 - x Dependent Independent

  33. Relationships Between Variables Epidemiology: Exposure Outcome HR = 210 - x Outcome Exposure

  34. Relationships Between Variables Epidemiology: Exposure Outcome Cancer rate = 210 - smoking Outcome Exposure

  35. Two Flavours of Variables • Continuous • Categorical (also called “Discrete”) Age, height, distance, temperature... Age group, gender, number of siblings, citizenship, race...

  36. Most Common Type of Categorical • Dichotomous • Meaning “having two levels” • E.g., sex • “Dichotomize” • Convert “age” to “under 40” and “over 39”

  37. Levels of Measurement • Level of Measurement: A system of classification with four types of measurement rules that affect the kind of statistical analysis that is appropriate: • Nominal • Ordinal • Interval • Ratio

  38. Nominal Measurement • Think of “name” when you think of “nominal” • Nominal Measurement: • Lowest form of measurement • Numbers are used simply as labels to name categories • E.g. Assigning 2 arbitrary numbers to code for sex: 0=male, 1=female • It does not matter what the codes are, the numbers have no quantitative meaning • Therefore we can’t treat these arbitrary numbers like we would any other numbers in math • E.g. in class we have 30 men (all coded “0”) and 70 women (all coded “1”). Average score is 0.7… which means nothing

  39. Ordinal Measurement • Ordinal Measurement: • Uses numbers to designate ordering on an attribute • Conveys some information about amount • But does not indicate distance between values • Example: Degree of pain 1 = None 2 = Some 3 = A lot • Pain of 1.7 means nothing distances are not equal, and are not known Averages do not make sense

  40. Interval Measurement • Interval Measurement: • Also uses numbers to designate ordering on an attribute and conveys information about amount • Distance between values are assumed to be equal • Averages can be computed • Example: Ambient temperature (Fahrenheit) |___|___|___|___|___|___|___|___|___|___| 70 71 72 73 74 75 76 77 78 79 80  The difference between 70 and 75 degrees is the same as the difference between 75 and 80 degrees • Note: The term “interval” measurement is used in the textbook, but I don’t encounter it often in real life. Usually, we just call this a continuous variable and be done with it.

  41. Ratio Measurement • Ratio Measurement: • Uses numbers to designate ordering, conveys information about amount, distances are equal • AND there is a real, rational zero • Averages can be computed • Example: Medication dose (e.g., number of milligrams, number of pills) • Note: The term “ratio” measurement is used in the textbook, but I don’t encounter it often in real life. Usually, we just call this a continuous variable and be done with it.

  42. Levels of Measurement • At each successive measurement level, there is more information, and greater analytic flexibility • If you start with ratio measures, you can collapse information to a lower-level measure, but the reverse is not true • i.e. you can “dichotomize” a continuous variable, but you can’t turn a dichotomous variable into a continuous one. • Higher-level scales are usually (though not always) preferred • Moving from continuous to ordinal causes us to lose information, but it’s often done for convenience • E.g. age  age group

  43. Comparison of Levels

  44. Sampling POPULATION (also called “REFERENCE POPULATION”) sample

  45. Sampling POPULATION = Students at U of O Sample = this class If I compute the average number of women in this class, I can generalize to the whole university.

  46. Sampling Bias sample Is the sample “representative”?

  47. Sampling • Target (or reference) population is group of individuals to which one wishes to generalize findings. • Accessible population is portion of target population that has chance of being selected. (Also called “Study population”) • Sample is selected from accessible population.

  48. Sampling • There’s also something called a “Sampling Frame” that is not discussed in the textbook • Sampling Frame is a subset of the Accessible Population, from which the Sample is taken

  49. Target and accessible populations (ref pop) (accessible pop)

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