1. CPSY 501: Advanced Statistics Instructor: Dr. Sean Ho Teaching Assistant: Jessica Nee Reminder: We are not allowed to have food or drink near the computers, so please keep your edibles & drink bottles in your pack, or on the shelf at the side “of the class while you are this “ISYS lab” in the CanIL bldg. Fall, 2008

2. Course Dedication & Introductions Syllabus: … strolling through the term Statistics Review: purpose Statistics Review: approaches Data Analysis Project & Assignm’t #1 t -tests, correlations, Χ 2, levels of m’t CPSY 501: Class 1 Outline

3. Stats, Math, Faith, & ?!! Faculty have many different ways to show faith-affirming dimensions of disciplines, classes, & topics Faculty have many different ways to show faith-affirming dimensions of disciplines, classes, & topics The “common sense” presumption current in Canadian culture = math & faith are mutually irrelevant. But … ethnomath; the The “common sense” presumption current in Canadian culture = math & faith are mutually irrelevant. But … ethnomath; the Pirahã; math history & phil; … lively traffic! Dedication: All of life shows God’s hand…

4. Introductions Instructors: Dr. Sean Ho (& Mac as assistant?) TA: Jessica Nee You: – name – year in this program – year in this program – interests, fears, gifts: research, or experiences, or hopes, … – interests, fears, gifts: research, or experiences, or hopes, …

5. Syllabus: Tentative notes Class notes, articles, data files, etc. Offices & contact info Course description & objectives; teams SPSS: in labs, including the Wong Rsch Centre Course requirements and evaluation Advice and policy Textbooks: 1 required, others optional …

7. Statistics: Review Statistics as a decision-making tool: - is an effect / relationship real? - how strong is it? [Connect “variables”] Possible limitations of statistical approaches: - some assumptions: extreme reductionism, neutrality of observation, objectivity = ? - groups, not individuals What is the purpose of statistics in counselling psychology research? Focus: Research questions!

8. Statistics: Review example Research Question = RQ Research Question = RQ RQ: are men taller than women? RQ: are men taller than women? - is this relationship real? - how strong is it? Variables = ______? & ______? Variables = ______? & ______? Independent samples t-test … Independent samples t-test … Limitations illustration: grps vs. individuals; variables as ‘incomplete’ … Limitations illustration: grps vs. individuals; variables as ‘incomplete’ …

9. Statistics as Model-Building Model-building process (“variables” terms): 1.“Operationally define” a phenomenon = vars 2.Measure it (data collection) 3.Build a model, using the data and statistical procedures (assumptions) 4.Make conclusions &/or predictions about the phenomenon in the “real world,” based on the statistical model If A. is holding 2 apples in his hands, B. is holding 1 apple, and C. is holding 6 apples, how many apples is a child most likely to have?

10. Statistical Models: EX RQ: Does “self-esteem” correlate with school performance?  “grades” RQ: Does “self-esteem” correlate with school performance?  “grades” Measure: questionnaire & marks … Measure: questionnaire & marks … Choose a correlation model: Assumptions! Measures, procedures Choose a correlation model: Assumptions! Measures, procedures Make conclusions: based on “objectivity;” individual vs. group patterns; & often “linearity” … Make conclusions: based on “objectivity;” individual vs. group patterns; & often “linearity” …

11. Linear Modelling A linear model is the straight “line” that best fits the observed data (i.e., the line that results in the least amount of error possible, given the data) Commonly used statistical procedures involve (a) mathematically determining the “best” straight line for an observed set of data, and (b) calculating the goodness of fit between the model and the data, using test statistics (e.g., t, F): variance due to model variance due to error variance due to error Test statistic =

12. Linear Modelling (cont.) In summary: Statistics are used to build models of psychological phenomena out of observations gathered from specific samples of individuals Statistics are used to build models of psychological phenomena out of observations gathered from specific samples of individuals Common type of models found in statistics are linear (the straight “line” that minimizes the distance between the model and the data) Common type of models found in statistics are linear (the straight “line” that minimizes the distance between the model and the data) The adequacy of the model, as a summary of the observed data, can be calculated through test statistics. The adequacy of the model, as a summary of the observed data, can be calculated through test statistics.

13. Limitations of Linear Models Common statistical procedures (some ANOVA & some regression) are only approximate for phenomena that do not relate to each other in a linear way. (even non-linear models are often very “crude” approximations base on group patterns) What are some examples of psychological phenomena where the variables are related to each other in a non- linear way? [practical approx. vs “reifying” models]

15. Group Project: Suitable Data Obtain existing data set to conduct a new analysis No collection of new data & no simulated (made up) data Minimum sample size: 50 Minimum of 3 variables in your analysis (one outcome/DV) Possible sources: your own data; faculty members; departmental thesis data storage (permission required from original student and supervisor)

16. Group Project: Intro Purpose: to demonstrate what you have learned Multiple regression OR some form of ANOVA (complex non-parametric analyses may be acceptable, with permission) Up to 3 people per group (submit 1 paper for the group). The project can be done individually. Instructor approval required before proceeding with part 3 or 4.

17. Project Step 1: Data Set Written description of the data set that you will be using Preliminary explorations of that data (attach SPSS outputs) Only describe the variables that you are thinking of using for this project. Tentative Due Date: September 26

18. Project Step #2: Data Meeting Meetings between the group and the professor, to discuss proposed analysis and obtain permission to proceed Bring previous assignment AND an electronic copy of your data-set It is expected that the group will have briefly reviewed the literature, planned the analyses, and determined that the sample size is sufficient. Due: In September (by October 3 rd at the latest)

19. Project Step #3: Research Ethics Board Submission All new analyses of existing data sets conducted at TWU must be approved by the REB Complete the “Request for Ethical Review - Reanalysis of Existing Data” form Consult with instructor if any part of the form is confusing. Submit two signed copies of the completed form Due: October 10

20. Group Project: manuscript The emphasis is to demonstrate your statistical knowledge, not to deal with the topic area that you are studying Full APA manuscript format is required (with exceptions as noted) Sections: Title page; Abstract; Intro; Method & Data Set; Data Preparation & Results; Discussion; References; Tables/Figures; SPSS Outputs Include at least one table/figure Maximum Length: 15 pages + SPSS outputs Project Step #4: Analysis Report

22. Review Assignment Review of Basic Statistics & practicing SPSS  download files, etc. Review of Basic Statistics & practicing SPSS  download files, etc. DUE: Sept. 19 DUE: Sept. 19 Data set: AttnDefDis-#1 Data set: AttnDefDis-#1

23. Basic Stats: Conceptual Heart Research Questions: numbers vs. “data” Research Questions: numbers vs. “data” Variables & “levels of measurement” Variables & “levels of measurement” Designs: Between & within “subjects” Designs: Between & within “subjects” EX: t-tests  EX: t-tests  Theory & conceptual work: description vs. inference, Theory & conceptual work: description vs. inference, Uses of t -tests, correlations, Χ 2, etc. Uses of t -tests, correlations, Χ 2, etc.