Information Session on Statistical Methods Offerings in LHAE and - PowerPoint PPT Presentation

Information Session on Statistical Methods Offerings in LHAE and OISE Prof. Anna Katyn Chmielewski Prof. Scott Davies Sep. 6, 2017

Overview Introductions 1. Why learn statistical methods? 2. Do I get “Research Methods [RM]” credit toward my degree program for 3. statistics courses? Where can I find statistics courses at OISE? 4. Which statistics courses are available? 5. How can I figure out which course(s) to take? 6. How can I plan my schedule of statistics courses? 7. Which software is used in statistics courses? 8. Where can I find up-to-date info on statistics course offerings? 9. How can I register for statistics courses? 10. Additional questions? 11.

1. Introductions Departments Applied Leadership, Psychology & Curriculum, Higher & Adult Social Justice Human Teaching & Education Education (SJE) Development Learning (CTL) (LHAE) (APHD) Programs Degrees • MEd Adult Education Educational MA • & Community Higher Leadership & • EdD Development Education (HE) Policy (ELP) (AECD) • PhD

2. Why learn statistical methods?  Fun!  Powerful tools for research  Valuable skills for the job market Past non-university research placements : HEQCO, Ministry of  Advanced Education and Skills Development, Statistics Canada, People for Education, Ministry of Community of Social Services, Ministry of Education, Education Quality and Accountability Office (EQAO), Council of Ministers of Education Canada; TDSB and other school board research offices; college institutional research offices Past university placements : University of Guelph, Nipissing, Laurier,  Waterloo, Harvard, Lakehead U, U of Saskatchewan, Western U, York U, Laurentian U

3. Do I get “Research Methods [RM]” credit toward my degree program for statistics courses?  Most grad programs in LHAE require 1 or more courses in research methods, chosen in consultation with Faculty Advisor  Additional research methods may be taken as electives  Research methods courses are indicated by [RM] in the OISE Graduate Bulletin and on ACORN/ROSI  All statistical methods courses have [RM] designation

3. Do I get “Research Methods [RM]” credit toward my degree program for statistics courses? Departments Applied Leadership, Psychology & Curriculum, Higher & Adult Social Justice Human Teaching & Education Education (SJE) Development Learning (CTL) (LHAE) (APHD) Programs Degrees • MEd Adult Education Educational MA • & Community Higher Leadership & • EdD Development Education (HE) Policy (ELP) (AECD) • PhD

3. Do I get “Research Methods [RM]” credit toward my degree program for statistics courses? Adult Education & Educational Higher Education Community Leadership & Development Policy Bulletin pages pp. 73-74 pp. 76-79 pp. 80-81 MEd “one research course is 1004 required, others as “a half-course in research recommended” electives methods approved by the faculty advisor” MA “one half course in research 1003 & 1004 required, others “a half-course in research methods is required” as electives methods approved by the faculty advisor” EdD N/A a half course in research “a half-course in research methods at the 3000 or 6000 methods approved by the level (+1 RM may be faculty advisor” substituted instead of internship) PhD “Students will normally take two advanced-level (3000 or “a half-course in research at least one specialized 6000) courses in research methods approved by the research methods course, methods faculty advisor” which may be taken outside the Program with permission of the supervisor”

4. Where can I find statistics courses at OISE? May be open to LHAE students Applied Curriculum, Psychology & Teaching & Social Justice Human Leadership, Learning (CTL) Education (SJE) Higher & Adult Development (APHD) Education (LHAE) several mixed- 0 stats 1 stats methods 4 quant Adult Education Educational Joint OISE & Community Higher Education Leadership & courses (JOI) Development (HE) Policy (ELP) (AECD) 2 stats 3 stats 1 mixed- 2 quant 1 quant methods Open to all OISE students Usually open to all LHAE students

5. Which statistics courses are available? Introductory Fall 2017 – JOI1287: Introduction to Applied Statistics [RM] – Tuesdays or  Thursdays (M. Azidi) Intermediate (pre-requisite: JOI1287 or equivalent) Winter 2018 – JOI3048: Intermediate Statistics in Educational Research:  Multiple Regression Analysis [RM] – Wednesdays (A.K. Chmielewski) Advanced (pre-requisite: JOI3048 or equivalent) Winter 2018 – LHA6003: Quantitative Research Practicum [RM] –  Tuesdays (S. Davies) TBA in 2019 – LHA600X: Multilevel and Longitudinal Modelling in  Educational Research [RM] - (A.K. Chmielewski) [Note LHA6000 = doctoral-level special topics code]

JOI1287: Introduction to Applied Statistics Fall 2017, Tuesdays 5-8pm or Thursdays  5:30-8:30pm Mathematics Score by Classroom 700 Dr. Mahshid Azimi  NO prior experience with stats needed  600 Mathematics Score Intro to quant methods of inquiry  Univariate and bivariate descriptive stats  500 Sampling, experimental design,  statistical inference 400 Chi-square, t-test, ANOVA, regression  Classroom 1 Classroom 2 Intro to SPSS software  Students will be able to analyze real data  and interpret results

JOI3048: Intermediate Statistics in Educational Research: Multiple Regression Analysis Winter 2018, Wednesdays 5-8pm, Ed  Commons (3 rd floor) Lab 6 Student Math Achievement by Socio-economic Status and Province 800 Prof. Anna Katyn Chmielewski  675 Bivariate and multivariate linear  Math Achievement QC regression models 550 ON Curvilinear regression functions; dummy  425 & categorical variables; interactions 300 Model selection, assumptions,  diagnostics -2 -1 0 1 2 Socio-economic Status Intro to Stata software (may use SPSS)  Students will be able to run, interpret  and write about regression models in their own research

LHA600X: Multilevel and Longitudinal Modelling in Educational Research TBA in 2019  Prof. Anna Katyn Chmielewski Math Achievement by Age  Student 3 800 Student 1 Analysis of data with multilevel structure  (e.g. students nested in schools, school 600 Math Achievement boards, provinces or countries) Student 2 Study of educational change (e.g. student  400 learning/growth, school improvement or organizational change) 200 2-level and 3-level cross-sectional and  growth curve models 0 5 6 7 8 9 10 Age Model selection, assumptions, diagnostics  Intro to HLM software (Stata or SPSS for data cleaning/prep)  Students will be able to run, interpret and write about multilevel models in their own  research

LHA6003: Quantitative Research Practicum  Prof. Scott Davies Date, Time and Location  Winter (January – April) 2018  Tuesdays 5-8pm  OISE 6-184 (Data, Equity and Policy in Education [DEPE] Lab)

LHA6003: Quantitative Research Practicum (cont.) Purposes of this Course  Instruction in causal inference (issues in survey analysis, quasi-experiments, some statistical techniques like categorical analysis and propensity score matching)  Guidance in management and analysis of large scale data sets, either provided or students’ own data  Access to unique educational data sets  Term papers: draft article involving data analysis

LHA6003: Quantitative Research Practicum (cont.) (Hopefully) Available Data Sets  OISE Surveys 1978-2017: Public opinion on educational issues, Ontario adults  TDSB-UofT: track cohort of students from grade 9 to BA years  Ontario Summer Learning: cross-sectional and 3 year longitudinal  Ontario EDI-EQAO: track cohorts of students from Kindergarten – Grade 9  Ontario School Mental Health Surveys

6. How can I figure out which courses to take?  Ask your Faculty Advisor!  For general skills, mixed methods thesis – intro or intermediate  For quantitative-only master’s thesis – (at least) intermediate  For quantitative-only doctoral dissertation – (at least) one advanced course

6. How can I figure out which courses to take? (cont.) Math Pre-requisites: high school math (algebra, graphing functions, solving  equations, basic probability); NO calculus or linear algebra necessary Statistics Pre-requisites:  Introductory (JOI1287) – no pre-requisite (i.e., NO prior experience needed)  If you are not sure whether to take intro (e.g., you took a similar course in  undergrad), a self-administered placement test is available here: https://portal.utoronto.ca/webapps/blackboard/execute/courseMain?course_id=_926375_1 (Click the “+Enrol” button, log in with your UTORid, click “Submit” and “OK”. The test is non- credit. You will be required to complete a demographic survey before attempting the test. Recommendations will be presented to you upon completing the test and viewing your results.) Intermediate (JOI3048, JOI1288) – pre-requisite: intro (JOI1287 or equivalent)  Advanced (LHA6003, LHA600X) – pre-requisite: intermediate (JOI3048,  JOI1288 or equivalent) Pre-requisites need not be taken at OISE 