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DRPS : Course Catalogue : School of Health in Social Science : Clinical Psychology

Postgraduate Course: Inferential Statistics in Applied Psychology (CLPS11073)

Course Outline
SchoolSchool of Health in Social Science CollegeCollege of Arts, Humanities and Social Sciences
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityNot available to visiting students
SCQF Credits20 ECTS Credits10
SummaryThis course aims to provide students with both a theoretical and practical understanding of inferential statistics, and their use in psychology. It is aimed primarily at postgraduate students who have completed an undergraduate degree in Psychology or a cognate subject, and who have a basic understanding of statistical principles and assumptions, including the ability to conduct descriptive statistics. Additionally, students are expected to have practical knowledge in the use of SPSS software. Students who have not previously used the SPSS software will be expected to engage in independent learning of the software. The course will cover content such as hypothesis testing and confidence intervals, power and effect size, correlation and regression models, assumptions and issues in regression analysis, non-linear and logistic regression models, mediation and moderation, non-parametric and resampling approaches including bootstrapping, ANOVA, ANCOVA and finally, MANOVA. By the end of the course students should be able to independently run statistical analysis, make choices regarding the appropriate use of statistical tests dependent on research questions being asked, and demonstrate an ability to interpret the results of statistical testing. This course is aimed primarily at students on the MSc Mental Health in Children and Young People: Psychological Approaches programme.
Course description Statistical testing and inference are important and necessary elements of graduate learning in psychology. The ability to understand how to meaningfully test hypotheses and interpret the results, are required components for any student undertaking an empirical dissertation project. This course is meant to provide students with both theoretical and applied competencies related to the use of inferential statistics. The content covered in this course will include: hypothesis testing and confidence intervals, power and effect size, correlation and regression models, assumptions and issues in regression analysis, non-linear and logistic regression models, mediation and moderation, non-parametric and resampling approaches including bootstrapping, ANOVA, ANCOVA and finally, MANOVA. The course will make use of a flipped classroom format to ensure that students are able to independently work through the lectures at their own pace prior to coming to the weekly tutorials. Each week students will be required to independently listen to a prerecorded 1-hour lecture. Students will also attend a one and a half hour face-to- face tutorial each week where they will have the opportunity to engage in hands-on-training related to the concepts covered in the lecture that week. Students will also be required to independently complete weekly readings for each of the 10 weeks during the semester. Achievement of learning outcomes will be demonstrated via individual assignments (week 2, week 5, week 7 and week 10), combining both theoretical and applied knowledge. Each assignment will be worth 25% of the final mark.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Course Delivery Information
Academic year 2022/23, Not available to visiting students (SS1) Quota:  None
Course Start Semester 2
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 200 ( Lecture Hours 10, Online Activities 20, Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 166 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) There will be 4 independent summative assessments over the duration of the 10-week course, each worth 25%. Assessments will be individual homework, which assesses both theoretical and applied knowledge of inferential statistics. Students will be required to work hands on with data by running their own statistical analysis along with completing short answer questions (250-word max per question totalling no more than 1,000 words per assignment). These assessments will be completed independently outside of tutorial time. Assessments will be submitted in weeks 2, 5, 7, and 10.
Feedback Not entered
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Demonstrate theoretical knowledge and critical understanding of inferential statistics.
  2. Demonstrate understanding of assumptions and limitations of inferential statistics.
  3. Demonstrate the ability to conduct inferential statistics and model building appropriate to research questions.
  4. Demonstrate the ability to make critical inferences regarding statistical testing.
Reading List
None
Additional Information
Graduate Attributes and Skills * Critical thinking, ability to analyse, and evaluate research via better understanding of statistical testing and inferences.
* Ability to critically select appropriate statistical tests in applied research.
* Ability to parse and critically interpret statistical information in published research.
* Critical understanding of interpreting practical meaning from statistics.
* Applied knowledge of statistical testing and inference.
* Autonomy skills via independent learning.
* Improvement of analytical skills.
KeywordsStatistical Analysis,Inferential Statistics
Contacts
Course organiserDr Hope Christie
Tel:
Email: HCHRIST5@exseed.ed.ac.uk
Course secretaryMrs Chelsea Kennedy
Tel: (0131 6)51 3969
Email: Chelsea.Kennedy@ed.ac.uk
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