.
Analytical Statistical Methods in Psychological Research
Study Course Description
Course Description Statuss:Approved
Course Description Version:9.00
Study Course Accepted:03.07.2024 15:59:00
| Study Course Information | |||||||||
| Course Code: | SL_026 | LQF level: | Level 7 | ||||||
| Credit Points: | 2.00 | ECTS: | 3.00 | ||||||
| Branch of Science: | Mathematics; Theory of Probability and Mathematical Statistics | Target Audience: | Psychology | ||||||
| Study Course Supervisor | |||||||||
| Course Supervisor: | Diāna Kalniņa | ||||||||
| Study Course Implementer | |||||||||
| Structural Unit: | Statistics Unit | ||||||||
| The Head of Structural Unit: | |||||||||
| Contacts: | Baložu iela 14, Block A, Riga, +371 67060897, statistika rsu[pnkts]lv, www.rsu.lv/statlab | ||||||||
| Study Course Planning | |||||||||
| Full-Time - Semester No.1 | |||||||||
| Lectures (count) | 6 | Lecture Length (academic hours) | 2 | Total Contact Hours of Lectures | 12 | ||||
| Classes (count) | 6 | Class Length (academic hours) | 2 | Total Contact Hours of Classes | 12 | ||||
| Total Contact Hours | 24 | ||||||||
| Study course description | |||||||||
| Preliminary Knowledge: | Bachelor's degree experience in research, appropriate Bachelor's degree knowledge in statistical methods. | ||||||||
| Objective: | To gain basic knowledge of data processing methods, to develop and use analytical statistical methods in psychological research. | ||||||||
| Topic Layout (Full-Time) | |||||||||
| No. | Topic | Type of Implementation | Number | Venue | |||||
| 1 | Research in psychology. Quantitative and qualitative research. General population and sample. Statistical methods and its applications. Data measurement scales and descriptive statistics. Inferential statistics. | Lectures | 1.00 | other | |||||
| 2 | One dimensional statistics – use of SPSS in data processing and analysis, presentation of results. | Classes | 1.00 | other | |||||
| 3 | Analysis of variance (ANOVA, MANOVA, MANCOVA, mixed design MANOVA). | Lectures | 1.00 | computer room | |||||
| 4 | Analysis of variance – use of SPSS in data processing, analysis, presentation of results. | Classes | 1.00 | computer room | |||||
| 5 | Regression analysis: standard, hierarchical, stepwise. | Lectures | 1.00 | computer room | |||||
| 6 | Regression analysis – use of IBM SPSS in data processing and analysis, presentation of results. | Classes | 1.00 | computer room | |||||
| 7 | Insight into mediation and moderation analysis. | Lectures | 1.00 | computer room | |||||
| 8 | Large scale assessment. | Lectures | 1.00 | computer room | |||||
| 9 | Large scale assessment – use of SPSS in data processing and analysis, presentation of results. | Classes | 1.00 | other | |||||
| 10 | Component analysis and factor analysis. Confirmatory and exploratory factor analysis | Classes | 1.00 | other | |||||
| 11 | Factor extraction methods. Factor rotation methods. | Lectures | 1.00 | other | |||||
| 12 | Factor analysis – use of IBM SPSS in data processing and analysis, presentation of results. | Classes | 1.00 | other | |||||
| Assessment | |||||||||
| Unaided Work: | Students independently read indicated literature. Independently do given data processing tasks (students are given data with fixed tasks in Excel, students do tasks, interpet results, work has to be submitted done in electronic format). | ||||||||
| Assessment Criteria: | (1) Fulfilled homework. The following tasks have to be done with SPSS: 1. General population and sample. 2. Descriptive statistics. 3. Analysis of variance (ANOVA, MANOVA, MANCOVA, mixed design MANOVA). 4. Regression analysis (standard, hierarchical, stepwise). 5. Scale item analysis. 6. Component analysis. Factor analysis. (2) Exam work – independent solution of individual task in SPSS program. The final grade consists of 3 components: attendance, average homework grade mark and exam mark (in the proportions 20: 40: 40). | ||||||||
| Final Examination (Full-Time): | Exam (Written) | ||||||||
| Final Examination (Part-Time): | |||||||||
| Learning Outcomes | |||||||||
| Knowledge: | Students use statistical terminology; explain differences between univariate and multivariate statistical methods; can name and describe univariate and multivariate statistical data processing methods that can be used in different study designs. | ||||||||
| Skills: | Students have technological knowledge in SPSS to process research data, analyse statistical estimators; correctly describe results accordingly to given hypothesis or research question. | ||||||||
| Competencies: | Students can professionaly deal with different psychological research tasks using computer, they use correct data processing methods accordingly to different study designs, analyse and interpet data processing results, formulate correct conclusions about research hypothesis results (accept or reject given hypothesis). | ||||||||
| Bibliography | |||||||||
| No. | Reference | ||||||||
| Required Reading | |||||||||
| 1 | Pētniecība: teorija un prakse (2016). K. Mārtinsones, A. Piperes, D. Kamerādes redakcijā. Rīga: RAKA | ||||||||
| 2 | Field, A. (2018). Discovering Statistics Using IBM SPSS Statistics 5th ed. SAGE | ||||||||
| 3 | Raščevska M. (2005). Psiholoģisko testu un aptauju konstruēšana un adaptācija. Rīga: Raka. | ||||||||
| 4 | Leech, N.L., Barrett, K.C., & Morgan, G.A. (2008). SPSS for intermediate statistics: Use and interpretation. 3rd ed. New York: London: Lawrence Erlbaum Associates. | ||||||||
| Additional Reading | |||||||||
| 1 | Ievads pētniecībā: stratēģijas, dizaini, metodes. (2011). Sastādījusi K. Mārtinsone. Rīga: RAKA. | ||||||||
| 2 | Moore D. S. (2003). The basic practice of statistics. New York: W. H. Freeman & Company. | ||||||||
| 3 | Raščevska M., Kristapsone S. (2000). Statistika psiholoģijas pētījumos. Rīga: Izglītības soļi. | ||||||||
| 4 | Наследов А. Д. (2006). Математические методы психологического исследования. Анализ и интерпретация данных. СПб.: Речь. | ||||||||
| 5 | Сидоренко Е. (2001). Методы математической обработки в психологии. СПб.: Речь. | ||||||||
| 6 | Arhipova, I. Bāliņa, S. (2006). Statistika ekonomikā. Risinājumi ar SPSS un Microsoft Excel. Mācību līdzeklis 2. izdevums. Rīga: Datorzinību Centrs, - 364 lpp. | ||||||||
| 7 | Krastiņš O. (2003) Ekonometrija. Rīga: LR CSP. | ||||||||
| 8 | Krastiņš O., Ciemiņa I. (2003). Statistika. Rīga: LR CSP. | ||||||||
| 9 | Lasmanis, A., Kangro, I. (2004). Faktoru analīze. Rīga: Izglītības soļi. | ||||||||
| Other Information Sources | |||||||||
| 1 | British Journal of Mathematical and Statistical Psychology. Available from: http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)2044-8… | ||||||||
| 2 | Choosing the Correct Statistical Test in SAS, STATA and SPSS. Available from: http://www.ats.ucla.edu/stat/mult_pkg/whatstat/default.htm | ||||||||
| 3 | How to choose a statistical test. Available from: http://www.graphpad.com/www/book/choose.htm | ||||||||
| 4 | Selecting statistics. Available from: http://www.socialresearchmethods.net/selstat/ssstart.htm | ||||||||
| 5 | SPSS tutorials: | ||||||||
| 6 | http://hmdc.harvard.edu/projects/SPSS_Tutorial/spsstut.html | ||||||||
| 7 | http://www.datastep.com/SPSSTutorial_1.pdf | ||||||||
| 8 | http://www.datastep.com/SPSSTutorial_2.pdf | ||||||||
| 9 | Statistics tutorials. Available from: www.statsoft.com/textbook/stathome.html | ||||||||
| 10 | Metodiskie norādījumi maģistra darbu izstrādei RSU veselības psiholoģijas un supervīzjas studiju programmām. / K. Mārtinsone, V. Perepjolkina, J. Ļevina, J. Ļubenko, J. Koļesņikova, K. Vende, D. Kamerāde, J. I. Mihailovs, S. Silniece, J. Duhovska; V. | ||||||||
| 11 | Laerd Statistics: SPSS Statistics Tutorials and Statistical Guides | ||||||||
