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| Soubory | Komentář | Kdo přidal | |
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article_sample202006-0001.pdf | Article sample | PhDr. Ing. Petr Soukup, Ph.D. |
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binary_logreg_HW.doc | description of binary logistic HW | PhDr. Ing. Petr Soukup, Ph.D. |
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blau_duncan_small.sav | Blau Duncan data (SEM practice) | PhDr. Ing. Petr Soukup, Ph.D. |
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CFA_WB.sav | CFA well-being data (1 factor model) | PhDr. Ing. Petr Soukup, Ph.D. |
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flats.sav | Data file for linear regression | PhDr. Ing. Petr Soukup, Ph.D. |
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INET.sav | Logistic regression data - Internet usage (WIP 2006) | PhDr. Ing. Petr Soukup, Ph.D. |
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INET.xlsx | Inet data in MS Excel format | PhDr. Ing. Petr Soukup, Ph.D. |
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ISSP_EFA.sav | Lecture 8 Exploratory factor analysis - data file | PhDr. Ing. Petr Soukup, Ph.D. |
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lecture1_Advanced_2022.ppt | Lecture 1 recap | PhDr. Ing. Petr Soukup, Ph.D. |
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library.sav | data for multinomial log reg | PhDr. Ing. Petr Soukup, Ph.D. |
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logregr_probability_curve.xls | How to prepare probability curve in Excel or in Jamovi | PhDr. Ing. Petr Soukup, Ph.D. |
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math_4_lca.sav | Lecture 7- data for attitudes for mathematics | PhDr. Ing. Petr Soukup, Ph.D. |
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motivation_ind.sav | Lecture 7 - Motivation for LCA data | PhDr. Ing. Petr Soukup, Ph.D. |
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MOTIVATION_lca.xls | Lecture 7 - Motivation for LCA | PhDr. Ing. Petr Soukup, Ph.D. |
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Regression_HW.doc | description of regression lectures and HW1 | PhDr. Ing. Petr Soukup, Ph.D. |
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SEM_reg_cor.sav | SEM Intro data | PhDr. Ing. Petr Soukup, Ph.D. |
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Titanic.csv | Logistic regression data - Titanic | PhDr. Ing. Petr Soukup, Ph.D. |
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11_EVS99_CFA.sav | CFA EVS data for practising | PhDr. Ing. Petr Soukup, Ph.D. |
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11_MI_CESD8.sav | MI CESD8 data | PhDr. Ing. Petr Soukup, Ph.D. |
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11_MI_intel.sav | MI data | PhDr. Ing. Petr Soukup, Ph.D. |
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Poslední úprava: Mirna Jusić, M.A., Ph.D. (26.02.2024)
This course is an overview course over topics of the use of statistics in sociology, which will cover advanced statistical procedures: linear regression, logistic regression, latent class analysis, exploratory factor analysis and confirmatory factor analysis. All procedures will be demonstrated and practised in SPSS, JASP and Jamovi(2024). It is possible to follow the lecture online via MS Teams: https://teams.microsoft.com/l/meetup-join/19%3ameeting_OTNiNDFjOTYtMDQxZC00ODE3LTgxYmQtZjBmY2I5NDBjZmFh%40thread.v2/0?context=%7b%22Tid%22%3a%2273844aaf-f10c-4dee-aaaf-5eeb27962a5d%22%2c%22Oid%22%3a%2244019797-e6cf-458d-996e-9e9b298c7895%22%7d Recordings will be available for all lectures: https://drive.google.com/drive/folders/1a9xBZCu9Um8RAYAkUoUVlPDGtdWIeGgU?usp=sharing Link for questionnaire for 1st lecture: https://forms.gle/44UJ2b5wQVkuRQbR6 |
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Poslední úprava: PhDr. Ing. Petr Soukup, Ph.D. (15.02.2024)
Exam consist of 5 homework, oral exam and draft (article) (every part is evaluated separately 0-100 %). Draft (article) should use classical structure: problem formulation (derived from theory), hypotheses formulation and statistical analysis for hypotheses evaluation (at least two techniques from sylabus). Weights for final evalution: every hw 10 %, oral exam 20 % ( for MA students) and draft 30 % (only for MA students). Final grading: 0-50 % 4 (failed), 51 % - 60 % E, ), 61 % - 70 % D, 71-80 % C, ), 81 % - 90 % B and 91 % and more A. |
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Poslední úprava: Mirna Jusić, M.A., Ph.D. (26.02.2024)
Field. A. 2009 Discovering statistics using SPSS. Sage Norusis, M. 2005. Advanced Statistical Procedure Companion. Prentice Hall. Tarling, R. 2009. Statistical Modelling for Social Researchers, Routledge. |
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Poslední úprava: PhDr. Ing. Petr Soukup, Ph.D. (15.02.2024)
The course will be held in Jinonice building, computer lab 229 Online access is possible via MS Teams: 1. Introduction to SPSS syntax language. Descriptive statistics and correlation analysis in SPSS. Missing values, results, handling and replacing. Data weighting. (1 lecture) 2. Linear regression analysis - simple and multiple regression. Assumptions, model fit, possible modification of regression model. Model evaluation and interpretation. Dummy variables, multicollinearity, influential points, heteroscedasticity. Robust regression.(1st HW) (2 lectures) 3. Logistic regression - binary, ordinal and polytomous model. Odds, odd ratio, logit. Model evaluation and interpretation. (2nd HW) (2 lectures) 5. Latent class analysis (typology from binary and nominal variables). Explanatory and confirmatory approach. Unconditional latent class probability and conditional probability of individual answer. Comparison of models (decision about the number of latent classes). (3rd HW) (1 lectures) 6. Exploratory factor analysis. Assumptions, number of factors, Extraction and rotation. Factor weights and interpretation of factors. Factor scores and it’s usage. (4th HW) (1 lecture) 7. Introduction to SEM. Correlation and regression as SEM model. Path analysis. Evaluation of SEM. 8. Confirmatory factor analysis for cardinal, ordinal and binary indicators. Model fit indices and criteria. Basic equations and graphical presentation. Modification indices. (5th HW) (2 lectures) Exam consist of 5 homework and oral exam (every part is evaluated separately 0-100 %). Weights for final evalution: every hw 10 %, oral exam 50 % (for BA students). Final grading: 0-50 % 4 (failed), 51 % - 60 % E, ), 61 % - 70 % D, 71-80 % C, ), 81 % - 90 % B and 91 % and more A. |