PředmětyPředměty(verze: 902)
Předmět, akademický rok 2021/2022
   Přihlásit přes CAS
Advanced Statistics in SPSS and AMOS - JSM010
Anglický název: Advanced Statistics in SPSS and AMOS
Zajišťuje: Katedra sociologie (23-KS)
Fakulta: Fakulta sociálních věd
Platnost: od 2021
Semestr: letní
E-Kredity: 8
Způsob provedení zkoušky: letní s.:písemná
Rozsah, examinace: letní s.:1/1 [hodiny/týden]
Počet míst: neomezen / neomezen (neurčen)
Minimální obsazenost: neomezen
Virtuální mobilita / počet míst: ne
Stav předmětu: vyučován
Jazyk výuky: angličtina
Způsob výuky: prezenční
Další informace: http://samba.fsv.cuni.cz/~soukp6as/ADVANCED_STATISTICS/
Poznámka: předmět je možno zapsat mimo plán
povolen pro zápis po webu
Garant: PhDr. Ing. Petr Soukup, Ph.D.
Vyučující: PhDr. Ing. Petr Soukup, Ph.D.
Třída: Courses for incoming students
Soubory Komentář Kdo přidal
stáhnout article_sample202006-0001.pdf Article sample PhDr. Ing. Petr Soukup, Ph.D.
stáhnout binary_logreg_HW.doc description of binary logistic HW PhDr. Ing. Petr Soukup, Ph.D.
stáhnout blau_duncan_small.sav Blau Duncan data (SEM practice) PhDr. Ing. Petr Soukup, Ph.D.
stáhnout CFA_WB.sav CFA well-being data (1 factor model) PhDr. Ing. Petr Soukup, Ph.D.
stáhnout flats.omv linear regression for flats - complete computation in JAMOVI PhDr. Ing. Petr Soukup, Ph.D.
stáhnout flats.sav Data file for linear regression PhDr. Ing. Petr Soukup, Ph.D.
stáhnout INET.omv Logistic regression for Internet data PhDr. Ing. Petr Soukup, Ph.D.
stáhnout INET.sav Logistic regression data - Internet usage (WIP 2006) PhDr. Ing. Petr Soukup, Ph.D.
stáhnout INET.xlsx Inet data in MS Excel format PhDr. Ing. Petr Soukup, Ph.D.
stáhnout ISSP_EFA.sav Lecture 8 Exploratory factor analysis - data file PhDr. Ing. Petr Soukup, Ph.D.
stáhnout lecture1_Advanced_2022.ppt Lecture 1 recap PhDr. Ing. Petr Soukup, Ph.D.
stáhnout lecture_11_Advanced_CFA.ppt Lecture 11 CFA PhDr. Ing. Petr Soukup, Ph.D.
stáhnout lecture_12_Advanced_CFA.IIppt.ppt Lecture 11 CFA II PhDr. Ing. Petr Soukup, Ph.D.
stáhnout lecture2_Advanced.ppt Lecture 2 Linear regression PhDr. Ing. Petr Soukup, Ph.D.
stáhnout lecture3_Advanced_2022.ppt Linear regression diagnostics PhDr. Ing. Petr Soukup, Ph.D.
stáhnout LECTURE4_5_Advanced_2022.ppt Lecture 4/5 Logistic regression PhDr. Ing. Petr Soukup, Ph.D.
stáhnout lecture7_LCA_Advanced_2022.ppt Lecture 7 Latent class analysis PhDr. Ing. Petr Soukup, Ph.D.
stáhnout lecture8_LCA_Advanced_2022.ppt Lecture 8 Exploratory factor analysis PhDr. Ing. Petr Soukup, Ph.D.
stáhnout lecure10_Advanced_2022_Path_analysis.ppt Lecture 10 Path analysis PhDr. Ing. Petr Soukup, Ph.D.
stáhnout lecure9_Advanced_2022_Intro_SEM.ppt Lecture 9 Intro to SEM PhDr. Ing. Petr Soukup, Ph.D.
stáhnout library.sav data for multinomial log reg PhDr. Ing. Petr Soukup, Ph.D.
stáhnout logregr_probability_curve.xls How to prepare probability curve in Excel or in Jamovi PhDr. Ing. Petr Soukup, Ph.D.
stáhnout math_4_lca.omv LCA results in JAMOVI PhDr. Ing. Petr Soukup, Ph.D.
stáhnout math_4_lca.sav Lecture 7- data for attitudes for mathematics PhDr. Ing. Petr Soukup, Ph.D.
stáhnout motivation_ind.sav Lecture 7 - Motivation for LCA data PhDr. Ing. Petr Soukup, Ph.D.
stáhnout MOTIVATION_lca.xls Lecture 7 - Motivation for LCA PhDr. Ing. Petr Soukup, Ph.D.
stáhnout Regression_HW.doc description of regression lectures and HW1 PhDr. Ing. Petr Soukup, Ph.D.
stáhnout SEM_reg_cor.sav SEM Intro data PhDr. Ing. Petr Soukup, Ph.D.
stáhnout Titanic.csv Logistic regression data - Titanic PhDr. Ing. Petr Soukup, Ph.D.
stáhnout Titanic.omv Logistic regression for Titanic data PhDr. Ing. Petr Soukup, Ph.D.
stáhnout 11_EVS99_CFA.sav CFA EVS data for practising PhDr. Ing. Petr Soukup, Ph.D.
stáhnout 11_MI_CESD8.sav MI CESD8 data PhDr. Ing. Petr Soukup, Ph.D.
stáhnout 11_MI_intel.sav MI data PhDr. Ing. Petr Soukup, Ph.D.
Anotace - angličtina
Poslední úprava: PhDr. Ing. Petr Soukup, Ph.D. (29.03.2022)
This course is a survey course over topics of the use of statistics in sociology, which will cover advanced statistical procedures: linear regression, logistic regression,
loglinear models, latent class analysis, exploratory factor analysis and confirmatory factor analysis. All procedures will be demonstrated and practised in SPSS, JASP, JAMOVI, Stata and other software.
Podmínky zakončení předmětu - angličtina
Poslední úprava: PhDr. Ing. Petr Soukup, Ph.D. (02.02.2022)

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.

Literatura - angličtina
Poslední úprava: PhDr. Petr Bednařík, Ph.D. (13.08.2020)

Obligatory:

Field. A. 2009 Discovering statistics using SPSS. Sage

Suggested:

Byrne, B. 2010. Structural equation modeling with AMOS. Taylor & Francis Ltd. (ch.1-3)

Norušis, M. 2005. Advanced Statistical Procedure Companion. Prentice Hall.

Tarling, R. 2009. Statistical Modelling for Social Researchers. Routledge. 

 

Sylabus - angličtina
Poslední úprava: PhDr. Ing. Petr Soukup, Ph.D. (29.03.2022)

The course will be held in Pekarska building, computer lab 308

Online access is possible via ZOOM:

https://cesnet.zoom.us/j/4677639176?pwd=dVAyWkJISGRrOGp2ZUJRc0I2Y3p2UT09

Meeting ID: 467 763 9176

Passcode: 355749

 

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) 

4.      Loglinear models (advanced contingency tables).  Model evaluation and interpretation.  (1 lecture)

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.      Confirmatory factor analysis for cardinal, ordinal and binary indicators. Model fit indices and criteria. Basic equations and graphical presentation. Modification indices, Bayesian estimation for nominal and ordinal data. (5th HW)  (2 lectures)

 
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