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Advanced Statistics in SPSS and AMOS - JSM010

Title:	Advanced Statistics in SPSS and AMOS
Guaranteed by:	Department of Sociology (23-KS)
Faculty:	Faculty of Social Sciences
Actual:	from 2016 to 2017
Semester:	summer
E-Credits:	8
Examination process:	summer s.:written
Hours per week, examination:	summer s.:1/1, Ex [HT]
Capacity:	unlimited / unlimited (unknown)
Min. number of students:	unlimited
4EU+:	no
Virtual mobility / capacity:	no
State of the course:	taught
Language:	English
Teaching methods:	full-time
Teaching methods:	full-time
Note:	course can be enrolled in outside the study plan enabled for web enrollment

Guarantor:	PhDr. Ing. Petr Soukup, Ph.D.
Teacher(s):	PhDr. Ing. Petr Soukup, Ph.D.
Class:	Courses for incoming students

Examination dates Schedule Noticeboard

Files		Comments	Added by
	article_sample202006-0001.pdf	Article sample	PhDr. Ing. Petr Soukup, Ph.D.
	binary_logreg_HW.doc	description of binary logistic HW	PhDr. Ing. Petr Soukup, Ph.D.
	blau_duncan_small.sav	Blau Duncan data (SEM practice)	PhDr. Ing. Petr Soukup, Ph.D.
	CFA_WB.sav	CFA well-being data (1 factor model)	PhDr. Ing. Petr Soukup, Ph.D.
	flats.sav	Data file for linear regression	PhDr. Ing. Petr Soukup, Ph.D.
	INET.sav	Logistic regression data - Internet usage (WIP 2006)	PhDr. Ing. Petr Soukup, Ph.D.
	INET.xlsx	Inet data in MS Excel format	PhDr. Ing. Petr Soukup, Ph.D.
	ISSP_EFA.sav	Lecture 8 Exploratory factor analysis - data file	PhDr. Ing. Petr Soukup, Ph.D.
	lecture1_Advanced_2022.ppt	Lecture 1 recap	PhDr. Ing. Petr Soukup, Ph.D.
	library.sav	data for multinomial log reg	PhDr. Ing. Petr Soukup, Ph.D.
	logregr_probability_curve.xls	How to prepare probability curve in Excel or in Jamovi	PhDr. Ing. Petr Soukup, Ph.D.
	math_4_lca.sav	Lecture 7- data for attitudes for mathematics	PhDr. Ing. Petr Soukup, Ph.D.
	motivation_ind.sav	Lecture 7 - Motivation for LCA data	PhDr. Ing. Petr Soukup, Ph.D.
	MOTIVATION_lca.xls	Lecture 7 - Motivation for LCA	PhDr. Ing. Petr Soukup, Ph.D.
	Regression_HW.doc	description of regression lectures and HW1	PhDr. Ing. Petr Soukup, Ph.D.
	SEM_reg_cor.sav	SEM Intro data	PhDr. Ing. Petr Soukup, Ph.D.
	Titanic.csv	Logistic regression data - Titanic	PhDr. Ing. Petr Soukup, Ph.D.
	11_EVS99_CFA.sav	CFA EVS data for practising	PhDr. Ing. Petr Soukup, Ph.D.
	11_MI_CESD8.sav	MI CESD8 data	PhDr. Ing. Petr Soukup, Ph.D.
	11_MI_intel.sav	MI data	PhDr. Ing. Petr Soukup, Ph.D.

Literature

Last update: PhDr. Petr Bednařík, Ph.D. (12.08.2020)

Obligatory:

Field. A. 2009 Discovering statistics using SPSS. Sage

Suggested:

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

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

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

Syllabus

Last update: 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.