Computer statistics

intermediate

PS_U06

The aim of the course is to prepare the student to independently carry out basic statistical analyzes in the SPSS package. The acquired knowledge and skills are necessary to write an MA thesis independently and to better understand empirical research reports in psychological literature. Classes are workshop, take place at computers with SPSS software.

1. Repetition of basic information about the research process in psychology

- research problem, research question, hypotheses

- directional and non-directional hypotheses

- null hypotheses and research hypotheses

- measuring scale and research hypothesis

- emphasis on diversity: relationships between variables and differences between groups

2. Introduction to SPSS - discussion of program functionality and specifics of data entry:

- three types of SPSS files: database, report, commands

- SPSS database: variables (all functionalities), data, rows, columns

- creating variables and entering data into SPSS

- SPSS and other file formats (Excel, dat.)

- combining sets, division into subsets, selection of observations

3. Basic ways of creating indicators (mean, sum). Recoding variables.

4. Description and presentation of data:

- frequency tables and charts,

- descriptive statistics (measures of central tendency, measures of dispersion, measures of shape of the distribution) and tests of distribution normality

5. Basics of statistical inference and its errors (repetition). Tests using the chi-square distribution (form of hypotheses - type of data - assumptions)

- cross tables (observed numbers and expected numbers)

- chi2, McNemar, Phi and V Cramer tests

6-7. Student's t-tests (form of hypotheses - type of data - assumptions and methods of their verification) and their nonparametric equivalents (Welch, Wilcoxon, U-Mann-Whitney tests):

8-9. Analysis of variance (form of hypotheses - type of data - assumptions and methods of their verification):

- one-factor, one-dimensional analysis of variance

- post hoc tests (concept of main effect) and contrasts

- two-factor, one-dimensional analysis of variance (the concept of simple effects and interactions)

- non-parametric Kruskal-Wallis test

10-11. Relationships between variables: correlations and regressions (form of hypotheses - type of data - assumptions and methods of their verification)

- correlation: assumptions, charts

- r-Pearson, rho-Spearman, tau-Kendall correlation

- partial correlation

- statistical inference: significance and strength of the relationship

- regression analysis in relation to r-Pearson correlation, partial correlation, and semi-partial correlation

- regression analysis with one predictor and with multiple predictors

12-13. Measurement reliability:

- Cronbach's alpha (internal consistency of the scale)

- exploratory factor analysis

14. Repetition and reporting of results in accordance with APA style (structure of the empirical report)

15. Test from classes 1-13

Bedyńska, S., Książek, M. (2012). Statystyczny drogowskaz, wydanie 3-tomowe. Warszawa: Wydawnictwo Akademickie Sedno.

Field, A. (2014). Discovering statistics using IBM SPSS statistics, fourth edition. Sage.

Educational outcomes

KNOWLEDGE:

- student distinguishes and characterizes basic statistical analyzes

SKILLS:

- performs basic analysis in SPSS and interprets their results

COMPETENCES:

- maintains criticism of the results of statistical analysis as a tool used to verify theoretical theses

- Insufficient (2): the student gives fragmentary or incorrect definitions of basic statistical terms (such as variance, level of significance of inference, type I and type II errors, etc.), or does not know them at all. He or she is not able to correctly use the statistical methods discussed during classes, or uses them thoughtlessly, without taking into account their conditions. The student formulates incorrect or unauthorized conclusions, inadequately using statistical terminology. He or she provides explanations or justifications unrelated or having little relevance to the problem being analyzed. Moreover, the student is often unable to identify this problem correctly. He or she does not pay attention to errors in the selection of statistical tests and draws incorrect conclusions on its basis.

- Sufficient (3): the student is able to give correct definitions of basic statistical terms. However, he or she is not able to synthesize knowledge of a specific problem. To a limited extent, he or she uses his or her knowledge to solve specific statistical problems and justify the adopted solutions. He or she is able to correctly use some of the statistical methods discussed during classes, but omits others or does not apply them correctly. Moreover, his or her justifications are often incomplete or unclear. Presenting the received solutions, the student sometimes incorrectly uses statistical terminology. It happens that he or she chooses wrong statistical tests and draws incorrect conclusions based on them.

Good (4): the student not only correctly defines the basic statistical terms but is able to synthesize information on a given topic, consistently presenting most statistical issues. The explanations he or she provides may contain some gaps. Although they are not always exhaustive, they have a logical structure. He or she correctly uses the statistical methods discussed during classes, although he or she sometimes misses some - sometimes even key - assumptions in analyzing the problem. The solutions received are presented using the correct statistical terminology. The student can capture wrong decisions regarding statistical tests, improve them and formulate appropriate conclusions.

Very good (5): the student is perfectly familiar with the discussed statistical issues and is able to present them in a logical, coherent, and exhaustive manner, using statistical terminology correctly and precisely. He or she is able to make a comprehensive analysis of a specific statistical problem, taking into account all available information and justify the choice of the proposed solution. The student is sensitive to inconsistencies and mistakes in presenting statistical issues and is able to capture and correct them. He or she correctly uses statistical methods discussed during classes, and can also discuss their limitations. The student rarely makes the wrong decisions about statistical tests, but corrects them and draws the appropriate conclusions.

The final grade is based on:

- attendance at classes

- activity during classes (30%)

- final test verifying the skills presented in class (analysis and interpretation of data from the tutor's database) (70%)