Statistics

elementary

PS_W06

PS_U06

none

The Statistics course provides an introduction to descriptive statistics and statistical inference. The discussed issues mainly concern key statistical concepts, characteristics of data obtained from the study and the basics of statistical inference (the theory of estimation and the theory of statistical hypotheses testing).

Concepts are introduced and discussed in the seminar and then are applied through exercises in practical classes (with the use of the SPSS statistical software). Students who successfully complete this course will possess basic data analysis skills and should be able to demonstrate comprehension of basic statistical concepts and statistical inference.

Statistics is the science of collecting, organizing, analyzing and interpreting data. The data itself is useless and can only be interpreted in the context of the research that generated the data. It is thanks to statistics that it is possible to discover the order hidden in the collected data, to reveal patterns and trends contained in them.

For a psychologist, statistics is a tool of analysis that allows to find answers to the research questions posed in the light of the data obtained in the study. Thus, it mainly concerns the relationship between the data obtained from the study and the research problem posed. The use of statistics allows us to draw conclusions on the basis of data, taking into account their variability and the assessment of the degree of uncertainty of formulated conclusions. Learning from data, understanding variability, and evaluating probability are the basic skills that this course is devoted to mastering.

They are particularly important due to the presence on the market of many computer statistical packages allowing for efficient and quick data analysis, and which - unfortunately - are very easy to use without any understanding of the results offered by the package.

The program of the course covers the basic statistical concepts, which are necessary to build any statistical description of analyzed variables. The basic assumptions of statistical inference are also introduced. The discussed issues mainly concern key statistical concepts, characteristics of data obtained from the study and the basics of statistical inference (the theory of estimation and the theory of statistical hypotheses testing).

Concepts are introduced and discussed in the seminar and then are applied through exercises in practical classes (with the use of the SPSS statistical software). Students who successfully complete this course will possess basic data analysis skills and should be able to demonstrate comprehension of basic statistical concepts and statistical inference.

Rekomendowana literatura stanowi literaturę kompleksową, z której studenci mogą dokonać wyboru.

Aczel, A. D., Sounderpandian, J. (2017). Statystyka w zarządzaniu. PWN.

Bedyńska, S., Cypryańska, M. (red.). (2021). Statystyczny drogowskaz 1. Wprowadzenie do wnioskowania statystycznego. Wydawnictwo Akademickie Sedno.

Blalock, H. M. (1977). Statystyka dla socjologów. PWN.

Dancey, C., Reidy, J. (2020). Statistics without Maths for Psychology. Pearson.

Ferguson, G. A., Takane, Y. (2020). Analiza statystyczna w psychologii i pedagogice. PWN.

Field, A. (2016). An adventure in statistics. The reality enigma. Sage.

Field, A. (2018). Discovering Statistics Using IBM SPSS Statistics. Sage.

Francuz, P., Mackiewicz, R. (2007). Liczby nie wiedzą, skąd pochodzą. Przewodnik po metodologii i statystyce nie tylko dla psychologów. Wydawnictwo KUL.

Howell, D. C. (2010). Statistical methods for psychology. Thomson Wadsworth.

Józefacka, N. M., Kołek, M. F., Arciszewska-Leszczuk, A., Iwankowski, P. (2023). Metodologia i statystyka Przewodnik naukowego turysty Tom 1. PWN

King, B. M., Minium, E., W. (2009). Statystyka dla psychologów i pedagogów. PWN.

Knowledge - a Student knows and understands:

• basic statistical concepts (population, sample, random variable, measurement) and basic measurement scales

• basic characteristics used to describe a distribution of the results (measures of central tendency, dispersion, skewness and kurtosis)

• rules for standardizing raw scores

• properties of normal distribution

• procedure for constructing the confidence interval for the expected value of the population

• basic assumptions of statistical inference

• assumptions, goals and procedures of using selected statistical tests (t-tests, Pearson's correlation coefficient r)

Skills - a Student is able to:

• create and conduct a survey using Google forms

• create and manage dataset using IBM SPSS Statistics

• visualize the obtained data

• compute and interpret basic descriptive statistics using IBM SPSS Statistics

• conduct basic statistical analyses: comparisons between two groups and relationship between two variables (t-Student tests, Pearson's correlation) and interpret the results

• based on the conducted analyses, write up a report according to the APA standards

ECTS:

Seminar - 30 hours

Practical classes - 30 hours

Students’ preparations for the seminar - 30 hours

Students’ preparations for the practical classes – 45 hours

Students’ preparation for the final assessments – 45 hours

TOTAL – 180 hours [180 : 30 = 6]

ECTS points = 6

Detailed methods and criteria of assessment are given for the lecture and the practical classes separately.

General criteria of assessment:

Insufficient (2): A student knows less than 60,0% of basic statistical concepts, does not understand their meaning, and is not able to use them to describe empirical data. A student is not able to properly use the statistical methods, described in the classes, or uses them without any reflection, without considering their assumptions. He or she formulates incorrect or groundless conclusions and uses the statistical terminology inadequately.

Sufficient (3): A student correctly and with understanding uses at least 60,0% of statistical concepts and mastered the related skills and competences. A student only in a limited scope uses their knowledge to solve and explain statistical problems. A student is able to properly use only some of the statistical methods, described in the classes, but omits other or is not able to use them properly. He or she provides explanations that are incomplete or unclear. A prerequisite, however, is to be able to define the most central statistical terms (such as variance, standard error of statistics and the significance level) and to specify the content of the main limit theorems.

Good (4): A student correctly and with understanding uses at least 80,0% of the knowledge, presented in the course of the semester, has skills and competences related to it. A student knows how a null hypothesis should be tested and is able to correctly make a decision in relation to the null hypothesis, using both the critical value criterion and the p-value. A student is able to properly use the statistical methods, discussed during the lecture, but he or she ignores some of their aspects or assumptions (crucial – at times).

Very good (5): A student mastered a virtually whole scope of material, covered in the semester. He or she is able to correctly chose statistical methods, proper for solving certain research problems. A student is able to analyse a given statistical issue in a comprehensive way, including all available information and explaining the solution. A student correctly uses the statistical methods, presented during the lecture and is able to discuss their limitations.