Data Analysis in Psychology

Overall objective: The aim of this subject will be to introduce students in the application of Statistics and statistical software to the analysis of data relevant to the domain of Psychology.

Specific objectives: As a result of the learning it is expected that the student acquire skills…
- To organize data from professional development so they can be statistically analysed using specific software.
- To carry on basic statistical analyses using software packages such as SPSS or other similar and complementary tools.
- To make statistical decisions through the application of criteria based on the properties of data and on the knowledge of probability distributions.
- To interpret the meaning of basic calculations for descriptive and inferential statistics.
- To make reports on the methodological process and on the results obtained from data gathered in a systematic fashion.

INTRODUCTION TO SUBJECT
The subject in context.
General concepts. Levels of measurement and types of variables. (3 hours)


PART ONE. DESCRIPTIVE STATISTICS
1. Introduction to numerical and graphical description of variables. (2 hours)
Frequency distributions. Graphical representations. First position indices: Percentiles.

2. Central tendency indices. (1 hour)
Definition and calculation of Arithmetic Mean. Relationship with Median and Mode.

3. Variability indices. (2 hours)
Definition and calculation of Variance and Standard Deviation.

4. Standard scores. (2 hours)
Definition of standard scores. Relationship between standard scores and the Normal Curve. Related scores: T scores.

5. Shape indices. (1 hour)
Concept of Skewness and Kurtosis. Types and interpretation of skewness and kurtosis coefficients.

6. Relations between variables. (2 hours)
Concept of linear relation. Definition and calculation of Covariance. Definition, calculation, and interpretation of Pearson’s Correlation Coefficient. Correlation matrix.

7. Linear Regression. (3 hours)
Concept of Linear Regression. Identification and interpretation of the Linear Regression Model.


PART TWO. STATISTICAL INFERENCE
8. Parameter estimation. (2 hours)
Sample and Population. Statistics and Parameters. Parameters estimation. Arithmetic Mean Estimation. Variance Estimation. Proportion Estimation. Sample selection.

9. Statistical Hypotheses Testing. (2 hours)
Concept of Statistical Hypotheses Testing. Null Hypothesis. Alternative Hypothesis. Level of significance. Level of confidence. Statistical testing for Arithmetic Mean. Statistical testing for Proportion.

10. Means comparison. (4 hours)
Concept of Means comparison. Two independent samples. Testing of equality of Variances hypothesis. Equal Variances. Unequal Variances. Two related samples.

11. Contingency tables. (2 hours)
Concept of contingency tables. Testing of independency hypothesis in contingency tables. Yates’ correction.

12. Nonparametric inference. (2 hours)
Objectives of nonparametric inference. Mann-Whitney's test for independent samples. Wilcoxon’s test for related samples.

13. Statistical tests in Correlation and Regression. (2 hours)
Statistical test for Pearson’s Correlation Coefficient. Statistical tests for Regression Coefficients.

BASIC BIBLIOGRAPHY
Botella, J., Suero, M., & Ximénez, C. (2012, electronic resource). Análisis de datos en psicología I. Pirámide.
Garriga, A. J., Lubin, P., Merino, J. M., Padilla, M., Recio, P., & Suárez, J. C. (2010, electronic resource). Introducción al análisis de datos. UNED.
Pardo, A., & Ruiz, M. A. (2014, electronic resource). Gestión de datos con SPSS statistics. Síntesis.
Pardo, A., Ruiz, M. A., & San Martín, R. (2009). Análisis de datos en ciencias sociales y de la salud I. Síntesis. (Library code: PS5 1358/1)
Prasad, S. (2023, electronic resource). Elementary statistical methods. Springer.

COMPLEMENTARY BIBLIOGRAPHY
Aron, A., Coups, E. J., Aron, E. N., & Cooley, E. (2022). Statistics for psychology (7th ed.). Pearson.
Gil, J. A. (2015, electronic resource). Estadística e informática (SPSS) en la investigación descriptiva e inferencial (2nd ed.). UNED.
Guàrdia, J., Freixa, M., Però, M., & Turbany, J. (2008). Análisis de datos en psicología (2nd ed.). Delta Publicaciones Universitarias. (PS5 2809)
Howell, D. C. (2013). Statistical methods for psychology (8th ed.). Cengage Learning. (PS5 2882)
Lomax, R. G., & Hahs-Vaughn, D. L. (2012). An introduction to statistical concepts (3rd ed.). Routledge. (PS5 1993)
Pardo, A., & San Martín, R. (2011, electronic resource). Análisis de datos en ciencias sociales y de la salud II. Síntesis.
Peña, D., & Romo, J. (2003). Introducción a la estadística para las ciencias sociales. McGraw-Hill. (PS5 2721)
Ritchey, F. J. (2008). Estadística para las ciencias sociales (2nd ed.). McGraw-Hill. (Library code Concepción Arenal: FS2 63)
Solanas, A., Salafranca, L., Fauguet, J., & Núñez, I. (2005). Estadística descriptiva en ciencias del comportamiento. Thomson. (PS5 2609)
TIBCO Software Inc. (2020). Data science textbook. https://docs.tibco.com/data-science/textbook

As part of the training module Methods, designs and techniques of psychological research, this subject is linked to the following specific professional competences of the degree:
SC1 (Specific Competence 1)- To demonstrate knowledge and comprehension of functions, characteristics, contributions, and limitations of the diverse theoretical models for Psychology.
SC6- To prove knowledge and comprehension of the research methods and designs, and the procedures for data analysis in Psychology.
SC9- To be able to identify the relevant features of the behaviour of individuals, groups, organisations, and contexts through methods, techniques and tools of psychological assessment.
SC11- To be able to select and apply techniques and instruments specific to Psychology.
SC14- To write psychological reports in different professional fields, addressed to recipients and to other professionals.
SC15- To conform to the ethical obligations of Psychology.

The work to be carried out during the semester can be summarised in the following categories: (1) expositive classes, (2) interactive classes, (3) tutorials, and (4) course work as a part of formative assessment.

Expositive classes will be carried out for large groups (A, B) in module B classrooms at the Faculty, and will be 70 minutes long. They will be basically devoted to present content, but they will require active participation of students, who will have to solve the questions and illustrative exercises which will be formulated after each unit or small group of units.

Interactive classes will be held at the Data Process Laboratory in module A of the Faculty, in groups of approx. 20 students, who will be provided with computers and specific software for the statistical treatment of numerical data. The main tasks to be performed will consist of recording of data, selection of the appropriate statistical tests, and interpretation of the results.

Tutorials will be for the smallest groups and will be specifically devoted to discuss the development of course work, and to the assessment by professors and students of the progress and difficulties in learning.

ATTENDANCE TO THE PREVIOUSLY MENTIONED ACTIVITIES IS MANDATORY.
The use of the USC's Virtual Campus has also been planned. We will employ the Virtual Site-Course to deposit useful information about the subject, such as programme, schedule of interactive classes and tutorship, exercises, and other materials complementary to teaching in the classroom.

The methodology for students with exemption from class attendance will be based mainly on the Virtual Site-Course.

Formative assessment
Formative assessment will in this course involve the following elements:
- On one side, the exercises which are formulated within each unit or small group of units have the goal of providing the students with a precise idea on the level of knowledge to be required an on their situation to this matter. In addition, they help to clarify concepts and to correct possible errors in learning.
- On another side, a course work will be produced. This work will consist of giving response to several questions on the analysis of data from an example, and of making a report. The process will be similar to that followed during the interactive lessons in the lab. The students are expected to be able to carry on these tasks in an autonomous way, individually or in reduced groups, and to present an individual report about the work developed. The production and handing of this report will be at the end of the first part of the subject. It will be voluntary, although it will contribute to the final grade as specified in the next paragraphs.

Final assessment
This type of assessment will take place at the end of the term (in January 2025) and later in June 2025. It will be an exam of a practical character. This test will be concerned with knowledge and competence which represent the whole subject. It will consist of the resolution in writing and with the help of a calculator of different problems, similar to the ones made in the classroom. For this exam students will have a supporting form at their disposal. They will also be allowed to use statistical tables.

The final grade will be calculated in the following way:
- Formative assessment (periodical exercises + course work): maximum of 3 points (1 point for the activity of exercises and 2 points for the activity of course work).
- Final assessment (exam relative to the whole programme): maximum of 7 points.

To pass the subject it is essential that the student achieve at least a passing grade (Aprobado 5 according to Spanish system).

If a student gets a grade inferior to 5, he or she will have to attend a new exam at one of the future dates at their disposal. In that case, the student will only maintain the grade corresponding to the previous formative assessment till July, when the second official assessment of the same course will take place.

Assessment system will be the same both for attending students and for non-presential petitioners.

The amount of time a student should devote to the subject in order to maximize his/her success in academic achievement should be:
(a) reading course material (18 hours),
(b) autonomous work and writing a report (25 hours),
(c) study (52 hours),
(d) final exam (2 hours).

In order to guarantee success in this subject, it is recommended:
(a) Continued attendance to both expositive and interactive classes. Intermittencies in attendance should be avoided.
(b) Active participation in class dynamic.
(c) Prepare previously taught concepts before each class.

All students may additionally consult any doubt about the subject in the usual tutorials timetable. The tutorials schedule for the first term of the year is as follows:
- Eulogio Real Deus. Monday, Tuesday and Thursday, from 16:00 to 18:00.
- Elena M. Andrade Fernández. Tuesday and Wednesday, from 9:00 to 12:00.

The tutorials schedule for each of the professors in charge of the subject will also be displayed in the corresponding teacher’s office:
Eulogio Real: room 67 (module A at the Faculty of Psychology, 2nd floor);
Elena Andrade: room 84 (module B at the Faculty of Psychology, 2nd floor).

The institutional addresses for email inquiries are as follows:
joseeulogio.real@usc.es
elena.andrade@usc.es