ECTS credits ECTS credits: 6
ECTS Hours Rules/Memories Student's work ECTS: 99 Hours of tutorials: 3 Expository Class: 24 Interactive Classroom: 24 Total: 150
Use languages Spanish, Galician
Type: Ordinary Degree Subject RD 1393/2007 - 822/2021
Departments: Social, Basic and Methodological Psychology, Political Science and Sociology
Areas: Behavioural Science Methodology, Organisational Psychology, Legal Forensics and Behavioural Science Methodology
Center Faculty of Psychology
Call: First Semester
Teaching: With teaching
Enrolment: Enrollable | 1st year (Yes)
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 Asymmetry 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 Variance. 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
Arce, C., & Real, E. (2001). Introducción al análisis estadístico con SPSS para Windows. Barcelona: PPU. (Library code: PS5 2344; material also accessible in the Virtual Course of the subject.)
Botella, J., Suero, M., & Ximénez, C. (2012, electronic resource). Análisis de datos en psicología I. Madrid: Pirámide.
Garriga, A. J. (2010, electronic resource). Introducción al análisis de datos. Madrid: UNED.
Pardo, A., & Ruiz, M. A. (2014, electronic resource). Gestión de datos con SPSS statistics. Madrid: Síntesis.
Pardo, A., Ruiz, M. A., & San Martín, R. (2009). Análisis de datos en ciencias sociales y de la salud I. Madrid: Síntesis. (PS5 1358/1; also available at http://www.academia.edu)
COMPLEMENTARY BIBLIOGRAPHY I
Gil, J. A. (2015, electronic resource). Estadística e informática (SPSS) en la investigación descriptiva e inferencial (2ª ed). Madrid: UNED.
Guàrdia, J., Freixa, M., Però, M., & Turbany, J. (2008). Análisis de datos en psicología (2nd ed.). Madrid: Delta Publicaciones Universitarias. (PS5 2809)
Howell, D. C. (2013). Statistical methods for psychology. New York: Cengage Learning. (PS5 2882)
Murillo, F. J., & Martínez-Garrido, C. (2013, electronic resource). Análisis de datos cuantitativos con SPSS en investigación socioeducativa. Madrid: UAM Ediciones.
Pardo, A., & San Martín, R. (2010). Análisis de datos en ciencias sociales y de la salud II. Madrid: Síntesis. (PS5 1358/2)
Pérez, F. J., Manzano, V., & Fazeli, H. (1999). Análisis de datos en psicología. Madrid: Pirámide. (PS5 2725)
Solanas, A., Salafranca, L., Fauguet, J., & Núñez, I. (2005). Estadística descriptiva en ciencias del comportamiento. Madrid: Thomson. (PS5 2609)
COMPLEMENTARY BIBLIOGRAPHY II
Amón, J. (1991). Estadística para psicólogos (Vol. II) (8th ed.). Madrid: Pirámide. (PS5 1419/2)
Aron, A., & Aron, E. N. (1998). Statistics for psychology. New Jersey: Prentice-Hall. (PS5 2074)
Lomax, R. G. & Hahs-Vaughn, D. L. (2012). An introduction to statistical concepts (3ª ed.). New York: Routledge. (PS5 1993)
Peña, D., & Romo, J. (2003). Introducción a la estadística para las ciencias sociales. Madrid: McGraw-Hill. (PS5 2721)
Ritchey, F. J. (2008). Estadística para las ciencias sociales (2nd ed.). Madrid: McGraw-Hill. (Library code Concepción Arenal: FS2 63)
StatSoft, Inc. (2013). Electronic statistics textbook. Tulsa, OK: StatSoft. WEB: http://www.statsoft.com/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 classrooms 1 and 2 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 leave useful information about the subject, such as programme, schedule of interactive classes and tutorship, exercises, and other materials complementary to teaching in the classroom.
Depending on the health situation, the methodology may undergo changes that allow it to be adapted to remote or hybrid teaching-learning scenarios (see Comments).
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 2021) and later in June 2021. 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.
Depending on the health situation, the assessment system may undergo changes that allow it to be adapted to remote or hybrid teaching-learning scenarios (see Comments).
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 (20 hours),
(b) autonomous work and writing a report (30 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 each of the professors in charge of the subject will be reported at the beginning of the course. The schedule for the first semester of the year will appear on the Faculty web page, and also on the corresponding teacher’s office:
Eulogio Real: room 67 (module A at the Faculty of Psychology, 2nd floor);
Elena M. Andrade: room 84 (module B at the Faculty of Psychology, 2nd floor).
The institutional addresses for email inquiries are as follows:
joseeulogio.real [at] usc.es (joseeulogio[dot]real[at]usc[dot]es)
elena.andrade [at] usc.es (elena[dot]andrade[at]usc[dot]es)
Contingency plan
METHODOLOGY
Given the uncertainty caused by the current health situation, some of the contents may be worked autonomously by the student, which will be duly informed through the Virtual Site of the subject.
The tools to be used for remote teaching will be both the University Virtual Campus (asynchronous teaching) and Teams (synchronous teaching) within the expected class schedule.
The monitoring of remote activities will be carried out by scheduling deliveries on the University's Virtual Campus, with the same frequency and difficulty level as those established for the face-to-face teaching scenario.
ASSESSMENT SYSTEM
Depending on the health situation, the final test can be carried out remotely, by combining the questionnaire and task activities of the Virtual Campus. The relative weight of the formative and final assessment would be the same.
For cases of fraudulent conduct of exercises or tests, what is set out in the 'Academic performance assessment of students and qualification review regulations' shall apply.
Jose Eulogio Real Deus
- Department
- Political Science and Sociology
- Area
- Organisational Psychology, Legal Forensics and Behavioural Science Methodology
- Phone
- 881813913
- joseeulogio.real [at] usc.es
- Category
- Professor: University Lecturer
Elena Maria Andrade Fernandez
- Department
- Social, Basic and Methodological Psychology
- Area
- Behavioural Science Methodology
- Phone
- 881813716
- elena.andrade [at] usc.es
- Category
- Professor: University Lecturer
| Monday | |||
|---|---|---|---|
| 11:00-12:25 | I8 I9(2) | Galician, Spanish | Data Processing Teaching Laboratory |
| 12:35-14:00 | I3 | Spanish, Galician | Data Processing Teaching Laboratory |
| 16:00-17:10 | Grupo B (L-Z) | Galician, Spanish | Classroom 7 |
| 17:20-18:30 | Grupo A (A-J) | Galician, Spanish | Classroom 2 |
| Tuesday | |||
| 11:00-12:25 | I4 | Galician, Spanish | Data Processing Teaching Laboratory |
| 12:35-14:00 | I2 | Galician, Spanish | Data Processing Teaching Laboratory |
| 16:00-17:10 | Grupo A (A-J) | Galician, Spanish | Classroom 2 |
| 17:20-18:30 | Grupo B (L-Z) | Spanish, Galician | Classroom 7 |
| Wednesday | |||
| 11:00-12:25 | I6 | Spanish, Galician | Data Processing Teaching Laboratory |
| 12:35-14:00 | I5 | Galician, Spanish | Data Processing Teaching Laboratory |
| Thursday | |||
| 11:00-12:25 | I1 | Galician, Spanish | Data Processing Teaching Laboratory |
| 12:35-14:00 | I7 I9(1) | Galician, Spanish | Data Processing Teaching Laboratory |
| 01.21.2021 09:30-12:00 | Grupo A (A-J) | Classroom 1 |
| 01.21.2021 09:30-12:00 | Grupo B (L-Z) | Classroom 1 |
| 01.21.2021 09:30-12:00 | Grupo B (L-Z) | Classroom 5 |
| 01.21.2021 09:30-12:00 | Grupo A (A-J) | Classroom 5 |
| 01.21.2021 09:30-12:00 | Grupo A (A-J) | Classroom 6 |
| 01.21.2021 09:30-12:00 | Grupo B (L-Z) | Classroom 6 |
| 01.21.2021 09:30-12:00 | Grupo A (A-J) | Classroom 7 |
| 01.21.2021 09:30-12:00 | Grupo B (L-Z) | Classroom 7 |
| 06.21.2021 09:30-12:00 | Grupo B (L-Z) | Classroom 1 |
| 06.21.2021 09:30-12:00 | Grupo A (A-J) | Classroom 1 |
| 06.21.2021 09:30-12:00 | Grupo A (A-J) | Classroom 2 |
| 06.21.2021 09:30-12:00 | Grupo B (L-Z) | Classroom 2 |
| 06.21.2021 09:30-12:00 | Grupo A (A-J) | Classroom 3 |
| 06.21.2021 09:30-12:00 | Grupo B (L-Z) | Classroom 3 |