UMH - DATA ANALYSIS IN BEHAVIORAL SCIENCES

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DATA ANALYSIS IN BEHAVIORAL SCIENCES

Course

Code: 938

Degree: Bachelor's in Psychology

Faculty of Health and Social Sciences

Year: Year 1 of Bachelor's in Psychology

Semester: Fall

Type: Core

Language: Spanish

ECTS credits: 6

Lecture: 3

Laboratory: 3

Hours: 150

Directed: 60

Shared: 31

Autonomous: 59

Subject matter: Statistics

Module: Core

Department: Health Psychology

Area: BEHAVIORAL SCIENCES METHODOLOGY

Course instructors are responsible for the course content descriptions in English.

Description

Observational, selective, and experimental methodologies. Qualitative and quantitative methodologies. Descriptive statistics. Statistical inference.

Faculty

Name	Coordinator	Lecture	Laboratory
CAÑADAS OSINSKI, ISABEL	■	■	■
PINEDA SANCHEZ, DAVID			■

Professional interest

The application of statistics to the different disciplines often receives the name "Data Analysis." The data analysis has gained increasing importance within the different scientific areas, especially within the so-called Social Sciences and Health.
The overall purpose of the subject is the one to provide procedures to give answer to issues of interest for the psychologists by the statistical treatment of data.
In his professional psychologist have multiple data must know how to organize, codify, analyze and interpret: patient data when working in the clinical setting , of students if you work in the education, of employees when working in the organizational, etc. .. That is, a future psychologist must properly interpret the reports relevant to their work area, locating the applied statistical techniques, understanding their goals and understanding the evidence that reflect their results.
The data analysis techniques are required for both empirical research and applied psychology, to the theoretical understanding of the foundations of empirical knowledge of the behavior.

Competencies and learning outcomes

General competencies

Ability to analyze, evaluate, and assess individual and group situations, identify problems, interpret data, and formulate solutions to individual and/or group problems.
Ability to update, consolidate, and integrate new knowledge to improve the exercise of the profession using continuous self-learning techniques and critical analysis.

Specific competencies

Be capable of conducting an interview and correctly applying tests, diagnostics, and other psychological evaluation tools. In its case, be capable of designing basic tools for psychological evaluation and diagnosis.
Know how to adequately analyze and interpret results from psychological evaluations and interventions performed by other professionals.

Objectives (Learning outcomes)

01The student must be able to identify the level of measurement of variables to choose the appropriate statistical treatment and understand the meaning of numbers.
02Students must be able to sort, organize, graph, and summarize statistical data obtained from samples of measurements of a variable.
03L'estudiant ha de ser capaç d'aplicar a la Psicologia la Teoria de la Probabilitat i els seus models més habituals.
04The student must be able to recognize and identify some techniques contrast statistical hypothesis.
05The student must be able to interpret and develop the descriptive and inferential results performed on data from a psychological research.
06The student must be able to handle the SPSS software for data analysis and perform descriptive and inferential statistical analysis with him.

Teaching units

Association between objectives and units

Objective/Unit	U1	U2	U3
01
02
03
04
05
06

Schedule

Week	Teaching units	Directed hours	Shared hours	Autonomous hours	Total hours
1	U1	4	2	3	9
2	U1	6	2	7	15
3	U1	4	1	3	8
4	U1	4	2	3	9
5	U1	4	1	3	8
6	U1	4	1	8	13
7	U1	4	4	3	11
8	U1	0	4	3	7
9	U2	4	1	3	8
10	U2	8	2	3	13
11	U2	0	0	3	3
12	U2	4	1	3	8
13	U2	8	2	3	13
14	U3	4	4	3	11
15	U3	2	4	8	14

Basic bibliography

Botella, Juan (Botella Ausina) / Suero Suñe, Manuel / Ximénez, M. Carmen. "Análisis de datos en psicología I teoría y ejercicios". Madrid Pirámide D.L. 2012.
Botella, Juan (Botella Ausina). "Análisis de datos en psicología I Teoría y ejercicios". Madrid Pirámide 2006.

Complementary bibliography

Amón, Jesús. "Estadística para psicologos". Madrid Pirámide 1993.
Ato Garcia, Manuel. López García, Juan José. "Análisis estadístico para datos categóricos". Madrid Síntesis 1996.
Pardo, Antonio. San Martín, Rafael. "Análisis de datos en psicología". Madrid Pirámide 1999.
Pardo Merino, Antonio. Ruiz, Miguel Ángel 1960-. "Análisis de datos con SPSS 13 Base". Madrid McGraw-Hill 2005.
Pérez López, César 1955-. "Muestreo estadístico conceptos y problemas resueltos ". Madrid [etc.] Pearson-Prentice Hall 2006.
Prieto Valiente, Luis. Herranz Tejedor, Inmaculada. "{Qué significa "estadísticamente significativo"? La falacia del criterio de 5% en la investigación científica ". Madrid Díaz de Santos cop. 2005.
Rodríguez Osuna, Jacinto. "Métodos de muestreo". Madrid Centro de Investigaciones Sociológicas 1991.
Rodríguez Osuna, Jacinto. "Métodos de muestreo casos prácticos". Madrid Centro de In vestigaciones Sociológicas 1993.
Tomeo Perucha, Venancio. Uña Juárez, Isaías. "Estadistica descriptiva". Madrid Ibergarceta 2009.

Software

SPSS 19

Methodology and grading

Methodology

Case studies: Learning through the analysis of actual or simulated cases in order to interpret and resolve them by employing various alternative solution procedures.
Cooperative learning: Develop active learning through cooperative working strategies among students and promote shared responsibility to reach group goals.
Learning agreement: Develop active learning through an agreement between the teacher and the student containing commitments for what will be learned, how monitoring will take place for that learning, the set period of time, and the evaluation criteria.
Lecture: Pass on knowledge and activate cognitive processes in students, encouraging their participation.
Problem-based learning: Develop active learning strategies through problem solving that promote thinking, experimentation, and decision making in the student.
Solving exercises and problems: Exercise, test, and apply previous knowledge through routine repetition.

Grading

1) THEORETICAL MODULE: Test. 2) PRACTICAL MODULE: Presentation of a inform. 3) FINAL SCORE ON THE SUBJECT 0.5 * Theory Score + 0.5 * Practice Score ---------------------------------------------------------------- Grading scale: Will apply the grading system established by Royal Decree 1125/2003, of September 5.

Research: DATA ANALYSIS IN BEHAVIORAL SCIENCES