Universitat Internacional de Catalunya
Methodology Applied to Psychology
Other languages of instruction: Catalan, English
Teaching staff
Course Coordinator: Dr Carlos Gª Forero (cgarciaf@ui.es)
Introduction
Statistics and Psychology have been closely related since the inception of both disciplines. The foundations of Scientific Psychology were conjoined with the beginning of methods for analyzing experimental data. Personality Psychology, Psychophysics or Psychometrics theoretical developments are rooted on results based on the creation of specific statistical techniques by psychologists.
Competence in analysing and analysing quantitative is essential in Health Sciences. Psychology researchers and applied professionals must be able to make inferences from data and to critically assess statistical results to keep up with the latest changes in their fields
Pre-course requirements
It is convenient to have passed the subject Introduction to Psychology Research.
Objectives
General objectives
- Develop an understanding of the importance of data analysis in the context of Psychology and Health Sciences
- Provide tools to appraise the potential and limitation of statistical analysis methods.
- Provide knowledge of the main techniques for statistical analysis of quantitative data.
Specific objectives:
- Systematise procedures for defining research questions as statistical hypotheses.
- Apply statistical analyses for answering research hypothesis
- Develop tools for creating analysis plans
- Establish a framework for understanding techniques for analysing differences and associations
- Appraise the limitations of statistical analysis.
- Write research reports based on statistical information
Competences/Learning outcomes of the degree programme
- CB03 - Students must have the ability to bring together and interpret relevant data (normally within their area of study) in order to issue judgements that include a reflection on relevant issues of a social, scientific and ethical nature.
- CB04 - Students must be able to convey information, ideas, problems and solutions to both specialised and non-specialised audiences.
- CE02 - The ability to write reports on the results obtained during the evaluation process using Psychology-specific language.
- CE03 - The ability to make adequate decisions about what Psychology-specific methods and measuring instruments to use in each situation or evaluation context.
- CG01 - Capacity for critical and creative thinking, and capacity to investigate and adopt a scientific and ethical approach in distinct professional settings.
- CG02 - The ability to make critical and well-founded judgements and assessments as part of the decision-making process.
- CG03 - The ability to read scientific literature in a critical, well-founded manner, take into account its provenance, situate it within an epistemological framework and identify and contrast its contributions in relation to the disciplinary knowledge available.
- CG06 - Flexibility, respect and discretion in the use of data corresponding to people, groups and organisations.
- CT02 - The capacity to solve problems.
- CT03 - The capacity for analysis and synthesis.
- CT04 - The capacity to work in a team
- CT05 - The ability to reason and assess situations and results from a critical, constructive point of view.
Learning outcomes of the subject
After passing the course, students must be able to
1. Autonomously obtain information about data analysis in the research literature.
2. Set up a data analysis plan within the context of behavioural and health sciences, deciding appropriately between differences and relationships, and identifying the correct statistical techniques for comparing and associating variables.
3. Interpret correctly statistical results using the software for statistical analysis used in the subject, being capable of choosing and applying the correct technique.
4. Critically assess research reports, being able to differentiate their elements, and learning where to find strengths and weaknesses.
5. Work systematically in statistical data treatment to avoid errors and reach rigorous conclusions.
6. Write technical reports based on statistical results.
7. Preparing and processing data following statistical database conventions.
Syllabus
The course begins reviewing basic concepts in descriptive statistics and moves on to concepts of statistical inference (sampling, estimation, and hypothesis testing). Then, the most general and useful statistical techniques in behavioural and health sciences are explained, structured in two broad families:
1) Comparison techniques: Z-tests, t-tests and ANOVA models
2) Techniques for associations: regression and contingency tables.
Each method is conceptually explained, emphasising the logic of the statistical technique, objectives and performance. Then, the technique is applied using statistical analysis software of widespread use in professional settings and academia (SPSS; Statistical Product and Service Solutions).
CONTENTS
Block 1. Descriptive statistics and concepts of statistical
- General concepts of Descriptive Statitics
- Correlation and regression
- Probability, random variable, statistical distribution
- Inference, estimation and statistical testing
Block 2. Analysing comparisons
- Statistical tests for comparing proportions
- Statistical tests for comparing means
- Statistical testing for more than two means: ANOVA models
Block 3: Analyzing relationships
- Regression
- Contingency tables
Teaching and learning activities
In person
In-class and outside-class time
The estimation of student work time for this course is 150 hours. Total hours per activity are:
|
Activity |
Presence |
Place |
Type |
Hours |
|
Classroom training |
In-class |
Classroom |
Individual |
38 |
|
Practices |
In-class |
Classroom |
Individual |
14 |
|
Group practice Project |
In-class |
Classroom |
Group |
8 |
|
Tutoría individual |
In person |
Seminars |
Individual |
4 |
|
Trabajo personal |
Outside-class |
-- |
Individual |
80 |
|
Evaluación |
In-class |
Classroom |
Individual |
2 |
|
|
|
|
Total in-class |
60 |
|
|
|
|
Total outside-class |
90 |
|
|
|
|
Total (6 ECTS x 25 hours) |
150 |
Methodology and Activities
Several teaching methodologies will be combined to achieve learning objectives and competencies.
1. Classroom training sessions: Full-group lectures with practice in the classroom. The teachers will explain concepts and present practical exercises on theoretical concepts. During the classroom, examples using the statistical software SPSS will be used to apply and interpret the different methods.
2. Problem-solving sessions: Full group sessions with practical exercises to be solved using jigsaw methodology. Sessions will include analyses using statistical software and result interpretations. Students will return in-class activities for constructing a learning portfolio.
4. Group mentoring: sessions upon students or teacher request. During these sessions, teachers will provide support to ordinary sessions. These mentoring activities are addressed both to students who need further training or those who desire to deepen their understanding of statistics. These meetings will be done to provide new explanations, helping with exercises, monitoring group work, practising with software or clarifying any issue concerning course contents.
Proper achievement in learning objectives will require the student’s commitment to continuing work. Continuing work is essential for adequate performance in the course.
Evaluation systems and criteria
In person
ORDINARY CALL
Final examination
Theoretical-practical final exam. The exam will consist of two parts:
a) Multiple choice questions: 30 multiple choice questions without penalty for wrong responses (40%).
b) Practical exercises (60% of the qualification): This part involves choosing, applying, and interpreting practical problems using research designs and measures like those carried out during the case methods.
The format and difficulty level of the Final Exam questions will be like those in the Questionnaires and Case Methods during the semester. The final exam will be conducted on paper in a conventional classroom at the end of the semester. Students must bring a scientific calculator to the exam.
Additional ongoing evaluation activities
None of the activities in the ongoing evaluation will be mandatory. However, they will add up to 2.5 points to the final qualification, which will be added exam qualification, provided that it is at least 5 points.
1. Individual Case Method Learning Portfolio (+2 points): a compendium of tasks in practical activities of the case method sessions. A week before the case method sessions, the case activities will be uploaded for the students to resolve:
a) Pre-session: You will upload in a Moodle task a number of individually solved exercises of your choice from the proposed exercises.
b) Post-session: As indicated in the methodology, at the end of the case method session, all students must upload the correction of all exercises. If you delivered the pre-session activity, you will clearly indicate the corrections and self-assessments made to your pre-session task, and upload this correction as the Moodle task.
The extra grade will be based on the number of exercises you delivered AND self-assessed. Thus, you try and assess 10 exercises of the pre-session activities from a total of 20 proposed exercises, 1 points will be added to qualification; if you try and correct all the 20 exercises, you will get 2 points to the exam qualification (provided that exam has a mark of at least 4 points)
2. Questions (+0.5 points): During the subject, there will be small tasks and multiple-choice questionnaires that will be answered in the classroom on the subject matter, based on previous readings on session contents. Students will respond to each assignment in the classroom.
None of the additional activities is mandatory. However, plagiarism behaviours will not be tolerated in any of the evaluation activities. In case of suspicion of these behaviours, the entire activity will be cancelled for the entire group and will not count as the evaluation.
QUALIFICATION OF THE ORDINARY CALL
The qualification of the ordinary call will be calculated in two ways:
a) Exam grade over 10 points (minimum 5 points for the pass)
b) Qualification of the exam over 8 points (minimum 3.2 for the pass) + Qualification activities 1 and 2
The final grade will be the maximum of these two qualifications. In both modalities, it will be necessary to obtain a minimum 5 in the final grade to achieve the approval of the subject.
SECOND CALL
In case of obtaining a total of under 5 points in the first call, the student must attend the final exam in the second call. The qualification of the second call will be based solely on exam qualification. Passing the course on the second call will require a score of 5 points in the final exam
Bibliography and resources
The following manuals are compulsory bibliography:
1) Pardo A, Ruiz MA y San Martín R (2015). Análisis de datos en ciencias sociales y de la salud (vol I, 2ª ed). Madrid: Síntesis.
2) Pardo A y San Martín R (2015). Análisis de datos en ciencias sociales y de la salud (vol II, 2ª ed).vMadrid: Síntesis
The course will follow these materials carefully. The students are expected to have read session materials beforehand.
Evaluation period
- E1 19/05/2022 A03 10:00h
- E2 28/06/2022 A12 08:00h