Subject

Methodology Applied to Psychology

  • code 12753
  • course 2
  • term Semester 2
  • type OB
  • credits 6

Main language of instruction: Spanish

Other languages of instruction: Catalan, English

Teaching staff

Head instructor

Dr. Carlos GARCÍA - cgarciaf@uic.es

Office hours

Course Coordinator: Dr Carlos Gª Forero (cgarciaf@ui.es)

 

Introduction

In the event that the health authorities announce a new period of confinement due to the evolution of the health crisis caused by COVID-19, the teaching staff will promptly communicate how this may effect the teaching methodologies and activities as well as the assessment.

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

Competencies

  • 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

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.

3. Group Project: A learning project in small (4-5 persons) groups, randomly selected. During the project, students will develop a statistical analysis project to return a research report based on a real dataset. These results will be delivered as group tasks in Moodle. Each group will solve and discuss several questions in the classroom. The project requires selecting, applying and interpreting most of the statistical techniques explained in-classroom training and problem-solving sessions. 

4. Individual mentoring: individual sessions upon student or teacher request. During these sessions, teachers will provide individualised 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

First  Call

Compulsory activities

We will use an ongoing sumative assessment. Students will have to participate (and pass with at least 50% qualifications in each activity) in three different activities: learning portfolio, group project and final evaluation.

1. The learning portfolio: A compendium of deliverables from problem-solving sessions. Students will have to deliver practical exercises and self-assessments from after practical sessions. Each activity will be given to the teacher after the session, for a total of 14 in 7 practical sessions. A minimum of 10 exercises must be delivered to obtain a passing grade in the course (30% final qualification)

2. Practical sessions: Students who participate in at least two exercise corrections during practical sessions will be assessed.  (15% of the final qualification).

3. Group project and final project assessment: Assessment of the group project. This assessment involves delivering the final project and a control session. Given that individual participation can be unbalanced, one of the group members, (randomly selected) will present a group project in a final 15-minute discussion. Only those students who deliver the project will be assessed. (30% of the final qualification)

4. Final exam: with two parts: theoretical and practice. Theory section emphasises concept acquisition and will be assessed in a 30 item multiple-choice  (4 response options with correction-for-guessing) that student will answer without materials. The practical session will assess competencies on deciding, applying and interpreting the statistical techniques explained during the course presenting two paper-and-pencil problems. Students will be able to use any written material, such as books and classroom notes for this second section. No digital instrumentation will be allowed (mobile phones, computer, or tablet devices) except for a non-programmable scientific calculator. The final exam will take place in an ordinary classroom at the end of the semester (25% of the final qualification)

Additional activities

Other than the previous activities, students can voluntarily carry out any of these activities for additional qualification: 

a) Deepening in a course topic to hone competencies (retrieving information, internet or database paper searches, Reading research reports, using other statistical techniques). (Up to a 5% extra qualification)

b) Active participation in classroom activities or during practical exercises for discussion as part of course activities. (Up to 15% additional qualification)

SECOND CALL

If a student result does not pass 5 points after summing the qualification of the different activities, the student can attend the second call final exam. Qualification of continuous assessment activities is kept, and the final exam qualification averaged with final exam qualification with 50% weight.

The Non-assessed qualification is possible when a student does not attend the final exam on the first call. If the student has a non-assessed qualification and did not yield the compulsory activities, the final exam will imply 100% of the final qualification.

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.

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