Advanced Methods of Data Analysis

Code:

M102

Acronym:

MAAD

Keywords
Classification	Keyword
OFICIAL	Methods

Instance: 2022/2023 - 2S

Active?	Yes
Responsible unit:	Psychology
Course/CS Responsible:	Master Degree in Psychology

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
MPSIC	137	Plano de Estudos 2021	1	-	3	27	81

Teaching language

Portuguese

Objectives

1. General Goals

- Elaborate on knowledge acquired in the preceding CUs of Statistics Applied to Psychology I and II

- Understanding of the relation between research methods and statistics procedures, based on the practice of real situations of data analysis

2. CONCEPTUAL ORIENTATION OF THE CU

- In each module we make it explicit the extent to which the contents of Statistics Applied to Psychology I and II are necessary for the acquisitions of this UC.

- In terms of conceptual orientation, we stress the “subsidiary” character of data analysis relatively to the research methods that sustain them. Therefore, we emphases the relation with other UCs that focus on research methodologies. 

- In the setting of the “statistical reasoning” of each of the procedures, emphases is made on the principle of contrasting systematic variance vs. error variance. This principle is illustrated, both theoretically as well as by the practice with real situations of data analysis, with the various models (ANOVAs, PCA).

Learning outcomes and competences

- Identify the conceptual the status of variables within a plan of data analysis: levels of measurement, relation between variables, Independent and Dependent Variables, etc. 
- Definition/identification of hypotheses, or “research questions”, deciding which statistics procedures are required to test them, as well as the alternative possibilities and the limitations within a specific scenario of data analysis 
- Acquisition of basic knowledge regarding the conceptual statistical framework of every statistics test presented 
- Training in the “practice” of data analysis with the software IBM SPSS Statistics
- Analysis, description and interpretation of results according to the APA rules

Working method

Presencial

Program

1. Datascreening to prepare and manipulate data files:

         - Detection data entry errors, treatment of missing data, detection routines and                   dealing with outliers 

1. Analysis of Variance with more than 1 factor

         - Factorial and mixed-design models (from the statistics rationale of each model to               the analysis of main effects and the decomposition of up to 2nd order interactions) 

1. Multivariate ANOVA

          - Conceptual contrast with multiple ANOVAs on the DVs, model’s rationale and the                variance matrices, multivariate and univariate tests 

1. Cluster analysis with numeric variables 

          - The K-Means method 

1. Principal Components Analysis

          - From the principle of Common vs. Unique Variance to the interpretation of factorial structures 

NOTE: In the academic years in which takes place the "transition" between the MIPSI Study Plan and those of the current Psychology Undergraduate Degree (1st Cycle) and Master's Degree in Psychology (2nd Cycle), the Program of the CU must be adjusted to the previous academic trajectory of the students (namely, due to the CU of Statistics III enrolled in the 2nd Year of the MIPSI)

Mandatory literature

Andy Field; Discovering statistics using IBM SPSS, SAGE Publications Ltd, 2017. ISBN: 9781526419521
Howell, D.; Fundamental Statistics for the Behavioral Sciences, Wadsworth Publishing, 2016. ISBN: 978-1305652972
David C. Howell; Statistical methods for psychology. ISBN: 0-534-51993-8
Julie Pallant; SPSS survival manual. ISBN: 978-0-33-524239-9
Julie Pallant; SPSS Survival Manual: A Step by Step Guide to Data Analysis Using IBM SPSS, Open University Press, 2020. ISBN: 978-0335249497

Complementary Bibliography

Grimm, L. G., & Yarnold, P. R.; Reading and understanding MORE multivariate statistics, American Psychological Association, 2000. ISBN: 1-557798-698-3
Hatcher, L.; Advanced statistics in research: reading, understanding, and writing up data analysis results, Shadow Fich Media, 2013. ISBN: 978-0985867003
Reis, H. T., & Judd, C. M.; Handbook of research methods in social and personality psychology, Cambridge University Press, 2014. ISBN: 978-1107600751
Harry T. Reis; Handbook of research methods in social and personality psychology. ISBN: 0-521-55903-0
Barbara G. Tabachnick; Using multivariate statistics. ISBN: 0-321-05677-9

Teaching methods and learning activities

- Each class has a theory component focused on the statistics rationale of the procedure presented. When considered relevant, we resume acquisition made on previous CUs, making it explicit how increasingly complex learning is based on common “statistical concepts”. 

- Module are organized in such a way that every class has a practical component of data analysis with the SPSS. More than the “mechanics” of executing an analysis, we stress the importance of knowing the statistical rationale that allows the necessary statistical decision making.

- All modules have a strong component dedicated to the “routine” of data analysis, from the sequence of decisions in the software of data analysis, through the specific skills of description and interpretation of results. 

- For each module students are given a series of support materials, namely one data file of an actual study previously conducted, or ongoing, for the purpose of training all the statistical procedures.

Software

IBM SPSS Statistics 27

Evaluation Type

Evaluation with final exam

Assessment Components

designation	Weight (%)
Exame	100,00
Total:	100,00

Amount of time allocated to each course unit

designation	Time (hours)
Estudo autónomo	54,00
Frequência das aulas	27,00
Total:	81,00

Eligibility for exams

Attendance in 3/4 of the classes is mandatory.

Calculation formula of final grade

The student's final grade is determined by his/her result in the Final Exam.

Classification improvement

Students are eligible for grade improvement in the evaluation period defined for this purpose