Statistics III

Code:

P406

Acronym:

ESTIII

Keywords
Classification	Keyword
OFICIAL	Scientific Research Methodology

Instance: 2019/2020 - 1S

Active?	Yes
Responsible unit:	Psychology
Course/CS Responsible:	Integrated Master Psychology

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
MIPSI	163	Official Curricular Structure	2	-	6	54	162

Teaching language

Suitable for English-speaking students

Objectives

1. GENERAL AIMS

- Elaborate knowledge acquired in the two preceding CUs (Curricular Unit), Statistics I and II

- Understand the relation between research methods and data analyses procedures

2. CONCEPTUAL ORIENTATION OF THE CU

- In each module we make explicit the extent to which the contents of Statistics I and II are articulated and necessary in this CU. However,

- In terms of conceptual orientation, in this CU we refer to the “subsidiary” character of data analysis relatively to the research methods that sustain them. We therefore stress the relation with other UCs that focus on research methodologies.

- In the setting of the “statistical reasoning” of each of the tests presented, the focus is made on the principle of contrasting systematic variance vs. error variance. This principle is evidenced in the ANOVA models or in PCA, but we demonstrate its presence in several models of increasing complexity since Statistics I (t tests, Simple and repeated measures ANOVAs, r, MR, etc.)

- Strong emphasis and valuation of specific skills of interpretation and presentation of statics results, at all times supported by data analyses adequate to the research’s goals.

- Reinforcement of the importance of data analysis skills (i.e. usage of SPSS to perform data analysis), by including such skills in the tests that compose the evaluation of the CU (see Evaluation Component ahead)

Learning outcomes and competences

- Identify the conceptual the status of the variables within a plan of data analyses: levels of measurement, relation between variables, IVs and DVs, etc. 

- Definition/identification of hypotheses, or “research questions”: taking into perspective the statistics procedures required to test them (alternative possibilities and respective limits of usage) 

- 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 23 

- Analysis, description and interpretation of results: presentation according to the APA rules 

Working method

Presencial

Pre-requirements (prior knowledge) and co-requirements (common knowledge)

Approved Status in the CUs Estatística I and Estatística II.

Program

Considering, at least, 13 classes (3½H, 1st Semester) 

Classes 1-5 – ANOVAs 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) 

Classes 6-7 – Multivariate ANOVA
- conceptual contrast with multiple ANOVAs on the DVs, model’s rationale and the variance matrices, multivariate and univariate tests

Class 8 - Intercalary Test 1

Class 9 – Analysis of reliability
- Cronbach’s alpha, KR20, Split-Half procedures 

10-12 – Principal Components Analysis
- from the principle of Common vs. Unique Variance to the interpretation of factorial structures 

Class 13 - Intercalary Test 2

Mandatory literature

Bryman Alan; Quantitative data analysis with SPSS for windows. ISBN: 0-415-14720-4
Hair Joseph F. 070; Análise multivariada de dados. ISBN: 85-363-0482-0
Howell David C.; statistical methods for psychology. ISBN: 0-534-51993-8
Reis Harry T. 340; handbook of research methods in social and personality psychology. ISBN: 0-521-55903-0
Tabachnick Barbara G.; Using multivariate statistics. ISBN: 0-321-05677-9

Complementary Bibliography

Judd Charles M.; Research methods in social relations. ISBN: 0-03031149-7
Kinnear Paul R.; SPSS for windows made simple. ISBN: 1-84169-118-6
Rosenthal, R., Rosnow, R. L., & Rubin, D. B.; Contrasts and Effect Sizes in Behavioral Research: A Correlational Approach, Cambridge University Press, 1999
Toothaker Larry E.; Multiple comparison procedures. ISBN: 0-8039-4177-3
Klockars, A. J., & Sax, G.; Multiple Comparisons: Structural Models for Qualitative Data, Sage Publications, Inc., 1986
Winer B. J.; Statistical principles in experimental design

Teaching methods and learning activities

1. STRUCTURE OF TEACHING METHODS

- Each class has a theory component relative to the statistics rationale of a test or group of tests. When considered fitting, we go back to the acquisition made on previous UCs, making it explicit how their increasingly complex learning is based on common “statistical concepts”.

- Modules’ contents are organized in such that every class has a practical component of data analysis with the SPSS. More than the “mechanics” of the process of executing an analysis, we stress the importance of knowing the statistical rationale that allows the necessary statistical decision making. Emphasis is made on the training of specific skills of analysis, description and results interpretation.

- Each module has support materials, namely one data file for the purpose of exercise. Students take part in an on-line study. Its objective is to be negotiated in the first class, and will aloe them to analyze their own data during the course.

2. CONCEPTUAL ORIENTATION OF TEACHING METHODS

- The organization of module’s contents in such a way that every class has a theory component and a practical one aims to serve a double objective: On the one hand, reinforcing the “applied” character of statistics learning with the immediate demonstration of “what does it serve for” the acquired theoretical knowledge; on the other, potentiate students orientation towards data analysis as a process that implies decision making supported by basic statistics and methodology knowledge.

- The statistical rationale of each statistic procedure is discussed with students and “broken down” in its more simple parts that go back, in most cases, to the basic principles acquired since Statistics I. Otherwise, complex analyses such as a PCA, or the test of an hypothesis that requires decomposing a 2nd order interaction, can become vacant exercises for 3rd semester students.

- Programmed contents are defined in such a manner that, when justified from the pedagogical point of view, parts of it can be excluded or some additional contents can be included. 

- All analyses performed in the class are conducted with data from psychological research either ongoing or already completed. By doing so, the use of any statistics test will always have the purpose of answering a question or testing a “real” hypothesis.
From the pedagogical standpoint, the use of data files from actual research is based on the premise of training data analysis skills with a structure provided by the professor.

- In different moments of each module students will have to use the data file from the study conducted in the UC. In these “free analysis” moments the only rule external to the class dynamics is the obligation of applying, at least once, the ongoing module’s contents. Students will be encouraged to perform analyses that make use of previous modules contents, as well as those from Statistics I and II.

keywords

Social sciences > Educational sciences > Research methodology
Physical sciences > Mathematics > Statistics

Evaluation Type

Distributed evaluation without final exam

Assessment Components

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

Amount of time allocated to each course unit

designation	Time (hours)
Estudo autónomo	108,00
Frequência das aulas	54,00
Total:	162,00

Eligibility for exams

Attendance in 3/4 of the classes is mandatory.

Calculation formula of final grade

The final grade is calculated on the basis of the student's results on 2 mandatory intercalary tests with the following weight:

(1) Test 1 - 40%
(2) Test 2 - 60%

To get approval status, the student must obtain a minimal score of 10 (out of 20) in both tests. In case this not succeed, the student has access to an exam in the evaluation period defined for these situações (Special Period).

1. INTERCALARY TESTS
1.1. TESTS PERFORMED AT THE COMPUTER ROOM

The tests consist on the actual execution of data analyses. The student will be provided with a data file that he or she must know how to handle in order to execute the requested analyses. The student also has to report the results and conclusion that are demanded.

1.2. EVALUATION CRITERIA
- Knowledge of statistical procedures adequate to different research settings;
- Ability to use SPSS software to perform data analyses, including the need to manipulate files and data (for example, coding and defining variables, entering data, recoding variables, creating composite variables, file manipulation, etc.)
- Ability to describe, analyze and interpret results provide by SPSS outputs, relative to statistical procedures presented in the course.

1.3. FORMAT AND DURATION
Students have access to an individual test generated randomly. The test has the maximum duration of 90 minutes.

2. SPECIAL PEDIOD EXAM
The exam in the Special Period of evaluation has the same characteristics of the Intercalary Tests.

Classification improvement

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