Content Programme

Material for Autonomous work

Week 1: 5^th February

CP1

Research process in HR

Main steps in a research process. Target population and sample. Precision of estimates and level of confidence in results.

Robson & McCartan (2016): Ch. 11.

Newbold et al. (2023): Ch. 1

CP3

Simple linear regression model

Analysis of correlations and causality

in HR. Model estimation.

Field (2024): Ch. 8 & 9.

Week 2: 12th February

CP3

Simple linear regression model

Applications in SPSS (‘DB_stressor_performance.sav’).

Field (2024): Ch. 8 & 9.

Sheet 1 – Exercise 1

(‘DB_Productivity.sav’)

CP3

Simple linear regression model

Evaluation of the validity and usefulness of the model.

Applications in SPSS (‘DB_stressor_performance.sav’).

Field (2024): Ch. 9.

Week 3: 26^th February

CP3

Multiple linear regression model

Model estimation.

Construction of dummy variables.

Field (2024): Ch. 8 & 9.

Sheet 1 – Exercises 2 & 3

(‘DB_Happiness.sav’,

‘DB_Productivity.sav’)

Sheet 2 – Exercises 1 & 2

CP3

Multiple linear regression model

Resolution of exercises in small groups in the classroom. Sheet 2 – Exercise 3.

Field (2024): Ch. 8 & 9.

Sheet 2 – Exercises 4 & 5

Week 4: 5^th March

CP4

Linear regression model with moderation Moderation in the linear model.

Applications in SPSS (‘DB_stressor_performance.sav’).

Field (2024): Ch. 11.

Sheet 1 – Exercise 4

(‘DB_ Commitment.sav’)

CP4

Linear regression model with moderation

Resolution of exercises in small groups in the classroom. Sheet 2 – Exercise 6.

Field (2024): Ch. 11.

Sheet 2 – Exercises 6

Week 5: – 12^th March

CP4

Linear regression model with mediation

Mediation in the linear model.

Applications in SPSS (‘DB_stressor_performance.sav’).

Field (2024): Ch. 11.

Ficha 1 – Exercício 5

(‘BD_ Happiness.sav’)

CP4

Linear regression model with mediation

Resolution of exercises in small groups in the classroom. Sheet 2 – Exercise 7.

Field (2024): Ch. 11.

Sheet 2 – Exercises 7

Week 6 : 16th April

CP2

Principal component analysis

Introduction to principal component analysis.

Adequacy of the technique for reductions of observed variables. Extraction of the principal components. Rotation methods.

Field (2024): Ch. 18.

CP2

Principal component analysis

Obtaining the principal components in SPSS. Interpretation of the obtained solution. Reliability analysis.

Applications in SPSS ('DB_Satisfaction.sav').

Field (2024): Ch. 18.

Sheet 3 – Exercise 1 (‘DB_HRPractices.sav’)

Sheet 4 – Exercise 1

Week 7: 23^th April

CP2

Principal component analysis

Applications in SPSS ('DB_Satisfaction.sav').

Field (2024): Ch. 18.

Sheet 3 – Exercises 2 & 3 (‘DB_Productivity.sav’, ‘DB_Commitment.sav’)

Sheet 4 – Exercise 3 & 4

CP2

Principal component analysis

Resolution of exercises in small groups in the classroom. Sheet 4 – Exercises: 2.             

Field (2024): Ch. 18.

Sheet 4 – Exercises 5-9

Week 8 : 23th April

Revisions for the test: resolution of the previous year’s test

Resolution of tests/exams from previous years.

Revisions for the test: resolution of the previous year’s test

Resolution of tests/exams from previous years.