Course guide of Statistical Methods for Human Resource Management (25911B1)

It is recommended to have completed at least one basic module in Statistics.

• Statistical Software for Labor and Human Resources Data Analysis.
• Statistical Regression Methods.
• Statistical Classification Methods.
• Survival Analysis. Longitudinal Analysis.

Cognitive:

• General Objective: To possess interdisciplinary knowledge that allows for the understanding of statistical methods and techniques within the context of Labor Relations and Human Resources.
• Specific Objectives:
  • Understand the importance of Statistics in analyzing data related to Labor Relations and Human Resources issues.
  • Know the strategies specific to Statistical Methods for synthesizing information.
  • Recognize the need for multivariate data analysis in practical situations.
  • Design appropriate strategies for data collection to ensure suitability for subsequent analysis.

Procedural:

• General Objectives:
  • Solve fundamental problems using techniques specific to Applied Statistics.
  • Efficiently manage computer resources aimed at the statistical treatment of multivariate data.
• Specific Objectives:
  • Develop appropriate databases for statistical treatment.
  • Correctly synthesize the observed information.
  • Solve problems related to the analysis of large volumes of data.
  • Address issues of group comparison and variable association.
  • Interpret the coefficients associated with the analyses produced.

Attitudinal:

• View statistical methodology as a fundamental tool in empirical research.
• Use critical thinking in evaluating the outcomes of research.
• Positively assess the use of information technologies and bibliographic and documentary resources.

TOPIC 1. DESCRIPTION OF STATISTICAL SOFTWARE MODULES. Creation of a database. Types of variables. Segmentation. Filtering. Data import. Basic graphs. Statistical summaries. Two-way tables. Multiple tables.

TOPIC 2. STATISTICAL REGRESSION METHODS Univariate simple linear regression model. Multiple linear regression model. Non-linear regression models.

TOPIC 3. STATISTICAL CLASSIFICATION METHODS Principal Component Analysis. Multidimensional Scaling. Simple Correspondence Analysis. Linear Discriminant Analysis. Cluster Analysis.

TOPIC 4. SURVIVAL ANALYSIS. Distribution of the waiting time variable. Estimation of the survival function (Kaplan-Meier method). Actuarial method. Parametric alternative. Proportional hazards model (Cox).

All topics will be developed using a STATISTICAL SOFTWARE, which in the current academic year will be R (and particulary the R-Commander library). In each session, the theoretical foundation of the technique will be briefly introduced, followed by a comprehensive study through exercises applied to Human Resource Management utilizing the software. As a complement, AI tools (ChatGPT, Gemini, Copilot, etc.) will be used for code generation in R and interpretation of results.

• Abad, F.; Huete. M.D. y Vargas, M. (2016). Estadística, Probabilidad en Introducción al Análisis Demográfico. Ed. Avicam. Granada.
• Abad, F.; Vargas, M. (2002). Análisis de datos para las Ciencias Sociales con SPPS. Ed. Urbano. Granada.
• Aldas Manzano, J.; Uriel Jiménez, E. (2017). Análisis multivariante aplicado con R. 2ª ed. Ed. Paraninfo
• Lara Porras, Ana. Guía interactiva de Autoaprendizaje de SPSS en https://www.ugr.es/~bioestad/guiaspss/
• Martín Martín, Q. (2007). Tratamiento estadístico de datos con SPSS. Prácticas resueltas y comentadas. Ediciones Paraninfo.
• Paladino, M. (2018). "R para Ciencias Sociales" en https://martinpaladino.github.io/rsociales/
• Pérez López, C. (2024). “Análisis Multivariante de Datos. Aplicaciones con R”. Garceta, Grupo Editorial.
• Manuales online del software utilizado (SPSS, JASP, R, etc.)
• Visauta, B. (2003). Análisis estadístico con SPSS para Windows, vol. II (Ebook) Ed. Mc Graw Hill Interamericana S.L.

Software manuals

• https://www.ugr.es/~bioestad/guiaspss/ (Lara Porras, Ana. Guía interactiva de Autoaprendizaje de SPSS)
• http://static.jasp-stats.org/JASPGuideEspanol.pdf (Análisis estadístico con JASP: una guía para estudiantes)
• https://knuth.uca.es/moodle/course/view.php?id=37 (Estadística Básica con R y R-Commander)

• http://www.ine.es/
• http://www.juntadeandalucia.es/institutodeestadisticaycartografia/
• http://www.math.uah.edu/stat/
• https://onlinestatbook.com/
• https://davidmlane.com/hyperstat/
• http://www.estadisticaparatodos.es/software/descartes
• html http://wpd.ugr.es/~bioestad/guia-spss/practica-1/
• https://www.bmj.com/about-bmj/resources-readers/publications/statistics-square-one

• MD01. Clases magistrales con soporte de las TIC, si es necesario, y debate en gran grupo. Planteamiento y resolución de ejercicios o supuestos prácticos. Exposición y discusión oral en clase. 
• MD02. Tutorías colectivas e individuales (presenciales o virtuales). 

Continuous Assessment

• Written tests (periodic essay exams, problem-solving, short-answer tests, reports, and class journals) and oral tests (presentations of oral work in class, either individual or in groups). Weighted for the final grade at 70%.
• Techniques based on attendance and active participation of the student in class, seminars, and tutorials: group work on proposed practical cases (weighted at 30%).

In the extraordinary assessment, a final exam covering all the material taught in the module will be conducted, which will account for 100% of the grade due to the extraordinary nature of the evaluation.

A final unique assessment is available for students who are unable to participate in the continuous assessment method due to work commitments, health issues, disabilities, or any other valid reasons that prevent them from adhering to the continuous assessment process.

Information of interest for students with disabilities and/or Specific Educational Support Needs (NEAE): Management of services and support (https://ve.ugr.es/servicios/atencionsocial/estudiantes-con-discapacidad)

The R program (and particularly the R-Commander library) will be used, as the open-source software of reference for data analysis in the academic field.