SOCI45015: Analysing Longitudinal Data

• Any introductory statistics training.

• None.

• None.

• To help students understand the meaning of statistical concepts and methods that are widely used for analysing longitudinal data.
• To train students in learning to use the computing software Stata in order to conduct statistical analyses of longitudinal data.
• To enable students to answer substantively important research questions relevant to their respective academic discipline.

• Lecture topics:
• 1. Introduction: longitudinal study designs, where to find the data, and why it is important to analyse them.
• 2. Importing and preparing longitudinal data
• 3. Exploring and describing repeated measures over time
• 4. Variance-component models
• 5. Random-intercept models
• 6. Random-coefficient models
• 7. Cross-lagged models
• 8. Growth curve models
• 9. Attrition and missing values
• 10. Review and brief introduction to other longitudinal studies and methods.
• Topics for computer sessions:
• 1. Introduction to Stata
• 2. Importing and preparing longitudinal data
• 3. Exploring longitudinal data with simple statistics and graphs
• 4. Variance-component models
• 5. Random-intercept models
• 6. Random-coefficient models
• 7. Cross-lagged models
• 8. Growth curve models
• 9. Attrition and missing values
• 10. Review and help with summative work.

Subject-specific Knowledge:

• Upon successful completion of this module, students will have demonstrated:
• Understanding the logic and the specific principles of a range of statistical methods and tools widely used in longitudinal data analysis;
• Detailed knowledge and critical understanding of basic but important principles for using statistics in a variety of contexts related to longitudinal data;
• Understanding the nature and production of different types of longitudinal data and how it affects the subsequent analyses of the data;
• Knowledge of different methods and the conditions for their appropriate use;
• Understanding the meaning of descriptive vs. inferential statistics;
• Understanding the meaning of variance matrix, random effects vs. fixed effects, measurement errors, clustering effects, etc.

Subject-specific Skills:

• Upon successful completion of this module, students will have demonstrated:
• Capabilities for managing research, including collecting and analysing data, conducting and disseminating research in such a way that is consistent with both professional practice and principles of research ethics and risk assessment;
• Interpretation of statistics derived from a particular method;
• Ability to prepare large scale data for missing values;
• Ability to evaluate the relative pros and cons of each specific concept, measure or method in the context of longitudinal data analysis;
• Ability to produce simple statistics and interpret their meaning in relation to the substantive meaning and context of longitudinal data analysis;
• Ability to critically review other researchers work in light of statistical reasoning in longitudinal data analysis;
• Ability to produce appropriate statistical models and interpret their results correctly;
• Ability to use Stata or an equivalent software for producing desired statistics;
• Ability to conduct replicable research.

Key Skills:

• Upon successful completion of this module, students will have demonstrated:
• KS1 - The ability to evaluate and synthesize information obtained from a variety of sources (written, electronic, oral, visual); to communicate relevant information in a variety of ways and to select the most appropriate means of communication relative to the specific task. Students will also be able to communicate their own formulations in a clear and accessible way; they will be able to respond effectively to others and to reflect on and monitor the use of their communication skills;
• KS2 - The ability to read and interpret complex tables, graphs, and diagrams; to organize, classify and interpret numerical and logical data; to make inferences from sets of data; to use advanced techniques of data analysis; and to appreciate the scope and applicability of numerical and logical data;
• KS3 - Competence in using information technology to use a computer software package effectively; to use effective information storage and retrieval; and to use web-based resources;
• KS5 - Effective time-management, working to prescribed deadlines;
• KS6 - The ability to engage in different forms of learning, to seek and to use feedback from both peers and academic staff, and to monitor and critically reflect on the learning process.

• Lectures: introduce and explain the meaning of key concepts and the logic of each method, illustrating with examples
• Computer practical sessions: introduce the widely used and powerful statistical programme Stata; demonstrate how each method covered in the lectures is applied with Stata or an equivalent software; explain the meaning of the outputs; enable students to use the methods and computing programmes to conduct their own analyses.
• Summative assessment: with a 3000-word essay, students are provided with the opportunity to apply the methods learnt for analysing real-world longitudinal data; conduct independent research project on an important issue of their interest. The essay is expected to follow the format of a quantitative research paper adopted by academic journals, including an introduction, a brief literature review, data and methods, findings, discussions, and conclusions. Essays with convincing explanations for why certain methods are used while others are not will be rewarded with a higher mark. More details will be provided in a separate document.
• Formative Assessment: with a 500-word outline, students have the option to produce a plan for their summative work, including their research focus, the data to be analysed, the methods to be used, and the expected results; they will receive feedbacks from the module convenor which will help keep them on track and enhance the quality of their summative work.

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