Module title: Principles of Data Management and Analysis

SCQF level: 11:
SCQF credit value: 40.00
ECTS credit value: 20

Module code: NMS11184
Module leader: Iain Atherton
School School of Health & Social Care
Subject area group: Mental Health
Prerequisites

n/a

2019/0, Trimester 2, Online,
Occurrence: 001
Primary mode of delivery: Online
Location of delivery: SIGHTHILL
Partner:
Member of staff responsible for delivering module: Iain Atherton
Module Organiser:


Learning, Teaching and Assessment (LTA) Approach:
The theory for this module will be delivered through asynchronous activities administered through the virtual learning environment (MOODLE), web-ex teaching/seminar sessions in real time and/or recorded for rescheduled viewing, podcasts, discussion boards, set task, group work, weekly seminars, agreed exercises and tutorial sessions using web-ex and other web-technology such as SKYPE. Students will require access to software such as SPSS and NVIVO.

There will be a total of 30 hours of formal web-based contact and students will be encouraged to utilise web-ex session’s outwith these formal sessions. Weekly on-line teaching sessions/seminars will make up the formal 23 hours’ contact. These teaching sessions/seminars will be used to promote contact between students within the group. Academic tasks and activities will be designed to support the development of academic skills to enable the critical appraisal and understanding of the conventions, underlying principles and philosophies that underpin data analysis as well as to support the student group as a community of practice through group management, group facilitation strategies to encourage the promotion of contact between students and the module team. Within these sessions students will be coached in the use of data analysis software. Development of a community of practice will help to provide a series of constructive alliances, relationships and co-working between students. In this way the community of practice will reproduce the benefits of face-to-face contact and discussion. The face-to-face seminars will take place within the first 15 weeks of the module.

All students will be offered further tutorial/academic support sessions/supervision as they require. Students will be offered further one-hour sessions every two weeks (7 hours) during the full duration of the module (two trimesters). These web-based group sessions will focus on academic support in the form of tutorials and academic supervision.


Formative Assessment:
Formative assessment
The assessment process for the module will enable knowledge, academic skill development and confidence through formative assessment exercises in preparation for the summative assessment. The students will complete three exercises (one for each of the three parts of the module) to analyse example data and then complete three short 500 word written reports (week 4, week 7 and week 10) of their interpretation of the results. Feedback will be provided for each of the three written formative reports prior to submission of the summative assessment. The student experience and skill development accruing from the three exercises will be the basis of reflective discussion at the weekly seminars. In order to complete these formative exercises students will have to develop an understanding of the conventions and underlying principles of quantitative, qualitative and synthesised data analysis and study synthesis (LO1) and to demonstrate the skills required for data analysis (LO3), discussion and reflection will include critical review and discussion of the skills used for the analysis of data (LO4; LO5).


Summative Assessment:
The summative assessment is in two parts:
7000-word written assignment; students will be required to descriptively and inferentially analyse a ‘publicly available dataset’ made up of qualitative and quantitative data parts (LO1; LO3; LO4). They will present the findings of the analysis, critically review the strengths and limitations of the data analysis strategies (LO2; LO3; LO5) used and discuss the interpretation (LO2; LO3) and implications of the results that arise from the data analysis (L03). The assignment will be written in the format of a peer reviewed scientific journal. Feedback regarding the consistency with peer reviewed format will be provided on the formative assessment.


Student Activity (Notional Equivalent Study Hours (NESH))
Mode of activityLearning & Teaching ActivityNESH (Study Hours)
Online Seminar 30
Independent Learning Guided independent study 370
Total Study Hours400
Expected Total Study Hours for Module400


Assessment
Type of Assessment Weighting % LOs covered Week due Length in Hours/Words
Report 100 1,2,3,4 30 HOURS= 0, WORDS= 7000
Component 1 subtotal: 100
Component 2 subtotal: 0
Module subtotal: 100
2019/0, Trimester 2, Online,
Occurrence: 002
Primary mode of delivery: Online
Location of delivery: SIGHTHILL
Partner:
Member of staff responsible for delivering module: Iain Atherton
Module Organiser:


Learning, Teaching and Assessment (LTA) Approach:
The theory for this module will be delivered through asynchronous activities administered through the virtual learning environment (MOODLE), web-ex teaching/seminar sessions in real time and/or recorded for rescheduled viewing, podcasts, discussion boards, set task, group work, weekly seminars, agreed exercises and tutorial sessions using web-ex and other web-technology such as SKYPE. Students will require access to software such as SPSS and NVIVO.

There will be a total of 30 hours of formal web-based contact and students will be encouraged to utilise web-ex session’s outwith these formal sessions. Weekly on-line teaching sessions/seminars will make up the formal 23 hours’ contact. These teaching sessions/seminars will be used to promote contact between students within the group. Academic tasks and activities will be designed to support the development of academic skills to enable the critical appraisal and understanding of the conventions, underlying principles and philosophies that underpin data analysis as well as to support the student group as a community of practice through group management, group facilitation strategies to encourage the promotion of contact between students and the module team. Within these sessions students will be coached in the use of data analysis software. Development of a community of practice will help to provide a series of constructive alliances, relationships and co-working between students. In this way the community of practice will reproduce the benefits of face-to-face contact and discussion. The face-to-face seminars will take place within the first 15 weeks of the module.

All students will be offered further tutorial/academic support sessions/supervision as they require. Students will be offered further one-hour sessions every two weeks (7 hours) during the full duration of the module (two trimesters). These web-based group sessions will focus on academic support in the form of tutorials and academic supervision.

Embedding of employability/PDP/scholarship skills
The module will provide students with the knowledge and skills to critically review a wide range of data analysis strategies used within the literature relating to health and social care. The knowledge and skills developed on the module will be applied to example datasets and so will allow students to practice and demonstrate their skills and knowledge preparing the students to undertake data analysis for their own empirical studies. Generic cognitive skills such as linking data analysis to study objectives, study design and interpretation to the evidence base and problem solving will be promoted in the module. Understanding how to use specialised data analysis software and will help the students to develop skills applicable to future empirical studies within their organisation. Knowledge and skills of data analysis and interpretation will allow the student to undertake a key role in terms of evaluation, reviewing of interventions within their organisation.

Assessment (formative and summative)
Formative assessment
The assessment process for the module will enable knowledge, academic skill development and confidence through formative assessment exercises in preparation for the summative assessment. The students will complete three exercises (one for each of the three parts of the module) to analyse example data and then complete three short 500 word written reports (week 4, week 7 and week 10) of their interpretation of the results. Feedback will be provided for each of the three written formative reports prior to submission of the summative assessment. The student experience and skill development accruing from the three exercises will be the basis of reflective discussion at the weekly seminars. In order to complete these formative exercises students will have to develop an understanding of the conventions and underlying principles of quantitative, qualitative and synthesised data analysis and study synthesis (LO1) and to demonstrate the skills required for data analysis (LO3), discussion and reflection will include critical review and discussion of the skills used for the analysis of data (LO4; LO5).

Summative assessment
The summative assessment is in two parts:
7000-word written assignment; students will be required to descriptively and inferentially analyse a ‘publicly available dataset’ made up of qualitative and quantitative data parts (LO1; LO3; LO4). They will present the findings of the analysis, critically review the strengths and limitations of the data analysis strategies (LO2; LO3; LO5) used and discuss the interpretation (LO2; LO3) and implications of the results that arise from the data analysis (L03). The assignment will be written in the format of a peer reviewed scientific journal. Feedback regarding the consistency with peer reviewed format will be provided on the formative assessment.

Internationalisation
The fundamental role of trustworthy data analysis and interpretation is universal in supporting evidence-based practice within health and social care. The main strategies and conventions relating to quantitative, qualitative and aggregated data analysis are applicable internationally. The module materials will reflect an international perspective and examples of data sets and the application of data analysis knowledge and skills will be discussed within the context of different international health and social care systems.


Formative Assessment:
The University is currently undertaking work to improve the quality of information provided on methods of assessment and feedback. Please refer to the section on Learning and Teaching Approaches above for further information about this module’s learning, teaching and assessment practices, including formative and summative approaches.

Summative Assessment:
The University is currently undertaking work to improve the quality of information provided on methods of assessment and feedback. Please refer to the section on Learning and Teaching Approaches above for further information about this module’s learning, teaching and assessment practices, including formative and summative approaches.

Student Activity (Notional Equivalent Study Hours (NESH))
Mode of activityLearning & Teaching ActivityNESH (Study Hours)
Online Seminar 30
Independent Learning Guided independent study 370
Total Study Hours400
Expected Total Study Hours for Module400


Assessment
Type of Assessment Weighting % LOs covered Week due Length in Hours/Words
Report 100 1,2,3,4 15 HOURS= 0, WORDS= 7000
Component 1 subtotal: 100
Component 2 subtotal: 0
Module subtotal: 100

Description of module content:

The module will provide the following:
Critical discussion of the underlying principles of quantitative, qualitative and synthesising data analysis and study synthesising strategies. Students will be provided with the opportunity to apply the knowledge and skills of quantitative, qualitative and synthesising data analysis and study synthesis to publicly available data sets and to discuss the interpretation of their findings arising from the application of data analysis strategies. Students will be introduced to quantitative data analysis issues such as levels of measurement, rationale for selection of data analysis strategies, relationship between variables, descriptions of samples/populations, drawing inferences from the data, summary data linked to specific methodologies and study designs and issues relating to the reporting and presentation of findings.
Students will also develop a critical understanding of the nature, methodology, underpinning philosophy and epistemological models of qualitative inquiry such as grounded theory, phenomenology, interpretative, ethnography, data preparation, summary of findings, interpretation of codes and findings, and theory development.
Whilst the first two parts of the module will cover quantitative and then qualitative inquiry and data, the third part of the module will cover strategies for synthesising data from different studies; issues of bias, heterogeneity/homogeneity, relationship between variables, and combining data sets and the synthesis and integration of qualitative studies; selection of studies, listing key concepts, translating the studies, synthesising the translations and identification of key concepts.

Learning Outcomes for module:

LO1: Critically understand the conventions, underlying principles and philosophies that underpin quantitative, qualitative and synthesized data analysis and study synthesis.
LO2: Enhance critical understanding of the issues relevant to health and social care data analysis/management
LO3: Critically appraise the credibility and trustworthiness of interpretations of data made by authors of health and social care peer reviewed study reports.
LO4: Demonstrate and effectively apply the use of enhanced skills for quantitative, qualitative, synthesized data and study synthesis management and analysis to professionally relevant methods of enquiry.
LO5: Critically evaluate the application of a range of quantitative, qualitative, synthesized data analysis and study synthesis strategies to datasets.

Indicative References and Reading List - URL:

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