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Leveraging Data to Make Better Decisions - An Overview of Databases Webinar Series . Webinar 6: Development of a Longitudinal Qualitative Database. Mary Crea-Arsenio MSc. Andrea Baumann RN, PhD Mabel Hunsberger RN, PhD Nursing Health Services Research Unit (NHSRU) McMaster University .
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Leveraging Data to Make Better Decisions - An Overview of Databases • Webinar Series Webinar 6:Development of a Longitudinal Qualitative Database Mary Crea-Arsenio MSc. Andrea Baumann RN, PhD Mabel Hunsberger RN, PhD Nursing Health Services Research Unit (NHSRU) McMaster University Date: Monday March 18, 2013 Time: Noon- 1:00PM (EST)
Leveraging Data to Make Better Decisions - An Overview of Databases • Webinar Series Outline • Types of Research • Types of Data • Qualitative Longitudinal Research (QLR) • Use of QLR in Policy Research • Development of a Qualitative Longitudinal Database
Leveraging Data to Make Better Decisions - An Overview of Databases • Webinar Series Types of Data Primary data • collected to answer a specific research question. Secondary data • collected by some other user. Source: Wunsch, Harrison, & Rowan (2005).
What is Qualitative Research? “Qualitative research seeks to identify, map and explore the multiple perspectives held by individuals and groups within their social setting.” Source: Molloy, Woodfield & Bacon, 2007
What is Quantitative Research? “Quantitative research is a formal, objective, systematic process in which numerical data are used to obtain information about the world.” Source: Burns & Grove, 2005.
Cross-Sectional • Data collected on a sample of the population at a single point in time • Used to examine relationships between variables of interest Source: Barratt & Kirwan, 2009
What is Longitudinal Research? • Data are collected over time • Participants are the same or are comparable • Analysis includes comparison of data between or within points in time • Quantitative or qualitative Source: Ruspini, 1999
Qualitative Longitudinal Provides an in-depth understanding of how and why change occurs. Quantitative longitudinal Measures the extent of change and identifies prevalence of factors that affect change. Source: Molloy, Woodfield & Bacon, 2007
Longitudinal Research: Quantitative versus Qualitative Source: Molloy, Woodfield & Bacon, 2007
Advantages of Longitudinal Research • Examines change over time • Allows for exploration of topics with a developmental aspect (e.g. processes related to aging, career trajectories) • Includes changing context
Qualitative Longitudinal Research (QLR) • Provides information on people’s perspectives and how and why these are perceived to have changed over time • Explores changes over time and processes associated with these changes • Detailed accounts of people’s experience of particular situations Source:Farrall, 2007; MacMillan, 2011; Molloy, Woodfield & Bacon, 2007
Key Principles of QLR Temporal element • annual waves of data collection Change • what changes from year to year and the processes associated with that change Relational account • how change is related to political and economic contexts Role of multiple factors in complex systems Source: MacMillan, 2011
Why Use QLR in Policy Research? • Produces in-depth knowledge about the individuals or groups within particular policy contexts. • Can be used to evaluate policy interventions because it is flexible and based on real-time developments Source: Neale & Morton, 2012.
Why use QLR in Policy Research? Examines experiences and perceptions of those affected by a policy decision over time e.g. realistic evaluation model emphasizes how and why policies work, and in what contexts (Pawson and Tilley, 1997). Source: Molloy, Woodfield & Bacon, 2002.
Advantages of QLR • In-depth analysis of phenomenon • Participants can reflect on responses provided in earlier interviews • Ability to link micro to macro- understanding change in relation to larger contexts (e.g. political, economic). Source: Farrall, 2007.
Disadvantages • Maintaining sample over time • Amount of data generated longitudinally • Analysis is complex and takes time
Ethical Considerations • Confidentiality • Repeated informed consent • Storing data
QLR Research Designs • In-depth interviews • fixed time intervals, same people, led by same research team • Retracing respondents from an earlier study • A long term follow-up of a particular group
Developing a Qualitative Longitudinal Database Providing an example
Nursing Graduate Guarantee 2007-2012 • Ontario policy initiative launched in 2007 • Incentive funding for employers • hire new graduate nurses • temporary full-time supernumerary positions • six months • Evaluated annually Source: Baumann, Hunsberger, Crea-Arsenio, & Idriss-Wheeler, 2012.
Policy Evaluation Research question: What is the impact of the NGG on full-time employment of new graduate nurses?
Sample • Examined employer perceptions of NGG over time • Sampled repeatedly at one year intervals over five years (2007-2012) • Semi-structured interview guide • Annual focus groups
Applying Principles of QLR *FT=full-time
Interview Guide • Developed in 2007 to evaluate employers perceptions of NGG • Questions added as new ideas emerge from data each year • Questions guided by unique context of each year
Coding Data • Open coding: line-by-line coding independently by two researchers • Compared across researchers to establish common themes • Coding structure entered into database and used as base for following year’s analysis • New themes emerge each year
Coding Challenges • Data collection can be difficult at the outset because hard to know what data will be significant over time • Data may seem preliminary because data collection is on-going • Evidence of change may take years to emerge Source: Neale, 2011.
How QLR Data Can Inform Policy? • How do stakeholder perceptions change over time? • What contextual factors impact stakeholders perceptions? How do they impact perceptions? • What changes are significant for policy-makers? • How can policy influence stakeholder behaviours and attitudes?
Data Analysis • Analyze cross-sectional data after each wave of data collection • Longitudinal analysis of each case (creating employer profiles) • Longitudinal analysis across cases to examine change over waves of data collection
Use of Software • QSR N-vivo qualitative software allows researchers to develop database of interview data • Able to hold large amounts of data • Can append new data annually
Challenges of Using Software • Does not analyze data; stores data • Longitudinal data cleaning can be time consuming • Meaning of codes may change over time • Expensive
Employer Qualitative Database- An Example of QLR Research Five years of interview data including 196 organizations participating in 38 focus groups: • Examine changes in employer perceptions of hiring new graduates over time • Evaluate change in ability to offer full-time employment to new graduate nurses over time • Describe employer perceptions of changing political and economic contexts
New Member Nurse FT Employment (Source: Baumann et al., 2012) Source: Baumann, Hunsberger, Crea-Arsenio & Idriss-Wheeler, 2012.
Understanding the Change Over Time • Decrease in FT employment in 2010 was a result of a number of factors related to employer participation in NGG • Interview findings provided context for change and allowed researchers to examine the factors associated with this change
Factors Affecting Full-Time • Economic downturn impacted number of FT positions available for new graduates • High participation in early years of initiative limited employers ability to hire as many new graduates in 2010. • Number of new graduates hired in 2010 would have been even less had the NGG funding not been available. Source: Baumann, Hunsberger, & Crea-Arsenio & Idriss-Wheeler, 2011.
Context for Policy • Qualitative database allowed for: • an examination of employer perspectives over time • an understanding of change in employer hiring practices • Provided evidence for policy-makers to continue to invest in new graduate nurses
Conclusions • Developing qualitative databases can provide researchers with detailed information about perceptions and experiences over time • Qualitative databases can be used to enhance quantitative databases • Databases allow opportunity for secondary analysis of qualitative data
References Barratt, H. & Kirwan, M. (2009). Design, Application, Strengths & Weaknesses of Cross-Sectional Studies. HealthKnowledge. Avaialable: http://www.healthknowledge.org.uk/ Burns, N. & Grove, S.K. (2005) The Practice of Nursing Research: Conduct, Critique, and Utilization (5th Ed.). St. Louis, Elsevier Saunders. Baumann, A., Hunsberger, M.,Crea-Arsenio, M. & Idriss-Wheeler, D. (2012). Health Human Resource Series Number 35. Employment integration of nursing graduates: Evaluation of a provincial policy strategy. Hamilton, Ontario: Nursing Health Services Research Unit, McMaster University. Baumann, A., Hunsberger, M., &Crea-Arsenio, M. (2011). Health Human Resource Series Number 29. Employment integration of nursing graduates: Evaluation of a provincial policy strategy. Hamilton, Ontario: Nursing Health Services Research Unit, McMaster University.
References Farrall, S. (2007). What is qualitative longitudinal research? Papers in Social Research Methods Qualitative Series no 11. London School of Economics and Political Science Methodology Institute . Molloy, D., Woodfield, K. & Bacon, J. (2007). Longitudinal qualitative research approaches in evaluation studies. Department for Work and Pensions Working Paper No. 7, London: HMSO. Ruspini, E. (1999). Longitudinal research and the analysis of social change. Quality and Quantity, 33, 219–227. MacMillan, R. (2011). Seeing things differently? The promise of qualitative longitudinal research on the third sector. Third Sector Research Centre Working Paper 56. Neale, B. and Morton. S (2012) Creating Impact through QL research. Timescapes Methods Guide Series, No 20. Wunsch, H., Harrison, D., Rowan, K. (2005). Health Services Research in critical care using administrative data. Journal of Critical Care, 20, 264-269.
Contact Information Andrea Baumann, PhD Scientific Director Nursing Health Services Research Unit McMaster University Michael DeGroote Centre for Learning MDCL 3500 (905) 525-9140 ext. 22581 baumanna@mcmaster.ca www.nhsru.com