The research process

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  • The Research Process - Quantitative Katherine Ryan (kate.ryan@staffs.ac.uk)
  • The Research Process - Quantitative Katherine Ryan (kate.ryan@staffs.ac.uk)
  • The Research Process - Quantitative Katherine Ryan (kate.ryan@staffs.ac.uk) Formulating a research Q – could be based on many things. Theories, literature, direct observation in work or clinical setting. Often takes time to refine a research Q from an original area of interest. Must search all sources in the area to see what the current level and scope of knowledge is in the area. Work can be exploratory but generally if testing a hypothesis you need to be testing it in a way that it can be negative as well as positive.
  • The Research Process - Quantitative Katherine Ryan (kate.ryan@staffs.ac.uk) Experimental Ho – Watching violent tv makes children more aggressive Null Ho – There is no relationship between watching violent tv and children’s aggression levels
  • The Research Process - Quantitative Katherine Ryan (kate.ryan@staffs.ac.uk)
  • The Research Process - Quantitative Katherine Ryan (kate.ryan@staffs.ac.uk) Was sampling biased in any way Is the sample very small and as such unlikely to represent the population results Validity (does it measure what it says it measures?) In physical measurement we usually have a ‘gold standard’ and accurate test such as beam balances for weighing people or timing with a stop watch how long it takes someone to walk 500 metres. But in clinical assessment we often don’t have gold standards e.g. measurement of depression. Fortunately there are a number of procedures available to assess validity and control threats to it. Reliability (does it do it consistently?) Several procedures to assess this such as test-retest R, interobserver R(2 or more clinicians – correlation), and internal consistency (how different items correlate with each other in a measure/test, usually reported as a Chronbach’s alpha. .08 usually considered desireable property of a test/scale. Brainstorm – threats of validity and reliability in terms of procedure etc (aim to think of 5 threats to each)
  • The Research Process - Quantitative Katherine Ryan (kate.ryan@staffs.ac.uk)
  • The Research Process - Quantitative Katherine Ryan (kate.ryan@staffs.ac.uk)
  • The Research Process - Quantitative Katherine Ryan (kate.ryan@staffs.ac.uk)
  • The Research Process - Quantitative Katherine Ryan (kate.ryan@staffs.ac.uk) Interpreting findings – may support existing theories or practices, suggest new techniques, or suggest new theoretical notions. It is rare for any single research project to be completely definite, and often results may suggest the need for further investigation in related subject areas or contexts. Research finding become part of scientific knowledge only if they stand up to methodological critique and replication. These stages of research represent a logical sequence which is followed in most research projects. An exception is in the case qualitative research where a more flexible sequence might be employed – also empirical evidence is not normally summarised and analysed through statistical methods, but through the use of language and the analysis of narratives.
  • The Research Process - Quantitative Katherine Ryan (kate.ryan@staffs.ac.uk)
  • The Research Process - Quantitative Katherine Ryan (kate.ryan@staffs.ac.uk) When designing or reviewing published research you need to consider concepts such as design, measurement and statistics. INTRODUCTION – reflects the planning of the research – inadequacies at this point might reflect problems with the study. LIT REVIEW – must be sufficiently complete to reflect current state of knowledge in the area and include all key papers which have direct consequences for the research hypotheses/aims. Must be presented in an unbiased way. AIMS – should be clearly and operationally stated. If lacking this how can the evidence obtained be used for conceptual/theoretical advances in the area Selection of APP RESEARCH STRATEGY/method – must use app. Method to meet aims. E.g. if demonstration of a causal effect is required, a survey may be inappropriate for satisfying the aim of the research. APP VARIABLES – important as inapp variables to the constructs being investigated then will not produce useful results. METHOD – was the sample representative of the target population and an adequate sampling method used, was the size adequate. A clear description of the sample – age, sex…. – and if possible demographic information should be provided so the reader can judge if the findings are applicable to the specific groups e.g. patients being treated MEASURES – Validity and reliability to be reported for new measures or will raise questions about the adequacy of the findings. Full descriptions of measures/instruments needed so study can be replicated by independent parties. PROCEDURE – full description needed so adequacy of design can be assessed. Including info like whether there were control groups to control for extraneous effects e.g. placebo group, or how this problem of internal validity was dealt with. How were subject assigned to groups, any treatment parameters – any differences in intensity or administering of personnel.
  • The Research Process - Quantitative Katherine Ryan (kate.ryan@staffs.ac.uk) RESULTS – inadequacies indicate that inferences/conclusions drawn by the investigator were incorrect. Selection of appropriate statistics according to specific rules as inapp stats could distort the findings and lead to inapp inferences/conclusions. DISCUSSION – Inferences must be made correctly or might lead to useless or dangerous treatments being offered to patients. Logically correct interpretations of the findings. Any protocol deviations need to be discussed with any implications for the results. Generally speaking data obtained from a given sample are generalisable only to the population from which the sample was drawn. This is sometimes ignored by investigators and finding get generalised to participants or situations not considered in original sampling. Stat sig does not necessarily imply that the results are clinically applicable. In deciding on clinical significance, factors such as size of the effect, side effects, and cost effectiveness as well as value judgements need to be considered. Theoretical sig – is necessary to relate the results of an investigation to any previous studies and findings identified in the lit review. Unless results are logically related to the lit, the theoretical significance of the investigation remains unclear. Keep in mind that even where an investigation is flawed, useful knowledge might be drawn from it. The aim of critical analysis is not to discredit or tear down published work, but to ensure that the reader understands it’s implications and limitations with respect to theory and practice.
  • The research process

    1. 1. The Research Process
    2. 2. AIM <ul><li>Promote understanding of the research process, </li></ul><ul><li>methods and statistical analyses reported in </li></ul><ul><li>publications </li></ul><ul><li>Identify strengths and weaknesses in papers </li></ul><ul><li>Better understanding of research methods </li></ul><ul><li>Understand the meaning of relevant statistical terms </li></ul><ul><li>To increase confidence in reading and using the literature </li></ul>
    3. 3. The Research Process <ul><li>What is research? </li></ul><ul><li>Systematic and principled method of obtaining evidence (data/information) </li></ul><ul><li>Why do we need research? </li></ul><ul><ul><li>Increased knowledge </li></ul></ul><ul><ul><li>Informed judgements </li></ul></ul><ul><ul><li>Credibility </li></ul></ul><ul><ul><li>Best practice - effectiveness, efficiency (cost) </li></ul></ul><ul><ul><li>Solve problems </li></ul></ul>
    4. 4. <ul><li>Planning & Aims </li></ul><ul><li>Formulating a research question/hypotheses </li></ul><ul><li>Selecting appropriate strategies and measurements to answer your research question or to test your hypotheses. </li></ul><ul><li>Consider previous research evidence, literature, ethical and economic factors </li></ul>Planning Hypotheses or aims Research Design Data collection Organization and presentation of data Interpretation and conclusions Dissemination Data analysis
    5. 5. Hypotheses <ul><li>Formally have two hypotheses </li></ul><ul><ul><li>Experimental hypothesis – what you predict will be the outcome of the research </li></ul></ul><ul><ul><li>Null hypothesis – that there is no impact / relationship between the variables </li></ul></ul><ul><li>Aim of research is to reject the null hypothesis. </li></ul>
    6. 6. <ul><li>Research design & method </li></ul><ul><li>Deciding on appropriate method for gathering data to answer the research question </li></ul><ul><li>Consider issues which impact on method chosen, including sampling method, sample size, generalisability of results, reliability and validity </li></ul>Planning Hypotheses or aims Research Design Data collection Organization and presentation of data Interpretation and conclusions Dissemination Data analysis
    7. 7. Planning data collection <ul><li>Sampling </li></ul><ul><ul><li>Random samples: an equal probability of being selected </li></ul></ul><ul><ul><li>Non-random samples: Convenience, Purposive, Snowball </li></ul></ul><ul><ul><li>Size of sample </li></ul></ul><ul><ul><li>Validity - accuracy of the test or measure </li></ul></ul><ul><ul><li>Reliability - reproducibility of the results </li></ul></ul>
    8. 8. <ul><li>Data collection </li></ul><ul><li>Methods </li></ul><ul><li>Quantitative (numerical) </li></ul><ul><li>Qualitative (non-numerical) </li></ul><ul><li>Procedures & Measures </li></ul><ul><li>Different measurement scales </li></ul><ul><li>Standardised measures </li></ul><ul><li>Participant assignment </li></ul><ul><li>Setting </li></ul>Planning Hypotheses or aims Research Design Data collection Organization and presentation of data Interpretation and conclusions Dissemination Data analysis
    9. 9. <ul><li>Presentation of data </li></ul><ul><li>Descriptive statistics for summarising and describing the characteristics of the data </li></ul><ul><ul><li>Type of variables </li></ul></ul><ul><ul><li>Summary statistics </li></ul></ul><ul><ul><ul><li>Measures of central tendency e.g. mean </li></ul></ul></ul><ul><ul><li>Distribution </li></ul></ul><ul><ul><ul><li>Measures of variability </li></ul></ul></ul><ul><ul><li>Tables </li></ul></ul><ul><ul><li>Pictorial representation </li></ul></ul><ul><ul><ul><li>Graphs </li></ul></ul></ul>Planning Hypotheses or aims Research Design Data collection Organization and presentation of data Interpretation and conclusions Dissemination Data analysis
    10. 10. <ul><li>Data analysis </li></ul><ul><li>Inferential statistics - test whether or not the data support the experimental hypotheses </li></ul><ul><li>Selected according to specific rules about data type </li></ul><ul><li>Involves applying the principles of probability for calculating confidence intervals and testing hypotheses. </li></ul>Planning Hypotheses or aims Research Design Data collection Organization and presentation of data Interpretation and conclusions Dissemination Data analysis
    11. 11. Terminology Statistical significance (p values) p<.05 Test statistic is calculated when a hypothesis test is performed by software such as SPSS. The statistic helps us to decide whether or not the difference (e.g. between two groups of patients) or relationship (e.g. between smoking and lung cancer) is statistically significant which means it was unlikely to have arisen by chance. 95% confidence intervals – defines the range within which we are 95% confident that the true population value may be Found Standard deviation (SD) – most commonly used measure of variability - the distribution of values around the mean. If The SD is large the values are widely distributed, if it =0 then all values equal the mean.
    12. 12. <ul><li>Interpretation and dissemination </li></ul><ul><li>Final step of a research </li></ul><ul><li>project is to interpret it’s </li></ul><ul><li>findings </li></ul><ul><li>For research to be </li></ul><ul><li>scientifically meaningful, </li></ul><ul><li>investigators must present </li></ul><ul><li>their findings in professional </li></ul><ul><li>journals and/or conferences. </li></ul>Planning Hypotheses or aims Research Design Data collection Organization and presentation of data Interpretation and conclusions Dissemination Data analysis
    13. 13. Method Summary <ul><li>NO PERFECT DESIGN </li></ul><ul><li>YOU CHOOSE BALANCE </li></ul><ul><li>INTERNAL VALIDITY (Control) </li></ul><ul><li>EXTERNAL VALIDITY (Real-world ) </li></ul><ul><li>MINIMISE THREATS TO WHAT YOU WANT TO DETERMINE </li></ul><ul><li>TIME, RESOURCES - SEVERE LIMITATION!! </li></ul>
    14. 14. Critical appraisal quantitative data <ul><li>Introduction </li></ul><ul><ul><li>adequacy of lit review </li></ul></ul><ul><ul><li>Aims/hypotheses </li></ul></ul><ul><ul><li>Selection of appropriate research strategy </li></ul></ul><ul><ul><li>Selection of appropriate variables </li></ul></ul><ul><li>Method </li></ul><ul><ul><li>Sample size and sampling method </li></ul></ul><ul><ul><li>Measures/instruments </li></ul></ul><ul><ul><li>Procedure – design, control groups, assignment of participants </li></ul></ul>
    15. 15. Critical appraisal quantitative data <ul><li>Results </li></ul><ul><ul><li>Statistically correct summary </li></ul></ul><ul><ul><li>Descriptive and inferential statistics </li></ul></ul><ul><li>Discussion </li></ul><ul><ul><li>Correct inferences/conclusions </li></ul></ul><ul><ul><li>Generalisability of findings </li></ul></ul><ul><ul><li>Protocol deviations </li></ul></ul><ul><ul><li>Statistical and clinical significance </li></ul></ul><ul><ul><li>Theoretical significance </li></ul></ul>

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