Lecture 5 Conducting Interviews and Focus Groups Talking to participants enables in-depth information about the experiences of health and illness; and of factors that influence health and illness behaviour Types of Interviews Positives and Negatives Emphasis on the participant s perspective of the world, and how they conceptualise their experiences of health In-depth information, prompts used to elicit further information about responses Use non-verbal cues, for example, recording gestures, expressions Flexible and exploratory, allows for unexpected discoveries Useful for getting preliminary insights about an unexplored area, for example, as part of a mixed methods study Participants have control over how much they tell the interviewer Allows for the discussion of sensitive topics Time consuming, length is usually longer that 30 minutes, travel time, participants need to give up their time Expensive, audio-recording, transcription Data analysis is very time consuming especially when there is lots of data generated Interviewer training and skills rapport, asking questions, being responsive to participant, closing the interview Lecture 6 Observation and Participatory Methods Observation Positives and Negatives Allows you to see how people act (which might be different to the way they think they act) See practices & interactions in context, good for practice improvement Flexible and exploratory Very slow and demanding, long period of immersion, collection of a lot of data Access can be difficult to negotiate and harder to ensure people are not coerced, situated ethics, consent forms
Questions with an organisational or institutional component Can include material and non- human components Holistic Approach Study context from specific features Spradley (1980, p.78) nine categories 1. Space: the physical place or places 2. Actor: the people involved 3. Act: Single actions that people do 4. Activity: A set of related acts that people do 5. Event: a set of related activities that people carry out 6. Object: the physical things that are present 7. Time: the sequencing that takes place over time 8. Goal: the things people are trying to accomplish 9. Feeling: the emotions expressed Personally challenging for researchers, what to do when you see something bad? Quality of data depends on relationship/rapport and researcher skill in making field notes Lecture 7 Analysing Qualitative Data Types of Qualitative Data Analysis Thematic Analysis Possible ideas or themes that would come from this include: gender roles (men vs women), inequality and discrimination
Step 1: Immersion in the Data Field notes after each (audio-recorded) interview, refer to field notes to increase understanding Listening to the audio recording Transcribe the audio recording, verbatim, de-identify Reading and re-reading the whole text Making notes: what is it about, major themes, unusual issues Step 2: Coding Data Coding is marking of segments of data/text with descriptive words, labels, etc. Step 3: Creating Categories Creating categories build on codes Lecture 8 Introduction to Quantitative Research Qualitative Research Quantitative Research
Emphasises the collection of narrative or words as data Generation of theory Inductive approach, bottom-up Thematic, discourse, narrative forms of analysis, often informed by phenomenology, critical social theory In-depth accounts of human experience, emphasises importance of authenticity and transferability Quantitative Research Emphasises collection of data that can be counted or quantified Hypothesis testing, usually based on existing theory Deductive approach, top-down Statistical data analysis, for example, t- tests, Chi-square analysis, logistic regression to produce statistically significant results Generalisability, reliability and validity Sources of Error in all Study Types Random error Imprecise measurement, chance findings Solutions: Repeat measurements, replicate studies Bias error is a systematic error that leads to a difference between the study results and the truth As your sample size increases, random error decreases, but bias does not go away Confounding error occurs when the relationship between study factor and the outcome is confounded or confused by another factor Bias Bias: a systematic error that means study results are different from the true result
Sources of bias can come from sampling, participants, measurements, researchers, or follow-up Sampling bias Participants/the sample are not typical or representative of the population from which they have been selected Study Designs Hierarchy of Evidence As the level of evidence increases there is an increase in the strength of evidence, and the quality of evidence There is also an increase in the practical or clinical significance to healthcare Types of Study Designs Experimental Studies Randomised control trials Examines the effectiveness of an intervention, study factor Random allocation of participants into groups, controlled, for example, those that receive intervention and those that don t Pre- and post-measurement using standardized measures Blinding to reduce bias and placebo effects Randomised controlled trials of the impact of coffee-drinking on university student s exam results Observational Studies Cohort Studies Are observational Are longitudinal or follows up participants over time Are usually prospective or looking forward from the study factor to the outcome At follow ups, it is important to see if the participant s have the outcome of interest
Provides evidence that the study factor comes before the outcome Possible to examine the effects of more than one study factor Useful for investigating rare study factors (cohorts are usually large) Diseases with a long latent period will need a long follow-up time Careful follow-up is needed to avoid selection bias (people may move, dropout, become unwell, die) Case-control Studies Are observational Are retrospective or looks back in time from outcome to study factor Usually involves matching on potential confounders Have participants that already have the disease/condition that is being studied Want to find people without it in order to find that both the control group and the conditions causes have risk further exposure of study factor in their path, correlation and causation study This can be done by looking through patient health history Relatively quicker and cheaper than cohort studies Enables examination of more than one risk factor Good for investigating rare diseases Useful for investigating diseases with long latent periods Retrospective, more prone to selection and information bias Difficult to establish time between exposure to risk factor and development of disease Cases do not usually reflect population as a whole Cannot examine one risk factor (or possible cause) and several diseases
Lecture 9 Quantitative Research Methods: Experimental Studies Randomised Controlled Trials (RCTs) Experimental study in human health research Ideal study design or gold standard Aim to eliminate confounding, randomization attempts to make group as similar as possible to reduce confounding RCTs aim to trial or evaluate a particular treatment or intervention by: Randomising participants to intervention and control groups Comparing outcomes in intervention group to outcomes in the control group Provides strong evidence for causality Can be very costly, time- consuming, i.e. intervention affects the outcome and complex to conduct Allows effectiveness of intervention to Careful attention to ethical issues is be evaluated compared to usual / required alternative treatment Participants may refuse the Less prone to confounding than intervention (non-compliance) and this observational studies may affect the results Error and Bias Random error - Imprecise measurement, chance findings - Solutions: Repeat measurements, replicate studies Bias is a systematic error that leads to a difference between the study results and the truth Confounding occurs when the relationship between study factor and the outcome is confounded or confused by another factor As your sample size increases, random error decrease but bias does not go away Two main types of bias Selection bias which concerns how participants are selected into the study and how they are followed-up - Impacts the representativeness of the study sample - Affects the generalisability of results to target population Measurement bias which concerns measurement of the study factor and the outcome Impacts the validity and reliability of data, study factor Affects the validity and reliability of the study results, of outcome
Systematic Reviews and Meta-Analyses Lecture 11 Introduction to Statistics Any value of a sample is statistic Variable, operationalisation and data Variable is the building block of quantitative research; it is anything that varies in a given study; the opposite of variable: a constant Operationalisation: precise description of how a variable will be measured, for example, work hours, easy to operationalise; mental health, much harder Data: facts, measures