Help needed in 10 multiple choice questions with 15 minute, there are 2 attaempt option are avaiable.Attahced is an sample file that i did it
Quiz_ Progress Quiz Week 2 11398 Lecture 2-4 8/3/21 1 Lecture Week 2-4: Reviews and meta-analyses Introduction to Research in the Health Sciences 1 Research designs Descriptive Correlational Quasi-experimental Experimental Review Meta-analytic 2 2 Reviews • Examine previous literature on a particular topic. Considered secondary sources, as no new raw data is collected. Reviews vary substantially in how systematically the literature is collected and collated. • Examples: • Narrative literature review • Systematic literature review 3 3 8/3/21 2 Reviews: Systematic literature review • A systematic review answers a defined research question by using systematic methods to collect and summarise all empirical evidence that fits pre-specified eligibility criteria. • Tell the reader what the research question is, describe exactly how and from which databases you will search for studies, use specific criteria to evaluate the studies, collect data from the studies, report the findings, interpret the findings. Sometimes updated versions are published later. • Pros: • Rigorous, replicable assessment of findings on a certain topic – the best evidence available to answer a particular research question • Cons: • Tends to be very narrow in focus, so answers one question very well, but doesn’t necessarily give the bigger picture 4 4 https://onlinelibrary.wiley.com/doi/full/10.1111/inm.12390 5 5 6 6 https://onlinelibrary.wiley.com/doi/full/10.1111/inm.12390 8/3/21 3 https://onlinelibrary.wiley.com/doi/full/10.1111/1747-0080.12402 7 7 8 8 Meta-analyses • Pull together and statistically analyse data from other studies to generate a findings summarising a particular topic. Often reveals patterns or subgroupings within findings in a field. Classified as a secondary source. • Typically conduct a systematic review and a meta-analysis – the meta-analysis is the further statistical analysis of the findings of the systematic review. • Pros: • Objective assessment of evidence • Generates new outcomes – e.g., an effect size for that intervention • Cons: • Selection of studies may be biased; publication bias can affect conclusions 9 9 https://onlinelibrary.wiley.com/doi/full/10.1111/1747-0080.12402 8/3/21 4 https://www.sciencedirect.com/science/article/pii/S0272494418303359?via%3Dihub 10 10 11 11 Research designs: Which is best? • A theory can be explored using a wide range of designs – one design isn’t ’better’ than another, they just provide different ways of investigating the theory • For example, let’s say you wanted to investigate the theory that exposure to traumatic events leads to a higher likelihood of addiction due to structural changes in the brain? • You could answer this using any of the designs we’ve discussed so far (and many more!)… 12 12 https://www.sciencedirect.com/science/article/pii/S0272494418303359?via%3Dihub 8/3/21 5 Descriptive: Case study • Carefully watch and take notes of a person’s behaviour after they have experienced a traumatic event. Interview them about their experiences. • Report how the person’s behaviour changes over time, how they react to different events and situations; obtain detailed information about this particular case. Correlational: Survey • Hand out a questionnaire to 100 people. Ask how many traumatic events they’ve experienced and ask how much alcohol they consume. • Conduct analyses to work out what the relationship is between number of traumatic events and alcohol consumption. • Write up the findings, focusing on whether a greater number of traumatic events in someone’s history is associated with higher alcohol consumption 13 13 Quasi-experimental: Non-randomised experiment • Recruit two groups of people – people who’ve experienced traumatic event (treatment) and those that haven’t (control). Assess the differences between the two groups in terms of the amount of alcohol they consume. • IV = exposure to traumatic events (Group A includes only people who have experienced a traumatic event; Group B includes only people who have not experienced a traumatic event) • DV = alcohol consumption (Provide alcohol to both groups, measure how much people drink) Experiment: Randomised controlled trial • We can’t realistically/ethically manipulate the variable we’re interested in (traumatic events) • Modify the research question: Higher levels of stress cause people to drink more alcohol, so manipulate stress level and measure alcohol consumption: • IV = exposure to stress (Group A put in a high stress environment with loud noises, hot, bright lights; Group B put in a normal stress environment) • DV = alcohol consumption (Provide alcohol to both groups, measure how much people drink) 14 14 Review: Systematic review • Define the topic of the review very specifically – so a review of only peer-reviewed articles between 1960 and the present reporting randomised trials where the experiment involved manipulating stress as the IV and the DV was alcohol consumption. • Conduct a systematic search of relevant databases using these criteria, and then summarise the findings in a single article. Can then draw conclusions about the current state of evidence - does the data currently suggest that stress affect alcohol consumption? Meta-analysis • Define the topic of the meta-analysis very specifically (as above) but also conduct statistical analyses on the data from the studies. So for example, could conclude that there is on average there is a significant difference of .52 standard units of alcohol consumption between stressed and non-stressed participants, and an effect size of .27. 15 8/3/21 6 Other ways of classifying research •High vs low constraint • Exploratory vs. confirmatory • Fixed vs. flexible 16 16 11398 Lecture 2-5 8/3/21 1 Lecture Week 2-5: Operationalisation Introduction to Research in the Health Sciences 1 Operationalising variables • When you operationalise a variable you are defining what you are measuring and how • You are deciding, for the purposes of the research you are conducting, specifically how you would manipulate or measure that variable • This might involve a specific question in a survey, with a particular response scale. Or using equipment in a certain way under certain circumstances. Or asking someone to perform a particular task. • e.g., let’s say want to measure how healthy someone is, how exactly would you do this? • What about measuring stress? Performance? Disadvantage? Deaths? 2 2 Operationalising variables • Often operationalisation is driven by logistical/contextual issues as well as theoretical approach • i.e., what can reasonably be measured? What equipment do you have? What funding do you have? Are participants able/willing to be measured in this way? • How you operationalise the variable impacts what statistical tests you can run during analysis • e.g., age in categories (20-30; 30-40) versus as a number (29 yrs) • e.g., ‘experiencing pain’ (yes/no) versus as a scale (1-7) • Huge implications for the conclusions that can be drawn • e.g., If you have operationalised ‘health’ as BMI, does this reflect all aspects of health? 3 3 8/3/21 2 Operationalising variables: Example • Aggression can be operationalised in many ways, including: • Criminal record • Self-reported history of violent incidents (would need to define ’violent’) • Number of violent incidents displayed when playing as a video game character • Allocation of spicy sauce to another person • Neural activation in specific regions of the brain • How fast they recognize aggressive words • Aggression survey • How much they help other people • Internet browser history • Desensitisation to violent images 4 4 5 5 Operationalising variables: Relevance-sensitivity trade-off • What we’re looking for in a variable is a measurement that is sensitive enough to show a difference that you’re interested in (i.e., don’t measure something that is unlikely to show change) • But you also need to be careful not to pick a variable that is so specific that it isn’t relevant beyond the study to other contexts (i.e., don’t measure something that isn’t meaningful in the wider world) 6 6 8/3/21 3 Relevance-sensitivity trade-off: Examples • Let’s say we want to measure the effect of alcohol consumption on the likelihood of having an accident when driving: • Randomise 40 people to drunk or sober conditions (IV with 2 levels) and measure number of accidents they have (DV) • BUT accidents are rare! In a single study probably won’t be any. So this DV may not be sensitive enough. • We could instead measure the number of times people look away from the road. But does the number of times someone ‘looks away’ actually tell us about accidents? This DV may not be relevant enough. • How to measure anxiety? • How to measure falls? 7 7 Tips for operationalising variables 8 Be realistic - think about logistics in in your study (where and how will you actually gather the data?) Use existing measures (e.g., questionnaires, equipment) (they're already tried and tested!) Try it out (often it’s only when you try to use a measure that you realise it’s no good!) ‘Shadow’ data collection in similar studies (ask other researchers what they are doing, and if you can observe!) Use two measures of the same variable if possible (this gives you greater confidence and insight!) Look across fields (do researchers in other areas have a different way of measuring that variable you could use?) 8 Lecture summary • Terminology • Research designs • Descriptive • Correlational • Quasi-experimental • Experimental • Review • Meta-analytic • Operationalisation 9 9 8/3/21 4 Next lecture 10 • Research design issues: • Sampling • Validity • Reliability • Additional design issues in surveys • Additional design issues in experiments 10 11398 Lecture 2-2 8/3/21 1 Lecture Week 2-2: Descriptive and correlational designs Introduction to Research in the Health Sciences 1 Research designs • There are countless ways research can be conducted, and different ways these approaches can be grouped together. • One common categorisation of research designs is: Descriptive Correlational Quasi-experimental Experimental Review Meta-analytic 2 2 Descriptive research designs • Don’t manipulate any variables, take advantage of the natural flow of behavior • Some are highly ‘flexible’ (allow the researcher to take advantage of unexpected occurrences and new ideas) • Examples: • Archival research • Naturalistic observation • Surveys • Program evaluation • Case studies 3 3 8/3/21 2 Descriptive research designs: Case studies • Focus on the behaviour of a single individual • Often individuals in traumatic or extreme situations • Pros: • Rich source information • Allows researchers to study rare disorders or circumstances • Cons: • Can’t generalise • Don’t know for sure what causes the individual’s behaviour – the event/disorder, some personality characteristic, or some combination of the two 4 4 https://onlinelibrary.wiley.com/doi/10.1111/ijun.12124 5 5 https://www.tandfonline.com/doi/abs/10.1080/00049158.2012.10676388 6 6 https://onlinelibrary.wiley.com/doi/10.1111/ijun.12124 https://www.tandfonline.com/doi/abs/10.1080/00049158.2012.10676388 8/3/21 3 Correlational research designs • Look at the relationship between variables, but where the variables are not under the researcher’s direct control (for logistical or ethical reasons) • Examples: • Case-control studies • Observational research