Appraising quantitative studies
HCA 699 Topic 3 DQ 2
What factors must be assessed when critically appraising quantitative studies (i.e., validity, reliability, and applicability)? Which is the most important? Why?
ADDITIONAL DETAILS
Appraising quantitative studies
Introduction
Quantitative studies are a useful way of understanding how people think and behave. They can help us understand emerging trends in society, identify gaps in our knowledge and reveal what might be possible with new strategies or approaches. In the context of social science research, quantitative methods involve collecting data by means such as surveys or interviews and then analysing them to produce statistical results.
To appraise quantitative research ask yourself …
The first step to appraisal is to ask yourself whether the research question is clear. If it isn’t, then you’ll need to make sure that it’s an appropriate study method for answering your question.
For example, if you want to know what kind of impact a particular piece of legislation has on employment levels in different sectors of society and industries, then consider using job survey data from before and after its introduction (or from other countries where similar laws have been passed). However, if your goal is simply to understand how much easier or harder it was for someone with low-level skillset compared with someone who already had higher qualifications prior to these changes being introduced – then perhaps there are better ways than this one?
Is the research question clear?
The first step in evaluating a quantitative study is to determine if the research question is clear. If you can’t answer it, then how can you tell if the data supports or refutes your hypothesis? A good way to think about this is by asking yourself:
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Is my research question specific? Is my hypothesis clearly defined? Does it make sense given what I know about this topic and what I want to find out about it (for example, does the hypothesis fit with my overall understanding of human behavior)?
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Is my research question interesting enough for me to read through all 100+ pages of an academic article on it? Or do I have another reason for wanting more information on this topic (maybe someone else has written something similar)?
Is the study method appropriate?
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Describe the research method chosen.
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Explain why it was chosen and how it was implemented.
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Explain how the study was evaluated, including any limitations or limitations that might have been introduced by the study design.
Where appropriate, is a systematic review or meta-analysis used to synthesise results?
A systematic review or meta-analysis is appropriate when there is a large body of evidence. This means that there are many studies, which can be described as high quality research.
However, if the body of evidence is small and limited to few studies with low quality research then a meta-analysis may not be appropriate.
Do the researchers consider relevant ethical issues?
Ethical issues are important to consider when performing quantitative research. The researchers should follow the ethical guidelines and be aware of the ethical implications of their study.
Are participants clearly described?
When reading a quantitative study, it is important to be aware of the research population. This means identifying the people who were involved in the research and describing them. It also means describing how they were identified and recruited into the study.
For example, if you are analyzing data from a survey on customer satisfaction with a new product, then you should know who took part in this survey and what criteria was used to determine who would take part in it (i.e., did they receive an invitation via email or was there some other way?). You should also describe how long each participant had been working for this company before taking part in your study—this will help give context as well because not everyone may have been at their job long enough yet for their opinions about products/services/marketing strategies etc.,
Are study outcomes valid and reliable?
Validity and reliability are two terms you’ll hear a lot when it comes to evaluating quantitative studies. Validity refers to the extent to which a measure actually measures what it is supposed to measure, while reliability refers to the consistency with which answers can be produced by participants.
Valid measures have been developed through careful research, whereas unreliable measures may result from poor data collection or lack of validating questions. In addition, some researchers have found that certain types of questions are more difficult for participants than others because they require more cognitive effort; these types of questions should not be used in your study unless there is no other way around them (for example by using multiple choice or Likert scales).
Is data collection rigorous and unbiased?
Is the data collection rigorous and unbiased?
The most important aspect of any quantitative study is that it be conducted in a way that minimises bias and error. If a researcher has an agenda, he or she will likely use his or her own preconceptions to shape the results of each study. For example: if you’re studying whether people prefer peanut butter to chocolate chip cookies, then you might ask your participants what they prefer between those two options before they taste them (in other words, have them choose one option over another). This would give us data on what we expected—but it also leaves open the possibility that some people may have picked out their favorite cookie in order to answer our question truthfully.
In addition to this possibility of biased selection by subject selection, researchers should also ensure confidentiality during data collection so as not reveal personal information about participants such as gender identity preferences or health conditions without consent from those involved in the research project
Were sufficient numbers of participants recruited and analysed to enable the research findings to be reliable?
The sample size should be large enough to detect the expected effect size. A larger sample size reduces the risk of sampling error, but there is no such thing as a ‘too large’ sample: if you want your research findings to be reliable, then you need to ensure that your sample has sufficient numbers of participants.
It’s also worth considering how many people will be included in your study – this can affect how long it takes for results from each individual participant (and therefore how accurate those results are) because researchers need time-consuming tasks such as data collection and analysis before they can start drawing any conclusions from their work.
Are any potential sources of bias accounted for and explained in the analysis and discussion of results?
Bias is a systematic error that may occur in the collection, analysis or interpretation of data. A potential source of bias may be due to methodological decisions, such as how participants are recruited and measured.
Bias can lead to the results of a study being wrong because it changes your interpretation of what you are seeing from what you actually saw. For example, if there was no effect for an intervention but instead many results showed that it had no effect then this would be a result that was biased by its methodologies (i.e., recruiting participants).
Conclusion
This is a good time to pause and reflect on what we’ve done so far. We’ve covered a lot of ground, from the importance of having clear research questions, through to ensuring that your data is collected and analysed in an appropriate way. In fact, I hope you found this guide useful!
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