Assignment: Critically Appraising Quantitative Studies
Assignment: Critically Appraising Quantitative Studies
Assignment: Critically Appraising Quantitative Studies
Assignment: Critically Appraising Quantitative Studies
Quantitative studies are essential in the research processes as they lead to both the statistical and clinical significance. Most of the qualitative research processes use primary data to establish the research outcomes or to answer the research objectives. Quantitative studies involves the incorporation of different methodologies that ensure that there is accuracy in the research outcomes (Cook et al., 2016). Most of the researchers often find quantitative research processes to be accurate given the fact that it relies on the primary data to establish accurate results.
While assessing the quantitative research studies, it is always necessary to consider different factors that often lead to the accuracy of the outcomes. First, there is always the need to consider the methodology that have been employed in the study process. To ensure accurate and reliable outcomes, it is always necessary for the quantitative researchers to follow the right methodology (Noyes et al., 2018). The right methodology often involve affective data collection processes, data analysis and the presentation of the research outcomes. Appraising the quantitative research studies should therefore involve looking into the study designs and the relevance of each design as applied in the research process.
Other factors that need to be assessed while appraising quantitative research include validity, applicability and the reliability. Validity refers to how accurately the method used in the study process conforms to the intended outcomes. Applicability on the other hand refers to the extent to which the research or the study outcomes can be applied in improving different healthcare situations (Zeng et al., 2015). Finally, reliability refers to how well a method, technique or test measures something. Assessing the methodology applied, and the validity are the most important because they will always tell the essence of the study.
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References
Cook, D. A., Kuper, A., Hatala, R., & Ginsburg, S. (2016). When assessment data are words: validity evidence for qualitative educational assessments. Academic Medicine, 91(10), 1359-1369. Retrieved from: https://doi.org/10.1097/ACM.0000000000001175
Noyes, J., Booth, A., Flemming, K., Garside, R., Harden, A., Lewin, S., … & Thomas, J. (2018). Cochrane Qualitative and Implementation Methods Group guidance series—paper 3: methods for assessing methodological limitations, data extraction and synthesis, and confidence in synthesized qualitative findings. Journal of clinical epidemiology, 97, 49-58. Retrieved from: https://doi.org/10.1016/j.jclinepi.2017.06.020
Zeng, X., Zhang, Y., Kwong, J. S., Zhang, C., Li, S., Sun, F., … & Du, L. (2015). The methodological quality assessment tools for preclinical and clinical studies, systematic review and meta‐analysis, and clinical practice guideline: a systematic review. Journal of evidence-based medicine, 8(1), 2-10. Retrieved from: https://doi.org/10.1111/jebm.12141
What factors must be assessed when critically appraising quantitative studies?
Which is the most important? Why?
The percentage of suicide attempters with risk factors, pro- tective factors, and diagnosis of mental illness are presented in Table 1. In addition to risk and protective factors, features of the attempt, such as prior planning (11.0% Yes, 89.0% No), attempt to hide (30.3% Yes, 69.7% No), and place of suicide attempt (80.1% Home, 2.2% Workplace, 9.3% Public place, 2.8% Friend’s house, .6% Public building) were also included in the analysis.
Direct logistic regression was performed to assess the impact of available variables, namely, risk factors, protective factors, and features of the suicide attempt on the likelihood that suicide attempters were diagnosed with mental illness. Logistic regression was used in similar studies for a large number of predictors [25, 26] and is typically used to develop a subset of variables useful for predicting the criterion, by eliminating superfluous variables. Our sample size is sufficiently large and representative for statistical regression [47]. The full model (see Table 2) containing all available predictors was statistically significant, ?2 (23, N = 462) = 83.40, p < .001, indicating that the model was able to distinguish between attempters with and without diagnosis of mental illness.Themodel as awhole explained between 16.5% (Cox and Snell?2) and 24.4% (Nagelkerke?2) of the variance inmental illness and correctly classified 79.0% of the cases. As shown in Table 3, only six of the independent variables made a unique statistically significant contribution to the model (unemployment, mental illness or suicide in family, alcohol or drug abuse, habitual poor coping, willing to seek help, and positive future planning). The strongest predictor of mental illness was mental illness or suicide in family, with an odds ratio of 2.75. This indicated that attempters who had mental illness or suicide in family were 2.75 times more likely to have a diagnosis of mental illness than those without mental illness, controlling for all other predictors in the model. The second strongest predictor was unemployment with an odds ratio of 2.43. This indicated that attempters who were unemployed were 2.43 times more likely to have diagnosis of mental illness. The third strongest predictor was willing to seek help, with an odds ratio of 2.28. This indicated that attempters who were willing to seek help were 2.28 times
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