Critique of Quantitative Research Report - Essay Example

A CRITIQUE OF “ATTITUDES OF UNDERGRADUATE HEALTH SCIENCE TOWARDS PATIENTS WITH WITH INTELLECTUAL DISABILITY, SUSTANCE ABUSE AND ACUTE MENTALILLNESS: A CROSS-SECTIONAL STUDY” By: Joanna Patynowska-slowik Presented to: Institution: Date: 3rd January 2013 Introduction The application of statistical knowledge spans across almost all modern academic and professional disciplines. However, learners in many non-mathematical disciplines have difficulty differentiating the exact tests to apply, despite knowing the kind of results they desired at the end of a particular analysis. The most commonly mistaken fields are the applications of parametric and non-parametric tests. A survey through various published works reveals that distinguishing between corresponding parametric and non-parametric tests has not really taken into the imaginations of most learners. In this review, the mishandling of the various parametric tests has been reviewed, including the most common error of its kind being reported by many critiques in the field- the application of ANOVA tests on non-parametric data in the article Attitudes of Undergraduate Health Science Students towards Patients with intellectual Disability, Substance Abuse and Acute mental Illness: A cross-sectional Study. This work represents one of many cases out there when researchers fail to conduct necessary investigations into the nature of the data they obtained for analysis. Statistically speaking, the requisites of conducting analysis on a set of data include cleaning the data, and classifying the same (that is according to its right distribution), so that the results will be cohesive with the distribution type. This paper looks into section per section analysis of the appropriateness of the literature thereof, and the appropriateness of the individual tests carried out on the data. It also explains lucidly why certain tests befit a certain category of data, and why their use may fail to impress when used on a different category of data. The instances that bear criticism for their wrongful representation are examined, and their suggested remedies listed. The paper concludes with the recommendation that the researchers re-test the original data so that they can overcome this most promiscuously standing shortcoming. Background Information: A close reference was made to the study Staff Regard towards Working with Substance Users: a European Multi-centre Study (Gilchrist et al). The aim of the study was to compare the levels of regard that medical practitioners have for working with various groups of patients for medical school students at the Monash University. Unwillingness to treat certain categories of patients stems from the perceived difficulties in handling them, lesser rewards from the intensive care required of the medics, and the general feeling of inadequacy of skills (Gilchrist et al 2011). The researchers note that the medical practitioners prefer treating other categories of patients, for instance those suffering from diabetes. Of special interest to this study is the section from which the researchers apply the t-tests and Analysis of Variance (ANOVA). On top of the ANOVA tests carried out inappropriately in this study (where the researchers should have used the non-parametric equal of ANOVA, the Mann-Whitney U-test. The overall implication of the errors presented by these researchers is that their findings can rightfully be termed as wrong and inappropriate, because they could not use the wrong approach to reach at their conclusions. In the end, the researchers confirmed that the medical practitioners surveyed in the various disciplines were least willing to work with patients who needed treatment for use of illicit drugs. It is not certain whether the same conclusion would have been reached if the correct tests were applied on the data. The source of the assumption that the data worked on by the researchers could be assumed to be normally distributed is not explained anywhere in their work. According to Ryan-Wenger (1992; cited Coughlan et al) enquiries into a critiquing tool can be classified into credibility variables and integrity variables. The following areas form the basis of the evaluation of the consistency of the work Attitudes of Undergraduate Health Science Students towards Patients with intellectual Disability, Substance Abuse and Acute mental Illness: A cross-sectional Study. The Research Problem/ Purpose of the Study: The researchers state that the purpose of their work is to measure the attitudes of undergraduate students enrolled in six health- related courses at Monash university towards patients with intellectual disability, substance abuse and acute mental illness (Boyle et al 2010). This is an interesting area where every stakeholder would love to read about. The choice of the topic of study for Boyle et al was not just remarkably relevant but also had a basic human-rights extension that gave it more strength. Logical Consistency: This is a check of the flow of the steps of the research process. The researchers give a deeply descriptive background of the basis of the study. They also explain some of the crucial terms used in the survey; for instance ,the term ‘attitude’ is brought out clearly to mean a positive or negative feeling towards a person. Several other jargons, including the ELM, HSM among others are also explained. Notably, the writers incorporate together the ‘Background’ section and the literature review which provides no complication to the intended delivery of the overall report since the works cited throughout the background are clearly relevant. Literature Review This work has also taken objectively to re-evaluate the relevance and contribution of the literature review as written in the work of Boyle and colleagues. As already mentioned, the writers chose to continue the literature review section with the background of the study. Top on the list of reviewed works is the Medical Condition Regard Scale (MCRS), the tool through which the questionnaires were administered. The high position held by healthcare professionals with regards to detecting and preventing abuse of substances highlights the irony of the mistreatment received by the acutely affected groups with mental illnesses, intellectual disability, and those who at some point abused substances. The development of the distinctive responses are defined and explained just after the definition of ‘attitude’. The Theoretical Framework The paper further interrogates the identification of a conceptual or theoretical framework, its appropriateness and whether this has been adequately described. In view of these factors the work shows admirable consistency in detailing objectives and sticking to the initial idea of examining the levels of patient discrimination among various healthcare trustees. To this end, the theoretical framework drawn by the researchers is clearly helpful, especially regarding the selection of articles within the plot. Aims of Study Adherence For a well written research paper, the literature review must be consistent with the overall objectives of the study. The study objectives and the hypothesis should be clearly stated. The work does not draw a clear hypothesis for the study. Indeed, the study fails to mention its hypothesis, and the reader is left to grapple for the same from the explanations provided in the background and the discussion section. Giving the work this look, it is then possible to conclude that the objective of the study is finally arrived at. The Sample and Representation The target population should be clearly identified. The criteria of selection in this case is that the interviewee: i) Is a student at Monash University ii) Is in their first, second, or third years of study iii) Is pursuing a course in any of the fields: emergency health, nursing, midwifery, occupational therapy, physiotherapy or health science. This inclusion criterion therefore implies that only students undertaking courses leading to a career in the medical field participated in the survey. This was obviously a plus for the group. This identification laid the basis for obtaining information from relevant sources. Being ongoing students it is expected that they have had some level of interaction with persons seeking medical attention owing to various medical complications. The experience can rightly be assumed to grow as the level of study progresses. This places the third year students at the top of the staircase in comparison with their fellow interviewees. It is relevant to the study that the researchers sought to establish whether there were any significant differences among the mean perceptions of the different levels of study. This would enable them to know whether there was an observable trend in the attitude levels. Probability sampling is regarded as the best way to acquire an unbiased sample for a survey. In view of the study under review, even after focusing attention on interaction the medical students, it was still possible to draw a random sample from the test population. The method used to select the 548 participants (unfortunately records only account for 429) is not explained. Furthermore, the mathematical basis for selecting this group of interviewees from a possible 1296 is not explained. The physiotherapy is grossly affected by failure to record any data for substance abuse and acute mental illness categories. Ethical Considerations This section evaluates whether the researchers acquired all required permissions from the university administration to undertake the survey, and whether the participants were made fully aware of the requirements of the survey. It further seeks to establish whether confidentiality of the survey participants was guaranteed. The Monash University Standing Committee on Ethics in research Involving Humans (SCERH) is the university body mandated to grant permission for the study to be undertaken. This gives credence to the research as one conducted legitimately. A statement by the researchers states that the interviewees received an explanatory statement outlining why the study was being carried out. The identities of researchers were kept anonymous and the survey was free. Only demographic details of the respondents were noted. Operational Definitions This is the section that evaluates the clarity of the definitions of the terms and concepts mentioned in the study. The core terms used in the study, including attitude and its two sides (positive and negative) and the Medical Condition Regard Scale (MCRS) and its development as a tool for measuring attitude are clearly explained. Other jargons defined to clarity in this work include Elaboration Likelihood Model (ELM), HSM and the EPPM. Methodology This section refers to whether the research design identification was clear, whether the research design used was appropriate for the intended results and whether validity and reliability tests were carried out and their results discussed. It also seeks to establish whether a pilot study was carried out before the actual study. For this research, the MCRS questionnaire was administered to the participants. SPSS was used to get the descriptive statistics and to carry out both t-tests and ANOVA. Statistically, this part account for the biggest failure of this study. For this reason a deeper understanding of data types is explained below. Descriptions of Parametric and Non-parametric Tests Discussed Parametric Tests These tests are normally carried out where a justified assumption regarding the distribution of the measurement variable(s) has been established prior to the test. Analysis of Variance: This is the most commonly used model of analysis, as it is relatively easy to grasp for varied categories of learners. Analysis of Variance or ANOVA as it is commonly referred to is a test that examines whether the factors in a multi-factor model are significant. ANOVA assumes two forms; the one-way ANOVA models and the two-way ANOVA models. The one factor model is a generalization of the two-sample t-test, which tests the hypothesis that two population means are not significantly unequal. In essence, the one factor ANOVA tests the hypothesis that ‘k’ population means are equal (NIST 2003; Smith 2012). The Kruskal-Wallis test is the befitting non-parametric replacement for the one factor ANOVA test. ANOVA finds its ground from the clarification that the data in question does not fail the normality test. Thus whenever a one factor data set fails this test the next obvious thing is to apply the Kruskal-Wallis test. T-tests The t tests are used to compare two sets of data in order to reach the conclusion whether the data sets are significantly different (Dayton n.d.). Basically, t tests are used to establish a probability that the sampled populations are similar in the sense of the value being tested (Caprette n.d). These tests are conducted to establish relationships in data sets at various levels for instance: i) Paired data As the name implies, the data are presented in matching pairs, with a suspected relationship being assumed to exist as a link between the two pairs, and for each joint pair. In this case the second or newer set of data is the result of treatment of the first set of data. According to the article Students t-tests, for this type of data, the paired samples t-test is carried out (n.d. par3). ii) Independent samples In this case the data are collected independently of each other. For example a researcher may want to know whether the average height of a randomly picked man in the streets of Riyadh is the same as that of a man randomly selected from the streets of Tripoli. Non-parametric Tests These tests are normally carried out where a justified assumption regarding the distribution of the measurement variable(s) can not be established prior to the test. Kruskal-Wallis Test: McDonald notes that this test is commonly used when there is one nominal variable and one measurement variable (2009, pp165; Pauling 1989; Plitcha 2012). In this case, the measurement variable does not meet the normality assumption requisite for undertaking an ANOVA test on the data (StatsDirect 2011; Han et al 2011). In the case that data are not normally distributed, the one way ANOVA will be susceptible to yielding inaccurate p-values. This test relies on the ranks assigned to the data set, where the least rank, 1, is assigned to the smallest value in the data set. The test basically draws its weakest attribute on the basis that it loses its power owing to the interchange of information from original to derived data. For understanding, the Kruskal-Wallis test is compulsory whenever we want to determine hypothesis on a data set that has one nominal variable and one ranked variable. The requisites for conducting a Kruskal-Wallis test on a data set are:  The data points must be independent from each other; the distributions do not have to be normal and the variances do not have to be equal; you should ideally have more than five data points per sample; all individuals must be selected at random from the population; all individuals must have equal chance of being selected; sample sizes should be as equal as possible but some differences are allowed (Gaten par1; EFSA 2011; Nishiumi et al 2012). Mann-Whitney U-test: The Mann-Whitney U-test is the non-parametric equivalent of the parametric t-test. The test applies when the nominal variable has got only two values. Despite the fact that they use different test statistics (H and U) for the Kruskal-Wallis and the Mann-Whitney U-test respectively, the p-value in both cases is arithmetically identical/ similar. These tests (Kruskal-Wallis and the Mann-Whitney U-test) revolve around the median, unlike most parametric tests that resonate around such parameters as the mean. Discussion: Review of the Data and Tests Used by Boyle et al: To ascertain the correctness of the tests applied, we shall take a few samples of the data they have presented in tables and use it to determine whether the data really obeys the normality assumption so as to warrant the parametric tests done on it. From each of these categories one of the simplest tests of normal distribution of data is performed, drawing the histogram. To avoid superfluous representation, the representatives among each category have been kept to the levels of studies. Having failed this test collectively it is clearly unlikely that the researchers’ work would find the basis for justification of the normality assumption made prior to investigations into their stated hypothesis. Observation From the appendices, the data collectively fail the normality test. For this reason we conclude that the ANOVA tests carried out on the data were undeserved, because the data failed the mandatory normality test for carrying out ANOVA studies on it. As earlier noted, whenever the normality assumption fails, we take on the Kruskal-Wallis test, and it was supposed to have been used by this group of researchers. This finding represents an instance of statistics abuse in analysis. Having established that the data the researchers used was non-parametric, it is again a fault that they used parametric t-tests. Again, reading from Table 2, the researchers converted single digit Likert scale into two digits, another statistical error. Trochim reaffirms Stevens definition of scaling as “the assignment of objects to numbers according to a rule.” Conclusion From the above discussions, it is apparent that Boyle et al used the inaccurate p-values, which is an unfortunate observation for the study. This stems from the fact that this group of researchers ignored the test for normality that is a requirement before ANOVA tests are carried out on the data. The group also made use of t-tests, again on a set of non-parametric data. Instead they should have made use of Mann-Whitney U-test, which replaces the t-test when the data in question are not exactly parametric. These findings put to question the authenticity of the conclusions drawn by the researchers. It is unclear whether prior tests were done on the data to classify it accordingly. Indeed, a review of their work in line with these non-parametric recommendations would be the most justified response to compensate for the misleading findings. The p-values appear forced to confirm the tests carried out, otherwise the level of the test should not have been varied. These findings contradict the leading literature through which the researchers show admirable prowess and mastery of the field they were researching. To sum up the findings, the leading section of the study carries the correct intentions and the writers score highly. However, the methodology and the data analysis sections are a clear flop, which totally limits the power and usefulness of the findings. In other words, this is the section that waters down the rest of the work. Bibliography Caprette, D. R. (n .d.). Experimental Biosciences: Resources for Introductory and Intermediate Level Laboratory Courses. Students T Test (For Independent Sample). Web 23rd December 2012. Dayton University. (n. d.). Using SPSS for t-tests. Web 22nd December 2012. EFSA Journal 2011. Scientific Report of EFSA: Statistical Analysis of Temporal and Spatial Trends of Zoonotic Agents in Animals and Food. p.25 Gaten, Ted. (2000). On-line Statistics. Kruskal-Wallis Nonparametric ANOVA. Gilchrist, G., Moskalewicz, J., Slezakova, S., Okruhlica, L., Torrens, M., Vajd, R. & Baldacchino, A. (2011). Addiction Research Report. Staff Regard towards Working with Substance Users: A European Multi-centre Study. pp 1114-1126 Han, X. et al. (2011). Metabolomics in Early Alzheimer’s Disease: Identification of Altered Plasma Sphingolipidome Using Shotgun Lipidomics. Lipidomics in Alzheimer’s Disease. Vol 6. Issue 7. p. 7. McDonald, J. H. (2009). Handbook of Biological Statistics. Kruskal-Wallis test and Mann-Whitney U-test. Web. < http://udel.edu/~mcdonald/statkruskalwallis.html> McDonald, J. H. (2009). A Handbook of Biological Statistics. Wilcoxon Signed-Rank Test. (2nd Edition) < http://udel.edu/~mcdonald/statsignedrank.html> Nishiumi, S. et al. (2012). A Novel Serum Metabolomics-Based Diagnostic Approach for Colorectal Cancer. Metabolomics for Colorectal Cancer. Vol. 7, issue 7. p. 4. NIST: Statistical Engineering Division. (2003). Data plot: Kruskal Wallis. Pauling, L. (1989). Biostatistical Analysis of mortality Data for cohorts of Cancer Patients: Hardin Jones principle/ Kaplan-Meier renormalization. Vol.86. pp 86 Plichta, S. B., Kelvin, E. Munros Statistical Methods for Health Care Research. Statistical Methods for Health Care Research. (6th edition). (2012). Smith, G. L. et al. (2012). Association Between Treatment With Brachytherapy vs Whole-Breast Irradiation and Subsequent Mastectomy, Complications, and Survival Among Older Women With Invasive Breast Cancer. The Journal of the American Medical Association. par 17. StatsDirect Limited. (2011). Kruskal-Wallis Test. Students T-tests. (n. d.). Web 22nd December 2012. Trochim, W. M. K. (2006). Research Methods Knowledge Base. General Issues in Scaling. Web. Read More