With respect to the McGuckin studies neither randomization nor sample selection is ever discussed. In fact sampling per se is not presented except for cursory mention in the results section. Again, without proper identification as to the sampling method implemented, the reader is ever cautious as to how legitimate the results will be. Not wanting to pre-empt the discussion on statistical methodology, mention must be made at this time with respect to the Poisson Regression statistical tool selected for use in these two studies in terms of sampling. This particular regression technique, if utilized properly, requires the sample size to be determined on the basis of the square root transformation of the Poisson random variables. More specifically, the formula for calculating the sample size of the Poisson variables is as follows:
2) 2
The data received from this calculation will give the research investigator the number of sampling units per group that are needed. Unfortunately however the reader is not presented information with respect to the Poisson random variables selected not the unit sample number per group.
Statistical Tool and Selected Variables. Both studies made use of the Poisson regression technique to ascertain their results. When a statistical tool is chosen the tool must conform to the theoretical requirements of the selected process. According to the authors the Poisson regression method was used to test the significance of the intervention. Actually, researchers to test for the interaction of selected variables use a Poisson model and not to test for differences between and amongst a study's selected independent and dependent variables. In essence the Poisson regression model permits predictability of the results. At no time during either research project did the authors state, or allude to, the need to establish the predictability of the Partners in Your Care assessment instrument. The appropriate conclusion drawn, therefore, is that a more user-friendly statistical tool should have been employed - a modified orthogonal analysis of variances wherein nested variables are also considered.
As mentioned earlier, all research investigations must clearly define the variables under investigation. In descriptive research studies this involves the identification of the independent and dependent variables. Although both studies have independent (treatment) and dependent (change) variables they are never clearly identifies. Identification must take place prior to the establishment of the selected statistical tool. Further, when discussing or presenting the independent and dependent variables the research investigator is obligated to inform the reader as to whether or not differences, relationships or effects are being sought. It is not until the data analysis section is the reader advised that the study is seeking to determine intervention effects of the Partners in Your Care model. Again, the fact that the authors make use of the work 'intervention' is indicative of determining effects - which the Poisson regression model is not equipped to handle. Had the research investigators wanted to seek the predictability of the aforementioned model then consideration for using the Poisson model can be established. Furthermore, as the variance of the Poisson random variable is equal to the mean of the response recorded (i.e., response to the Partners in Your Care assessment instrument) and must be reported by the investigators as the regression model is set up to meet normality and variance homogeneity assumptions. Again, no such presentation was made in either study.
Selected Measurement Instruments. When employing any type of measurement instrument the instrument must be evaluated in terms of reliability and validity (Stanley & Hopkins, 1972). Reliability comes from the necessity for dependability in measurement. If the data is not dependable, any conclusions drawn will lack soundness. In order to achieve reliability the research investigator must maintain the integrity of the testing by ensuring that the same results from a testing instrument will deliver the same set of results when tested by a comparable instrument (Ohlson, 1998). As there exists no reported studies confirming the reliability of the Partners in Your Care instrument, the acceptance of the results is suspect. A measurement instrument that has not been properly serviced or calibrated will yield biased or erroneous data and faulty conclusions will be drawn. The error commonly associated with the measurement instrument is commonly called systematic variance or error (random) variance. In reference to the two studies conducted by McGuckin, et al. there is no reference to how the assessment instrument achieved the necessary required reliability. The reader is simply expected to accept the measurement instrument as a reliable indicator of the variables being examined.
In terms of instrument validity the authors are first obligated to define the variables under investigation followed by a presentation of the measurement instruments validity. The types of measurement validity all...
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