The Process of Selecting and Locating an Instrument
A researcher should consider three factors when choosing an instrument for research: validity, reliability, and comparability with data from other studies (Bastos, Duquia, González-Chica, Mesa, & Bonamigo, 2014). The validity of an instrument is its ability to create results measuring what it is intended to measure. Internal validity means that the results of a measurement reflect the properties of the used sample; external validity means that these results can be correctly generalized to the population from which the sample is drawn (Bastos et al., 2014).
Reliability of an instrument is its ability to “consistently generate the same results after being repeatedly applied to the same group of subjects”; the notion of reliability also includes the aspect of temporal consistency (Bastos et al., 2014, p. 919). Finally, comparability means that the results gained by using an instrument in research can be compared to those of existing studies, and vice versa (Bastos et al., 2014). For instance, it is easy to compare the results of temperature measurements made in the Fahrenheit scale with those made in the Celsius scale, and vice versa.
To locate an existing instrument, a scholar should formulate a research question identifying the property (variable) to be measured, and identify how specifically the variable should be measured (i.e., on what scale) (DePoy & Gitlin, 2013). Next, it is recommended to search scientific literature (e.g., journal articles, books) to find a proper instrument. The researcher should consider the reviews of the instrument in the literature, its assessed validity, reliability, and comparability. If no appropriate instrument can be located, the researcher can review the research question to agree on it with the existing instruments, develop their instrument (which is a tedious, difficult and time-consuming process), or wait until a proper instrument is developed by others (Bastos et al., 2014).
The research question is: “In patients aged 30-60 with diabetic neuropathy, how effective is PEMF in reducing pain originating from this condition when compared to analgesics, tricyclic antidepressants, and anti-seizures medication?” Consequently, a measurement instrument to be selected should assess the levels of pain resulting from diabetic neuropathy in patients. Pain should be measured on an interval/ratio scale because its levels should be assessed.
The instrument to be used in the study is the S-LANSS (Self-Completed Leeds Assessment of Neuropathic Symptoms and Signs pain scale), which is a modification of LANSS scale that allows the questionnaire to be completed by the patient (“The S-LANSS pain score,” n.d.; Toth & Moulin, 2013, pp. 14, 24-25). S-LANSS is valid and reliable (Eckeli, Teixeira, & Gouvêa, 2016); the validity and reliability of the original LANSS scale have been validated in several studies, including cross-cultural studies (Barbosa, Bennett, Verissimo, & Carvalho, 2013; Spanos, Lachanas, Chan, Bargiota, & Giannoukas, 2015). The scale is one of the most used instruments for assessing neuropathic pain nowadays (Eckeli et al., 2016; Toth & Moulin, 2013; van Hecke, Austin, Khan, Smith, & Torrance, 2014), and is, therefore, comparable with other studies.
The level of measurement of the tool is ratio; it uses an 11-point ratio scale, where 0 indicates no pain, and 11 indicates pain “as severe as it could be” (“The S-LANSS pain score,” n.d.). Also, the instrument allows for identifying whether the pain is neuropathic in origins; a patient can get a score from 0 to 24, where the values of ≥12 indicate neuropathic pain (“The S-LANSS pain score,” n.d.); thus, it is, in fact, a yes (neuropathic if score ≥ 12) / no (non-neuropathic if score < 12) scale.
The data collection procedures will include an initial assessment of pain using the S-LANSS instrument (questionnaires will be completed by patients; those with non-neuropathic pain will be excluded), and a post-treatment assessment. The data will be collected and analyzed using statistical tools, possibly ANCOVA–to check whether the type of treatment can predict the post-treatment levels of pain using the pre-treatment levels of pain as the covariate (Field, 2013).
More specifically, the level of pre-treatment neuropathic pain in each patient will be assessed using the S-LANSS scale; the results will be recorded, creating a variable which will be used as the covariate in the subsequent ANCOVA. The patients will be assigned to treatment groups (e.g., 1 = PEMF therapy, 2 = analgesics, tricyclic antidepressants, and anti-seizures medication therapy) according to their preferences and physicians’ recommendations; the groups variable will be used as the independent variable for the ANCOVA. After the course of treatment is finished, the levels of patients’ neuropathic pain will be assessed once again, creating a variable which will later be used as the dependent variable.
The ANCOVA will be carried out using the alpha level of.05, and the power of.80; the test will be aimed at detecting an effect of medium size (f =.25). This will require the sample size of N = 158 (“Sample Size: ANCOVA,” n.d.). The alpha level chosen for the test is the commonly accepted standard (Field, 2013); similarly, it is often recommended to have the power of at least.80 in a study (Warner, 2013). As for the effect size, the analysis will be aimed at detecting a medium effect because detecting a small effect (f =.10) requires too many resources (sample size N = 947) (“Sample Size: ANCOVA,” n.d.), and a small effect will probably not make much difference between the two types of therapy.
Barbosa, M., Bennett, M. I., Verissimo, R., & Carvalho, D. (2013). Cross-cultural psychometric assessment of the Leeds Assessment of Neuropathic Symptoms and Signs (LANSS) pain scale in the Portuguese population. Pain Practice, 14(7), 620-624. Web.
Bastos, J. L., Duquia, R. P., González-Chica, D. A., Mesa, J. M., & Bonamigo, R. R. (2014). Field work I: Selecting the instrument for data collection. Anais Brasileiros de Dermatologia, 89(6), 918-923. Web.
DePoy, E., & Gitlin, L.N. (2013). Introduction to research: Understanding and applying multiple strategies. St. Louis, MO: Elsevier Health Sciences.
Eckeli, F. D., Teixeira, R. A., & Gouvêa, A. L. (2016). Neuropathic pain evaluation tools. Revista do Instituto de Medicina Tropical de São Paulo (Journal of the São Paulo Institute of Tropical Medicine), 2016(17), S20-S22. Web.
Field, A. (2013). Discovering statistics using IBM SPSS Statistics (4th ed.). Thousand Oaks, CA: SAGE Publications.
Sample size: ANCOVA. (n.d.). Web.
Spanos, K., Lachanas, V. A., Chan, P., Bargiota, A., & Giannoukas, A. D. (2015). Validation of the Leeds Assessment of Neuropathic Symptoms and Signs (LANSS) questionnaire and its correlation with visual analog pain scales in Greek population. Journal of Diabetes and its Complications, 29(8), 1142-1145. Web.
The S-LANSS pain score. (n.d.). Web.
Toth, C., & Moulin, D. E. (Eds.). (2013). Neuropathic pain: Causes, management and understanding. Cambridge, UK: Cambridge University Press.
Van Hecke, O., Austin, S. K., Khan, R. A., Smith, B. H., & Torrance, N. (2014). Neuropathic pain in the general population: A systematic review of epidemiological studies. Pain, 155, 645-662. Web.
Warner, R. M. (2013). Applied statistics: From bivariate through multivariate techniques (2nd ed.). Thousand Oaks, CA: SAGE Publications.