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The goal of the study discussed in this post was to analyze risk factors of hypertension in menopausal women in Rejomulyo, Madiun- a small town in the western part of Indonesia (Ardiani et al. 2015). An observational analysis was performed with cross-sectional study design (Ardiani et al. 2015). 
The author used strategies to pose control in the research plan as follows. The study used a target population of women aging from 50-80 in the small town of Rejomulyo. (Ardiani et al. 2015).  All of these women had not yet experienced menopause for at least 12 months and had no surgery due.  Furthermore, women who had smoking habits, consumed alcohol or had preexisting heart disease, kidney, thyroid and diabetes mellitus were eliminated (Ardiani et al. 2015). A proportional stratified random sampling method was used to gather data. According to Polit & Beck (2021), a stratified random sampling leads to improved coverage of the population in which the data is being collected thus ensuring the researchers have more control over each subgroup which leads to better representation in the sample.
Identified risks of internal and external validity were noted.  A potential threat to internal validity could be the lack of a set control group in the study.  All 90 respondents to the study were used to collect data. None of the women were NOT analyzed and designated the “control group” on the basis of providing a comparison between known risk factors versus unknown risk factors as they directly relate to hypertension in menopausal women. The data collection consisted of measuring blood pressure, weight, height, waist circumference and a Perceived Stress Scale (PSS) questionnaire (Ardiani et al. 2015).  In addition, an interviewer was selected to perform the PSS questionnaire. The article does not disclose the credentials of this interviewer thereby leading the reader to question the reliability and credentials of the interviewer. According to Steckler & McLeroy (2008) a key to internal validity is solid measurement and study design with reliable data collection instrumentation. A potential threat to external validity could be the lack in variation amongst the group of participants. All women were from a very small town in Indonesia – so small, the population could not be found listed online.  Furthermore,  this town already had a statistically higher percentage of hypertension amongst women. This could greatly constrict the variation in people, conditions and setting. 
A proportional stratified random sampling method was used in this study.
The population of women in Rejomulyo, Madiun are divided into a subpopulation that differs in that they range in age from 50-80 years old. Scientifically, women of this age are at a higher risk of already going through menopause or in the early stages of menopause. The women in this study had not experience menopause for at least 12 months and had no surgery due (Ardiani et al. 2015).  This allows the study to draw a more precise conclusion ensuring that the subgroup is properly represented.  Random sampling was then conducted from this subgroup. 
References:
Ardiani, H., Saraswati, L. D., & Susanto, H. S. (2015, August 1). Risk factors of hypertension in … – Universitas Indonesia. Retrieved October 20, 2021, from https://scholarhub.ui.ac.id/cgi/viewcontent.cgi?article=1113&context=mjhr (Links to an external site.).
Polit, D. F., & Beck, C. T. (2021). Nursing research: Generating and assessing evidence for nursing practice. Wolters Kluwer.
Steckler, A., & McLeroy, K. R. (2008, January). The importance of external validity. American journal of public health. Retrieved October 20, 2021, from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2156062/.
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Amjad et al. (2014) investigated the correlation between both experiences with personal illness and end-of-life care of others with advance care planning (ACP) in older adults. The study design included some methods to impose control. During data collection, the study participants chose from a predetermined set of responses to specific questions about ACP behaviors that evaluated the stages of change for each behavior. Similarly, the participants answered specific questions with yes-or-no responses about their experiences with their own illnesses, their experiences making medical decisions for others, and if they had experienced ‘types’ of deaths of others. This reduced the likelihood that this data was subject researcher subjectivity as they attempted to classify unique responses in a way that allowed for statistical analysis (Polit & Beck, 2021).
Amjad et al. (2014) also imposed control via the preliminary inclusion and exclusion criteria of their study participants. Potential participants were selected from primary care practices and a senior center. The potential participants from the primary care practices were selected based on only two criteria – age of at least 60 and without a dementia diagnosis. The senior center simply recruited volunteers who were interested in a study. From both groups, all potential participants were screened for exclusion criteria (non-English speaker, hearing loss that would interfere with interview participation, current nursing home resident, acute illness, and short-term memory impairment). The inclusion and exclusion criteria both did not include any items related to either illness/death exposure or ACP behaviors. These criteria helped to control for sample imbalances (Polit & Beck, 2021).
However, the sample was still subject to self-selection bias as the participants volunteered to participate either after receipt of a letter or in-person solicitation. However, there is a mitigating factor to this internal validity threat. The majority of the participants were recruited from the primary care practices as opposed to the senior center (258 vs 46 persons, respectively). There was a high rate of response to the initial recruitment letter from the primary care practices (92% and 88% from each practice). This represents a low refusal rate, which decreases the likelihood of volunteer bias (Center for Evidence-Based Medicine, n.d.). There was no information provided on how many persons from the senior center were solicited, only the total number of volunteers – this group of study participants may have had a higher refusal rate. The convenience sampling of the senior center participants represents a risk to external validity (Polit & Beck, 2021).
The biggest risk to internal validity was the lack of any attempt to control for potential confounding variables (Amjat et al., 2014) . Sociodemographic data about the participants was collected but only to provide a descriptive univariate statistical analysis of the study sample. These same sociodemographic data points could have been used to analyze differences in the types of responses based on different groups represented within the sample to assess for the impact of these confounding variables. However, this analysis would be challenging. Age, for example, could be a factor that increases the likelihood of ACP behaviors, but it also increases the likelihood that someone has had a personal illness experience or end-of-life care experience. However, additional statistical analysis within sociodemographic groups would have likely illuminated future areas of investigation.
The uncontrolled confounding variables also threaten the statistical conclusion validity as failing to control for confounding variables reduces the precision of the study (Polit & Beck, 2021). The external validity of the study is threatened by the representativeness of the study sample. The sociodemographic data provided indicated that the sample was mostly female and they were all volunteer participants (Amjad et al., 2014). This may negatively affect how generalizable the results are to the older adult population as a whole (Polit & Beck, 2021).
The recruitment of study participants was detailed above. Amjad et al.’s (2014) nonprobability sample was selected without actions that would enhance representativeness. The participants from both the primary care practices and the senior center were functionally convenience (and self-selected) samples. However, the sampling from the primary care centers that recruited participants who simply were over the age of 60 and did not have a dementia diagnosis seems to be stronger and more suggestive of representativeness than the in-person solicitation of volunteers in attendance at the senior center. A strength of the sampling plan was the preliminary inclusion and exclusion criteria for the letter mailed from the primary care practices, as discussed earlier.
References
Amjad, H., Towle, V., & Fried, T. (2014). Association of experience with illness and end-of-life care with advance care planning in older adults. Journal of the American Geriatrics Society, 62(7), 1304–1309. https://doi.org/10.1111/jgs.12894
Center for Evidence-Based Medicine. (n.d.). Volunteer bias. Catalog of Bias. Retrieved October 20, 2021, from https://catalogofbias.org/biases/volunteer-bias/
Polit, D. F., & Beck, C. T. (2021). Nursing research: Generating and assessing evidence for nursing practice. Wolters Kluwer.
Edited by Liza Baker on Oct 20 at 4:09am
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Fendrich et al. (2021) studied the retention rates and substance use for participants enrolled in a federally funded medication assisted treatment (MAT) program.  Various methods were utilized to impose control in the study.  All sites uses to select participants were considered enhanced programs which have stringent guidelines based on treatment pathways and included specific roles of practitioners within the program.  The rigidity of these programs help to control confounding variables that could influence retention rates.  In addition, all participants were interviewed shortly after engaging in the program using the same standardized tool to assess their baseline population.  From there, statistical control was used to assess confounding variables. Polit & Beck (2021) argue the best variable is the outcome variable and should be measured before independent variables are considered, leaving demographic variables to be statistically controlled.  Fendrich et al. (2021) used the Chi square statistic to assess for demographic variation and logistical regression models to analyze baseline differences as related to retention rates.
Fendrich et al. (2021) does discuss the inclusion of cognitive behavioral therapy at these programs as a possible threat to internal validity.  Because the use of this therapy was not directly analyzed, the therapy itself cannot be eliminated as a contributing factor to the results.  In addition, abstinence was self-reported by the participants and not verified with a urine drug screen, which the authors note is an high risk to validity due to the nature of high risk populations and self-reporting (Fendrich et al., 2021).  External validity is also discussed as a potential limitation due to the lack of generalizability of the study as it relates to different programs and locations.
Convenience sampling was used to select participants for the study. Volunteers from four sites in Connecticut were consented for the study.  A strength of the sampling plan is that the study design looked specifically at retention rates for MAT programs and the subjects obviously had to be enrolled and defined by the study.  Conversely, a limitation of the sample group is that they were enrolled in very specific MAT programs that included specific therapies that not all MAT programs employ.  Therefore, the sample population is not generalizable among all MAT programs because the structure of the programs vary amongst sites.
References
Fendrich, M., Becker, J., Ives, M., Rodis, E., & Marín, M. (2021). Treatment retention in opioid dependent clients receiving medication-assisted treatment: Six-month rate and baseline correlates. Substance use & Misuse, 56(7), 1018-1023. https://doi.org/10.1080/10826084.2021.1906276 (Links to an external site.)
Polit, D. F., & Beck, C. T. (2021). Nursing research: Generating and assessing evidence for nursing practice. Wolters Kluwer.
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