Topic_3_Multivariate_Approaches_(Obj._3.1_3.3_3.4_3.5_and_3.6)
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Multivariate Approaches
Monica D Piper
Grand Canyon University
CNL 540- Research Methods and Program Evaluation
Dr. K
July 19, 2023
Multivariate Approaches
The study "Telebehavioral Health (TBH) Use Among Rural Medicaid Beneficiaries:
Relationships with Telehealth Policies" focuses, examines and looks directly into the interactions
and/or connection between telebehavioral health, Medicaid, rural health, and telehealth policy.
This research study, like all other research studies, includes a predetermined and established
control group consisting of 70,459 rural fee-for-service (FFS) beneficiaries with behavioral
health needs, and it also includes important and significant considerations such as their consisting
of their own unique and /or specific health needs, beneficiary characteristics, use of
telebehavioral health, and accessibility to mental health professionals. For the purpose of
ensuring accuracy, validity and reliability, this study makes use of multivariate models, resource
files, surveys, documents provided by websites hosting users of telebehavioral health, and other
secure data sources. As is customary in many case studies, internal validity faced some particular
difficulties, and the multivariate models employed in this study offered both benefits and
drawbacks.
Key Variables
In order to ensure or guarantee accurate, trustworthy , reliable and precise information
results, key variables require being present throughout all studies. As stated in Basic Research
Concepts (2018), "Researchers carefully analyze and interpret the value(s) of each variable to
make sense of how things relate to one another in a descriptive study or what has occurred in an
experiment." .Geographical region, healthcare recipients, and users of outpatient mental health
services are only a few of the factors that are taken into account and/or considered by the study's
research. 70,459 rural FFS Medicaid patients who got outpatient behavioral health care and
resided in one of the 36 Medicaid-operated states that funded telehealth in 2011 made up the
study's sample. This data was provided by Talbot et al. (2020). Furthermore, they did not meet
the requirements and/or were denied for both Medicaid and Medicare. These critical elements are
significant because they will have a direct impact on the data that will be collected for this study.
The participants' geographic location is crucial, for instance, as telebehavioral health is more
popular among those who live in rural areas and places with a shortage of mental health
professionals.
Validity and Reliability
These figures suggest and/or demonstrate that there is considerable validity to this report.
The study employs general estimation equations to "examine how odds of TBH use were related
to informed consent, facility fees, and the interaction between these variables after adjusting for
covariates" (Talbot et al., 2020) and to further analyze the collected data. TBH was applied rather
uncommonly (2.1%), with the highest utilization occurring "among beneficiaries with severe
mental illness (3.2%), and those living in rural counties (2.6%) or in areas where there is a
shortage of mental health professionals (2.2%)" (Talbot et al., 2020). Accuracy, validity,
trustworthiness, and credibility are made possible by the study's usage of crucial design elements
with its experiments, including dependent and independent variables, preparation and post
testing, and both control and experimental groups.
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Threats to Internal Validity
Internal validity and consistency is problematic or a concern with regard to any
experimental case study. Hazards still have a potential of happening, despite how hard specialists
work to prevent them. It's possible that this research will lose some subjects. Inaccurate data will
be produced as a result of participants leaving the study for a variety of reasons, such as moving
or deciding they do not want to participate. History may be at danger of undermining internal
legitimacy. On the other hand, certain unforeseen circumstances might take place between the
start and finish of the research, which could result in tainting the conclusions. It is important for
researchers to be aware of internal risks to their work and the potential damage they might do to
its validity and reputation.
Strengths and Limitations of Multivariate Models
Different multivariate models are used to investigate the behaviors of multiple random
variables. In order to analyze the numerous data acquired and further evaluate the interaction's
nature, contrast analysis is used in this study. This model claims that "informed consent and TBH
use varied depending on whether users lived in states where facility fees were paid" (Talbot et al.,
2020). The model's strength is its capability for further analysis of the gathered data. On the other
hand, there were limitations. Since the study began, fewer state Medicaid programs have been
operating in the FFS setting, which may make data scarce and alter data collection. The
behavioral model used in the study shed light on another drawback of a multivariate model.
Conclusion
Experimental research allows for the storage of large volumes of data, advancing
professional understanding. The information collected for this study included statistical
comparisons between "state Medicaid telehealth policies and TBH use among rural
fee-for-service (FFS) beneficiaries with behavioral health needs and assessed relationships
between beneficiary characteristics and TBH use" (Talbot et al., 2020). Due to the employment
of multivariate models and control groups, the data appears legitimate and dependable, which is
essential in experimental research.
References
Basic Research Concepts. Elements of research : Variables. (2018). Retrieved May 31, 2023,
from
https://ori.hhs.gov/education/products/sdsu/variables.htm
.
Talbot, J. A., Jonk, Y. C., Burgess, A. R., Thayer, D., Ziller, E., Paluso, N., & Coburn, A. F.
(2020). Telebehavioral health (TBH) use among rural medicaid beneficiaries:
Relationships with telehealth policies. Journal of Rural Mental Health, 44(4), 217–231.
https://doi-org.lopes.idm.oclc.org/10.1037/rmh0000160
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