Evaluating Tools and Samples Used in Research Studies
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Evaluating Tools and Samples Used in Research Studies
Christopher Colon
Grand Canyon University
Health Care Research Methods, Analysis and Utilization
Gail Tucker
26
th
September 2023
2
Evaluating Tools and Samples Used in Research Studies
Healthcare management requires evidence-based decision-making for patient care and
efficiency. This paper discusses healthcare management research, including elements such as
surveys, interviews, pre- and post-tests, pilot projects, power analysis, sample size,
generalizability, reliability, and validity. Effective research requires careful assessment tool
selection, pilot projects to refine design, and statistical and methodological attention to robust
findings. These elements must be connected to generate meaningful insights that affect
healthcare organizations, practitioners, and patient outcomes. To achieve research goals,
interviews, surveys, and pre- and post-tests assess interventions and changes. Pilot projects
prepare final studies, improve data collection tools, identify issues, and optimize study protocols
for more reliable results. Healthcare management research requires statistical power and sample
size to detect meaningful effects or associations and improve generalizability to the population of
interest. Insufficient power can cause inconclusive or misleading results, affecting decision-
making. Results that inform evidence-based practice and policy decisions must be reliable and
valid.
Types of Assessment Tools
Various assessment tools are used in healthcare management research to collect data and
gain insights into healthcare systems, processes, and outcomes. Surveys, interviews, and pre- and
post-tests are some of the main assessment tools used in healthcare management research.
Surveys are versatile and efficient, making them popular in healthcare management research.
Scalability, ease of administration, and the ability to collect standardized data from a large and
diverse pool of respondents are their benefits. Many healthcare management researchers use
surveys to measure patient satisfaction, staff perceptions of organizational culture, and policy
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implementation. Researchers use surveys to quantify and analyze responses, which informs
decision-making. Surveys have drawbacks. Response bias, where participants give socially
desirable answers instead of honest ones, is a drawback (Bernardi & Nash, 2022). Survey design
also requires careful question phrasing and response format to reduce ambiguity and ensure data
validity. Interviews can provide deeper qualitative insights than surveys.
Data collection in healthcare management research is dynamic with interviews. They
allow researchers to dig deeper into complex topics by eliciting detailed responses. Interviews
help investigate sensitive topics or gain a deeper understanding of someone's perspective.
Healthcare professionals, patients, and administrators may be interviewed for healthcare
management research to understand their experiences, challenges, and decision-making.
However, interviews have drawbacks. Interviews, transcription, and analysis of qualitative data
take time. Additionally, the interviewer's presence and interaction style may bias participants'
responses. Interviews have a smaller sample size than surveys, which may limit generalizability.
Pre- and post-tests help researchers evaluate healthcare management interventions and
changes. These assessment tools measure outcomes before and after an intervention to determine
its impact. Pre- and post-tests can quantify change and prove causality, which is important when
evaluating healthcare management interventions like quality improvement or training (Shields et
al., 2016). Pre- and post-tests have limitations. They work best for short-term changes, not long-
term successes. Confounding variables can also affect results, making it hard to attribute changes
to the intervention. Researchers must carefully design studies to control these variables.
Healthcare management research uses surveys to assess patient satisfaction with hospital
services, healthcare staff attitudes toward EHR implementation, and patient medication
adherence.
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Pilot Project Results
Healthcare management research relies on pilot projects to prepare for the final study.
Pilot projects in healthcare management research are small-scale, preliminary studies before the
main research project. To ensure the research design, methodology, and data collection tools are
effective and reliable in the larger study, it tests and refines them. Pilot projects help researchers
refine their approach and improve study quality and rigor by identifying and addressing potential
issues.
Piloting a project requires several steps. Prior to the pilot study, researchers must
establish clear research objectives and hypotheses (Hazzi & Maldaon, 2015). They should then
select a representative sample from the target population and administer the main study's
research instruments (surveys, interviews). In this phase, researchers should carefully document
any challenges, such as ambiguous survey questions or participant recruitment issues.
Researchers examine the pilot project's data quality, response rates, and unexpected findings and
patterns after data collection. This analysis helps determine data collection tool effectiveness and
informs final study changes. Pilot projects provide valuable insights for improving the final
study. The pilot can help researchers improve survey questions and interview protocols for data
collection. Adjustments to the recruitment strategy can boost participant engagement and
representativeness. Pilot project results can also guide final study sample size and statistical
power. To detect significant effects, researchers may need to increase the sample size if the pilot
shows unexpected variability in responses. This ensures the final study has enough power to
draw conclusions.
During the pilot project, survey questions used to measure patient satisfaction with
telemedicine services are unclear. Some patients are confused about certain items, resulting in
inconsistent responses. The researchers also find demographic underrepresentation in patient
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recruitment. By analyzing pilot project results, the research team gains insights. They simplify
survey questions to make them more understandable. They adapt their patient recruitment
strategy to ensure a more diverse and representative sample. The pilot project's adjustments
improve the final study's precision and confidence. Effective research instruments, proper sample
size, and study quality improve, resulting in more reliable findings.
Power, Sample Size, and Generalizability
Statistical power, sample size, and generalizability greatly affect healthcare management
research quality and applicability. Healthcare organizations need these factors for evidence-based
decision-making and policymaking. The ability of a researcher to detect meaningful effects or
differences depends on statistical power in healthcare management research. High statistical
power reduces Type II errors, where researchers miss important effects (Matuschek et al., 2017).
Low statistical power can lead to harmful decisions in healthcare management, where lack of
evidence can affect patient outcomes and resource allocation. The right sample size is crucial in
healthcare management research. Researchers must consider statistical significance, effect size,
and target population variability. Larger sample sizes increase statistical power but also data
collection costs. Thus, researchers must balance statistical power and feasibility based on their
goals and resources.
Healthcare management research emphasizes practicality. A study on the effects of a new
healthcare management software system on patient data security may have trouble coordinating
data collection across departments. Practicalities can affect sample size and study feasibility.
Researchers must maneuver these complexities without compromising their design. Healthcare
management research must consider generalizability, or how well findings can be applied to
other populations or settings. The field has diverse healthcare settings, patient populations, and
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practices, making generalizations difficult. Researchers must carefully define their target
population and use sampling methods that maximize results applicability. Stratified sampling
improves generalizability. A study evaluating a healthcare management intervention to reduce
readmission rates may stratify its sample by patient demographics (age, gender) or hospital
characteristics (teaching vs. non-teaching hospitals). This ensures subgroup representation and
increases findings' relevance to healthcare contexts.
Reliability and Validity in the Scientific Method
Reliability and validity are crucial to healthcare management research. These two
elements underpin research integrity, ensuring credible, accurate, and meaningful data and
conclusions. Reliability ensures measurement tool consistency and stability. Consider a
healthcare management scenario where multiple observers evaluate hospital patient satisfaction.
These observers' evaluations are consistent due to inter-rater reliability. This is crucial because it
ensures that other observers would reach similar conclusions about the same patients.
Additionally, test-retest reliability maintains measurement stability. Healthcare management
research relies on this for data reliability. If a survey measures healthcare workers' job
satisfaction, test-retest reliability checks whether their scores remain consistent over two
administrations.
However, validity involves accurately measuring what you intend to measure. It's the
assurance that a measurement tool or instrument measures the intended concept. Content validity
ensures that measurement tool items or questions accurately represent the phenomenon under
study in healthcare management research. Consider a patient satisfaction survey. Content validity
would ensure that survey questions cover all important patient experience aspects. In healthcare
management, researchers often study abstract concepts like healthcare quality, organisational
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culture, and patient trust, making construct validity important. Construct validity is assessed
using statistical methods like factor analysis to ensure that the instrument accurately measures
the theoretical construct.
Conclusion
This paper examines the complexity of healthcare management research and the factors
that make findings credible and impactful. To solve complex problems and improve patient
outcomes, healthcare management research is crucial. Surveys, interviews, and pre- and post-
tests are used for data collection, perspective analysis, and intervention evaluation in the paper.
To improve research methods and rigor, pilot projects are discussed. The paper emphasizes
statistical power and sample size in evidence-based healthcare management research. Due to
diverse healthcare settings, patient populations, and management practices, generalizability is
stressed. To make research more applicable, stratified sampling and cross-validation are
recommended. Finally, reliability and validity are crucial to data consistency, accuracy, and
applicability. The paper concludes that these elements are interconnected and essential in
healthcare management research, ensuring a holistic process with rigorous and significant
results. Quality healthcare management research shapes policy, best practices, and provider and
patient outcomes.
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References
Bernardi, R. A., & Nash, J. (2022). The importance and efficacy of controlling for social
desirability response bias.
Ethics & Behavior
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Hazzi, O., & Maldaon, I. (2015). A pilot study: Vital methodological issues.
Business: Theory
and Practice
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(1), 53-62.
Matuschek, H., Kliegl, R., Vasishth, S., Baayen, H., & Bates, D. (2017). Balancing Type I error
and power in linear mixed models.
Journal of Memory and Language
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94
, 305-315.
Shields, L., & Smyth, W. (2016). Common quantitative.
Nursing and Midwifery Research:
Methods and Appraisal for Evidence Based Practice
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143
, 11-28.