What are the 3 major issues of empirical studies?

Empirical studies are the foundations of scientific research. They give a valuable understanding of the real world via gathering and analysing the data. It includes a research approach; empirical research has several issues and challenges. When researching Google scholar research proposal topics, you can utilise these complexities as a starting point to identify areas where empirical research is required. All of which may affect the reliability and validity of research outcomes. These complexities are measurement error, selection bias and causation versus correlation. 

Selection Bias

Selection bias is a substantial problem in empirical studies when the sample employed for analysis does not represent the broader phenomenon or population being researched. This bias may lead to inaccurate or skewed research findings from systematic variation between the groups or individuals included in the research and those excluded (TRG, 2023). Considering selection bias is important for researchers as it may compromise the generalisability of their findings and external validity. By including Write My Dissertation Proposal in your search, students may explore guidance and resources on making a research proposal and choose an appropriate topic when addressing the key issues in empirical research.  

Sampling Bias:

Convenience sampling: Researchers might choose participants who are easy to reach or readily available, such as surveying students on university campuses. This may lead to a sample that only shows some of the population. 

Non-response Bias:

In the survey, if particular groups are less expected to respond, their viewpoint can be understated in the information, potentially twisting the findings.

Healthy volunteer effect:

Individuals in medical research who volunteer for health studies or clinical trials are usually more health-conscious or healthier than the general population, which may lead to biased health results. 

Seasonal Bias: 

Research conducted during particular times or seasons may yield findings different from the year.

Addressing Selection Bias:

To mitigate the selection bias and enhance the generalisability and validity of the empirical study, researchers may use the Ten Commandments for motivating language learners:

  • Random sampling:

Using the random sampling method assures all group members equal opportunity to be involved in the research, decreasing the possibility of selection bias.

  • Stratified Sampling

Researchers may separate the population into strata (sub-groups) and then randomly select from all strata. This assures that all subgroups are appropriately represented in the sample.

  • Weighting: 

Using weights to observe based on their probability of selection may facilitate correcting for bias in non-random samples.

  • Propensity Score Matching:

Researchers in observational research may utilise statistical approaches such as propensity score matching to balance covariates between teams, effectively decreasing selection bias.

  • Transparent Reporting:

Documenting the sampling approaches, potential sources, and response rates of bias in the study enables viewers to evaluate the generalizability and quality of the research.

  • Sensitivity Analysis: 

Researchers may conduct sensitivity analysis to evaluate how different selection bias cases might influence the research conclusions.

  • Measurement Error:

Measurement error is a problem in empirical research that may significantly influence the reliability and accuracy of research outcomes (Mintzberg, 2019). The information gathered during the research does not accurately demonstrate the true worth of the assessed variables. This discrepancy between true and measured values may increase from different sources and cause imprecise and biased results. Addressing measurement errors is important to maintain the reliability and validity of the empirical study. There are some approaches to mitigate its influence:

  1. Standardisation: standardise measurement process to reduce observer bias. Training and clear protocols for data collectors may help overcome variability (Singer and Alexander, 2017).
  2. Instrument Validation: Assure that measurement tools are properly reliable, validated and calibrated. Quality control and regular maintenance checks are crucial (Guest, Namey and Chen, 2020).
  3. Convenience Sampling: Convenience sampling method in survey research may facilitate to overcome sampling error and enhance the sample’s representativeness.

Sources of Measurement Error:

Measurement error may increase from different sources, so researchers must be attentive to these sources to deal with them effectively:

Sensor malfunction: Sensor malfunction or technical issues may form errors in data gathering, specifically in subjects like engineering and science.

Instrumental calibration: 

Errors in the calibration, including scales or thermometers, may lead to inappropriate measurements.

Causation versus Correlation:

Causation versus correlation refers to the important notion in empirical study and statistics. Featuring between correlation and causation is fundamental for making significant conclusions from data and evading common issues. 

Correlation: It represents the statistical association between more than two variables where modifications in one variable are related to changes in another variable. It demonstrates whether there is a consistent way of change or co-occurrence in the values of such variables.

Cautions and Common Issues:

Reverse causation: Mistakenly observing the way of causality. The association is likely contradictory to what one originally thought.

Omitted variable bias: Failing to deal with relevant variables that might clarify the observed correlation may lead to inappropriate causal conclusions.

Third-Variable Confounding: When an unmeasured third variable affects the presumed effect and cause, it may generate a false occurrence of causation between them.

Mitigating the issue between correlation and causation is imperative in empirical study to assure meaningful and accurate outcomes. There are some of the approaches for mitigating the error and issues. 

Use Experimental Design:

Manipulation of variables:

Manipulate in an experimental setting the presumed cause and emphasise its influence on the effect. Assure that the manipulation is self-determining of other variables.

Randomised controlled trails:

Conduct controlled experiments in which you manipulate one variable when keeping another variable constant. RCTs focus on the gold values for making causation as they include random subjects’ work to control and treatment groups.

There are two Conceptual and empirical research examples used to mitigate the issue:

Example of conceptual research:

In psychology, conceptual research might form a theoretical model to understand emotional intelligence based on expert opinion and current literature without conducting empirical tests.

Example of empirical research:

Empirical study in environmental science may include collecting information to evaluate the influence of deforestation on biodiversity, utilising statistical analysis and field surveys to identify its impacts on species diversity, and notifying conservation policies and efforts.

 

References

Guest, G., Namey, E., & Chen, M. (2020). A simple method to assess and report thematic saturation in qualitative research. PloS one, 15(5), e0232076.

Mintzberg, H. (2019). Managerial work: Analysis from observation. In Managerial Work (pp. 99-112). Routledge.

Singer, L. M., & Alexander, P. A. (2017). Reading on paper and digitally: What the past decades of empirical research reveal. Review of educational research, 87(6), 1007-1041.

TRG (2023). How About a Fresh Start for Your PhD Dissertation Writing? Available at: https://www.theresearchguardian.com/how-about-a-fresh-start-for-your-phd-dissertation-writing/

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