Last Week We Discussed Research Techniques This Week Do

In Last Week We Discussed Research Techniques This Week Discussion Fo

In last week we discussed research techniques, this week discussion focuses on the other aspects of research, please Discuss the following into detail with applicable in text citations and a minimum of three credible references per question. 1) Different types of bias in research and discuss. 2) Experimental and Non-Experimental Research 3) Distinguishing between a Sample and a Population and then compare at least two sampling methods (e.g. random sampling vs. stratified random sampling or convenience and quota sampling methods? 4) What specific steps would you have taken to obtain a representative sample?

Paper For Above instruction

Understanding Bias, Research Types, and Sampling Techniques in Research Methodology

Research is a fundamental aspect of advancing knowledge across disciplines, and understanding various components such as bias, types of research, sampling methods, and strategies for obtaining representative samples is essential for conducting valid and reliable studies. This paper discusses the different types of bias in research, differentiates between experimental and non-experimental research, clarifies the distinction between a sample and a population while comparing two sampling methods, and outlines the steps required to achieve a representative sample.

1. Different Types of Bias in Research and Their Implications

Bias in research refers to systematic errors that can distort the findings, leading to inaccurate conclusions. Various types of bias can threaten the validity of studies, and recognizing these biases is crucial for designing rigorous research. Selection bias occurs when the process of selecting participants leads to a non-representative sample, affecting the generalizability of the results (Shadish, Cook, & Campbell, 2002). Measurement bias, also called information bias, arises when there is systematic error in the data collection process, such as faulty instruments or biased interviewers (Charmaz, 2014). Confirmation bias occurs when researchers favor data that confirms their preconceived hypotheses, potentially skewing interpretation (Nickerson, 1998). Publication bias involves the preferential publication of positive or significant findings, which can distort the scientific literature and meta-analyses (Dwan et al., 2008). Recognizing and mitigating bias through methodological rigor, blinding, and proper sampling procedures enhances the validity of research outcomes (Cohen & Swerdlick, 2010).

2. Experimental and Non-Experimental Research

Research can be categorized into experimental and non-experimental designs based on the control over variables and the study's aims. Experimental research involves manipulating an independent variable to observe its effect on the dependent variable, often with random assignment to control and experimental groups (Shermer, 2002). This approach allows for establishing causality and is commonly used in clinical trials and laboratory settings. Conversely, non-experimental research does not involve manipulation but instead observes and analyzes variables as they naturally occur (Creswell & Creswell, 2018). Methods such as surveys, observational studies, and correlational research fall under this category. While experimental research provides stronger evidence for causal relationships, non-experimental studies are valuable for exploring phenomena where manipulation is impractical or unethical (Cook, 2011). Both types contribute uniquely to the body of knowledge, and the choice depends on research questions, ethics, and feasibility.

3. Distinguishing Between a Sample and a Population and Comparing Sampling Methods

A population in research refers to the entire group of individuals or instances with characteristics relevant to a study, whereas a sample is a subset of the population selected for analysis (Lohr, 2019). Correctly sampling from the population ensures that findings are generalizable. Two common sampling methods are random sampling and stratified random sampling. Random sampling involves selecting participants solely based on chance, ensuring each member has an equal probability of inclusion, which minimizes selection bias (Levy & Ellis, 2018). Stratified random sampling enhances representativeness by dividing the population into homogeneous strata (e.g., age, gender) and then randomly sampling from each stratum proportional to their occurrence, improving precision in estimates (Kish, 1965). While simple random sampling is straightforward and efficient, stratified sampling ensures that minority groups are adequately represented, reducing sampling error and increasing accuracy in parameter estimates.

4. Steps to Obtain a Representative Sample

To ensure that a sample accurately reflects the population, several systematic steps should be followed. First, clearly define the target population and establish inclusion and exclusion criteria (Etikan & Bala, 2017). Second, choose an appropriate sampling method aligned with the research goals—stratified random sampling often provides better representation in heterogeneous populations. Third, determine the required sample size through power analysis, considering factors such as effect size, confidence level, and variability (Cohen, 1988). Fourth, implement the sampling process meticulously, ensuring that the selection method is free from bias and error. Fifth, monitor the sampling process and verify the demographic and characteristic distribution of the sample against the population parameters, making adjustments if necessary. Finally, document every step thoroughly to enhance replicability and transparency (Marshall, 1996). Consistent and careful implementation of these steps helps attain a sample that closely mirrors the characteristics of the population, thereby increasing the study's external validity.

References

• Charmaz, K. (2014). Constructing grounded theory. Sage.
• Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Routledge.
• Cohen, L., & Swerdlick, R. (2010). Research methods in education. Routledge.
• Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications.
• Dwan, K., et al. (2008). Systematic review of the empirical evidence of study publication bias and outcome reporting bias. PLOS ONE, 3(8), e3081.
• Etikan, I., & Bala, K. (2017). Sampling and sampling methods. Biometrics & Biostatistics International Journal, 5(6), 00149.
• Kish, L. (1965). Survey Sampling. John Wiley & Sons.
• Levy, P. S., & Ellis, N. R. (2018). Introduction to survey sampling. Sage publications.
• Lohr, S. L. (2019). Sampling: Design and Analysis. Chapman and Hall/CRC.
• Nickerson, R. S. (1998). Confirmation bias: A ubiquitous phenomenon in many guises. Review of General Psychology, 2(2), 175-220.
• Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Houghton Mifflin.
• Shermer, M. (2002). Why people believe strange things: Pseudoscience, superstition, and other confusions of our time. Holt Paperbacks.