Eliminating Bias in Quantitative Research, Threats to Validity Eliminating Bias in Quantitative Research, Threats to Validity What are some ways a researcher can eliminate bias from subjects or participants in quantitative research? This solution reviews some of the common types of bias and threats to validity in quantitative research such as history, maturation, regression, selection, mortality, diffusion of treatment, testing, and instrumentation. It also discusses how to avoid these biases. ? BrainMass Inc. brainmass.com March 22, 2019, 12:04 am ad1c9bdddf https://brainmass.com/psychology/abnormal-psychology/eliminating-bias-quantitative-research-486871 ORDER INSTRUCTIONS-COMPLIANT NURSING PAPERS Solution Preview When we refer to bias in quantitative research studies, we are often referring to threats to the internal validity of a study. Internal validity is the degree to which the results are accurate and the producedures of the experiment support the ability to draw correct assumptions or inferences about the results. So in order to eliminate bias for participants, we must first understand what types of bias can occur. Potential Bias/Threats to Validity and Ways to Mitigate Them History ? If an experiment/study occurs over a longer period of time, participants may be exposed to different events or experiences that may influence them beyond the conditions of the experiment. For example, if you were conducting an experiment during 9/11, that event may change participants beliefs and attitudes and bias your end results. To prevent this type of bias, it is helpful for the researcher to use both an experimental and control group that experience the same events. This may be achieved by selecting groups in the same organization or community. Maturation ? As a study is being conducted, the participants may mature or change during that time, again skewing the results. For example, if you were conducting a longer-term study of students over the course of a school year or several years it is likely that they will mature and change their attitudes and beliefs as a natural growth process. So, how can you show that the results of your study are due to the treatment or situation you are researching versus just the natural growth process? This is best managed by selecting participants who are the same age and would mature at the same pace throughout the experiment. Regression ? This bias occurs when researchers select participants that have extreme scores. For example, if we studied people with high anxiety and low anxiety scores only, it is natural that their scores will change over the course of the study because we are only looking at the extremes. As researchers we want ? Eliminating Bias in Quantitative Research, Threats to Validity Order Now

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Eliminating Bias in Quantitative Research, Threats to Validity

Introduction

Quantitative research is often seen as an objective process, but bias can affect the validity of results. From a researcher’s perspective, there are two broad categories of threat to validity: internal and external. Internal validity refers to whether or not the research design adequately addresses the research questions being investigated. External validity refers to whether or not the results that a researcher obtains can be generalized to other individuals and settings (e.g., in different organizations).

Threats to validity can be divided into two broad categories, internal and external.

Internal validity refers to the ability to generalize the results of a study to other individuals and settings. External validity refers to the ability for an intervention or treatment program to be used in other settings.

Internal threats include confounding variables, selection bias, and lack of randomization. External threats include Hawthorne effect (participants may act differently because they know they’re being watched), experimenter expectancy effects (participants may act differently because they expect that something will happen), regression dilution bias (invalidation due to increased variability due to randomization), researcher manipulation/reporting bias (a researcher who wants better test results than those obtained before him manipulates them by selecting more extreme cases).

Internal validity refers to whether or not the research design adequately addresses the research questions.

Internal validity is the extent to which research findings can be attributed to the independent variable. It is concerned with whether or not the research design adequately addresses the research questions.

For example, if you are studying how a certain drug affects pregnancy outcomes, you may want to measure both pregnancy outcomes and how well participants’ pregnancies went. If your study has enough participants for each group (i.e., before-and-after), then this would be an internal validity issue because it’s possible that other factors could have influenced both groups’ pregnancies (for example, it could be that women chose different treatments). In this case, researchers need more data before they can draw any conclusions about whether or not their treatment worked better than another one did—and this requires external validity in order for them to do so

External validity refers to whether or not the results that a researcher obtains can be generalized to other individuals and settings.

External validity is the extent to which the results of a study can be generalized to other individuals, settings and times. You’re probably thinking: “This is just another way of saying that it’s not really important if one person or group can be identified as having experienced something (like racism) when in fact they didn’t! Who cares? It doesn’t matter because there will always be some sort of bias towards this kind of thing! There’s no way for us humans ever stop being biased against each other; therefore we should never try.”

Well…that’s true…but here’s why external validity matters: If you want your research findings to apply outside the context in which they were conducted then you need access to more people than just those within your own lab or from similar backgrounds (i.e., white males). For example, let’s say that over half feel like their gender does not fit into society due their appearance or lifestyle choices; however only half actually feel comfortable sharing these feelings with friends/family members who might judge them based on these differences. If we only looked at those who felt comfortable speaking up about their experiences then our results would likely only represent those who did share them openly without considering other factors such as age range etcetera which may have played bigger roles during interviews but weren’t easily measured due lack thereof

Quantitative research is subject to many different types of bias that can affect the validity of the results obtained.

The most common types of bias that can affect the validity of quantitative research are internal and external validity.

Internal validity refers to whether the design of your study adequately addresses your research questions, while external validity refers to how generalizable those results are. To understand this concept better, let’s look at an example: Say you’re conducting a study on “how much time it takes people to get ready in the morning.” You might get very different results if you had conducted it with college students in New York City vs high school students in rural Kansas; as a result, both studies would be considered flawed because they didn’t follow similar methods (e.g., using one set of participants for each location). However, if we were interested in exploring whether or not there was any difference between these two groups’ attitudes toward makeup application before school began each day—whereas one group typically had less than five minutes per day at home before going off to class—then we could consider our findings valid because they would apply universally regardless where they took place (e.g., wherever someone lives).

Conclusion

We hope that this article has given you a better understanding of the different types of bias in quantitative research, and how to recognize and avoid them. As we stated earlier, there are many ways to address these threats; however, if you’re planning on writing or publishing any kind of quantitative study, it is important that your methods are solid and reliable so as not to present readers with invalid results.