Assignment: Eliminating Bias in Quantitative Research Assignment: Eliminating Bias in Quantitative Research 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 YOUR PROFESSIONAL PAPER HERE 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 ? Assignment: Eliminating Bias in Quantitative Research Order Now

ADDITIONAL INFORMATION 

Eliminating Bias in Quantitative Research

Introduction

Bias is a problem in any research project. This article will discuss eight common sources of bias, ways to reduce them, and how you can use this methodology to eliminate bias in your own research.

Re-reading your research

Let’s talk about re-reading. If you’re like me, reading your research paper can be difficult because you want to get through it quickly and move onto other things. But it’s important to take the time to read over each section of your paper so that you don’t miss any important details or leave out important information that could lead to bias in your final conclusions.

Here are some tips for how to do this:

• Re-read the research questions and make sure they align with what you currently know about the topic; if not, then maybe rethink them or create new ones instead of relying on old ones

• Re-read every piece of data analysis (including tables) and check for errors such as incorrectly counted responses/references/etc., wrong units used throughout entire report (e gg vs mm), etc.; if there are any issues here then fix them immediately!

Avoiding problematic question phrasing

• Avoid using gender-specific pronouns.

• Avoid leading questions.

• Avoid wordy questions.

• Don’t ask about sensitive topics like race and gender, or personal beliefs that could be perceived as discriminatory, such as religion or sexual orientation.

Diversifying sample pools

Diversity in the sample pool is important, and it can be achieved through a variety of methods. For example, if you’re studying a certain topic or issue, you could create a panel that includes people from different backgrounds: men and women; ethnicities (white versus non-white); age groups; sexual orientation (gay vs straight); socioeconomic status; etc. The purpose of diversifying your sample pool is to ensure that you are getting an accurate representation of how your research will affect those who aren’t currently participating in it.

Another way to diversify your sample pool would be through using multiple sources as well as different types of sources such as government data sets or surveys done by private organizations like Google or Amazon Alexa Voice Accessory System (AVAS). You could also include memberships at organizations where they meet regularly such as churches or sports teams which may lead them down different paths than others who don’t attend these events regularly but still want access just because they enjoy being around friends/family members associated with those groups!

Takeaway:

Takeaway:

The takeaway should be something that you can remember easily and that will help you. It should also be clear and concise so that it can be easily applied to your own research projects.

Importance of eliminating bias

Eliminating bias is important to improve the quality of research and avoid mistakes, embarrassment and wasting time, money, effort, or energy.

Challenges to eliminating bias

Bias is a human thing. It is everywhere, it’s hard to avoid, and it’s hard to detect. If you’re looking for bias in your research then you might as well give up now because there will be so many other things that catch your eye before biases do—and those things may not always be what they seem either!

It can be difficult for researchers to see their own biases when they are conducting quantitative research on data sets collected from individuals or groups (e.g., students). However, the more familiar we become with our own personal perspectives on issues such as race or gender equality within society at large through interacting with others with similar backgrounds; the easier it becomes for us know ourselves better as humans by comparing ourselves against others who hold different beliefs than ours do about certain topics such as these ones mentioned above (i) “Race” vs “Racial”.

Conclusion

The good news is that you can reduce bias in your research by using the right tools and keeping things in perspective. The bad news is that bias is still a very real problem, and it’s going to take some time before it disappears entirely. If you want to make sure your biases don’t ruin the integrity of your study, then follow these tips:

• Practice mindfulness and try not to take things personally when reading or listening to text-based materials (such as journal articles). This will help you avoid getting emotionally invested in what other people have written about their experiences – which could lead them into making biased assumptions about yours!

• Be aware of problematic question phrasing like “Why did this happen?” or “What could have been done differently?”. These types words often require more explanation than necessary from both parties involved; therefore they yield less useful information compared with more specific questions such as “How did this happen?” or even “What if we tried something else?”.

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