How do you ensure your research population is representative?

Defining the population precisely is crucial in research. It forms the basis for selecting a representative sample and estimating the margin of error. With a clear population definition, you can calculate the required sample size for more accurate results. By specifying criteria like age, gender, location, or behavior, you can identify suitable sources and methods to reach your target audience effectively. This reduces the margin of error and enhances the study's validity and applicability. A well-defined population ensures more informed conclusions and data-driven decisions, increasing the research's overall quality and impact.

Ensuring that your research population is representative is crucial for obtaining accurate and results. A representative sample helps minimize biases and allows you to make broader conclusions about the larger population. Also it is important to choose an appropriate sampling method. There are several methods, including random sampling, stratified sampling, cluster sampling, and more. The choice of method depends on the characteristics of your population and the research goals.

PART 2 Step 1: The researcher identifies the Sampling Frame using the Snowball sampling techniques. Snowball sampling is a non-probability sampling technique where existing study subjects recruit future subjects from among their acquaintances (contacts). Step 2: The researcher calculates the sample size from the identified sampling frame. An online sample calculator available at surveysystem.com is used for this purpose. The researcher can use 95 percent confidence level and 5 percent confidence interval to calculate the sample.

Defining your population against your research goal and objectives is very important. It will help to identify the respondents easily and categorize them based on your research objectives.

The defined polulation becomes the setting within which your findings, conclusions and recommendations apply. If the population is well defined, there is a higher likelihood of success when recommendations from a study are implemented. Defining your population also helps in attracting funding as providers can easily determine if a proposed study falls within their strategic interest. Decision options: 1) Restrict a population to increase robustness, reliability and intensity of knowledge provided for a study. 2) Relax the defined population to have an extensive coverage and fundng options particularly in exploratory studies. The researcher should remember that the defined population had implications for subsequent sampling decisions.

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Although, non-probability sampling may introduce bias, it is very useful in cases where it is difficult to access a more representative sample through probability sampling. So, its significance cannot be denied. For example, snowball sampling is very effective in cases where you do not have many leads to identify your exact target sample. For example, if your population is a minority group and you have no idea who belongs to that group, you can use snowball sampling to identify your target audience for sample from a single or few leads.

When considering a sampling method, there are many factors that can impact the decision between which to use. An example of this for probability sampling methods is that it will likely require a larger sample size to collect representative data, as there will need to be sufficient representation to normalise individual effects. Non-probability sampling can introduce bias from the researcher, which can be mitigated if assumptions are challenged. This method also offers an opportunity to use other techniques to help create a more representative sample, such as selecting normalising factors that consider the research question and basing the sample population on outlying components from the normalisation factor.

I think it is hard to define the research population in scientific research. For example, Synthetic biology combines diverse research areas and majors of study such as molecular biology, cell biology, microbiology, oncology, enzymology, computational biology, biostatistics, etc.

In my opinion, the sampling method and the definition of bias are directly related to the research method, whether the research method is quantitative or qualitative? Bias is almost meaningless in qualitative research because a small number of samples that have the maximum characteristics and data required for the research are selected. Qualitative research does not claim to generalize, so there is no concern about bias (like what is intended by the quantitative approach).

This is based on the intention or the purpose of study. Only those elements will be selected from the population which suits the best for the purpose of our study. For Example: If we want to understand the thought process of the people who are interested in pursuing master’s degree then the selection criteria would be “Are you interested for Masters in..?” All the people who respond with a “No” will be excluded from our sample.

I will use my doctorate research as an example. The aim of my study is to check if the soft skills college students learn are being transferred into the workplace reality. I am adopting action research and purposive sampling. So, I choose participants representing identified key traits. This means including students from diverse majors (engineering, arts, business), academic years, ages, genders, and cultural backgrounds. Ensuring a truly representative research population.

Sample size determines the accuracy. Based on the test statistics, one must chose right sample size like in many instances n=30. randomisation reduces standard error. However, the 'n' depends on the N-the total population size. identifying the right test statistics will resolve this issue

Too much research has focused only on able-bodied, white, cisgender, straight, middle-class men. If the target population is ‘men between the ages of 18-30 with mental illness,’ do your best to recruit men from a variety of racial backgrounds, socioeconomic backgrounds, languages spoken, and more to get a more diverse sample. Remember that folks of color, especially Black folks in the US context, might be suspicious of participating in research because of the history of being tortured and mistreated in medical research. Offering payment for participation can make participating in research more appealing. Without these essential experiences, research will continue to ignore folks from marginalized backgrounds and may even lose funding.

When analyzing human populations, it's important to keep in mind that convenient recruitment methods (such as online surveys, phone calls, emails, & social media), may be biased by socioeconomic class and technological literacy. In addition, it's important to keep in mind that passive recruitment methods (e.g., posters and ads) and financial incentives may also introduce different types of biased based on the personal interest and economic need of potential study participants.

It is also important to remember that study participants are first and foremost human beings and should be always treated with respect. Often studies can include participants that speak various languages so it's important to have informed consent forms that are easy to understand and available in languages used in the area. It is also beneficial to have a health care worker/recruiter that can communicate in the participants chosen language.

Data analysis is a crucial part of any research finding. In case of urban planning and policy development , it aids in optimizing resource allocation along with budgeting and resource management.An effective data analysis should reveal trends, patterns, and correlations in the data . These insights can lead to new discoveries and inform future research directions. It further helps Governments and policymakers rely on data analysis to make informed decisions about public policies, regulations, and interventions.

Depending on the nature of the study, it can be helpful to recruit independent analysts to perform separate analyses of the same datasets in the interest of reproducibility and validation.

The ultimate goal of recruiting a diverse and balanced sample that “reflects the characteristics and distribution of your population” is excellent. Still, according to many studies, Census data and zip code boundaries often don’t match. There are other solutions; Cornell University researchers recently developed a new mathematical method to inject fairness into the frail political redistricting process. This unique two-step method is described in a research paper called “Fairmandering: A Column Generation Heuristic for Fairness Optimized Political Districting,” step one is creating billions of potential electoral maps for each state. It algorithmically identifies a range of possibilities meeting the desired criteria for fairness.

The last crucial step in ensuring a representative research population is evaluating the representativeness of your sample and results. Compare your sample to the population based on relevant variables and assess the degree of similarity or difference. Utilize measures of sampling error like confidence intervals to estimate result accuracy. Acknowledge limitations or bias, discussing the implications and generalizability of your findings.

Disease Relevance. One has to clearly state the genetic disease that is under study and why it's important. This helps in focusing on the relevant population. Inclusion and Exclusion Criteria. Most certainly specific criteria for including and excluding research participants from your sample is key. This could involve factors like age, genetic markers, medical history, and other relevant traits. Genetic Diversity. Ensure that your sample includes a DIVERSE range of genetic backgrounds to capture the genetic heterogeneity of the population affected by the disease. Family History. Definitely one has to consider including individuals with a family history of the disease, as they might provide valuable insights into genetic predisposition.

Most of the above mentioned aspects suit for hypotheses testing. If one wish to undertake exploratory enquires, the sample size is determined by saturation of ideas. When no new ideas emerge from the sample already interviewed, one may consider that the respective sample size is sufficient for the purposes of theory generation. Moreover, in many exploratory enquires that may take a grounded theory approach, one can mix samples and methods to come up with a grounded theoritical model to explain a chosen phenomenon.

Probability of occurrence of the issue, economics, non availability of the data, review of literature, purpose and such other parameters decides the usefulness of the research

Qualitative and quantitative studies discussed above are important to complement each other and very useful to draw conclusion.