When conducting a scientific study, you want to determine the effect one thing has on another.Since you can't study a whole population, you take a sample.You divide the sample into groups according to the research design.The thing you're trying to measure should not be different between those groups.There are other differences that can affect your results.The results of your study can't be applied to the larger population.Randomized controlled studies are the main way to reduce selection bias.In some studies such as social science studies, randomized controlled studies aren't feasible because of the cost.You can adjust your results if you can't do a randomized controlled study.
Step 1: Enroll study participants who are representative of your target population.
You'll apply the results of your study to your target population.The study participants should be drawn from that single population.If your study participants don't accurately reflect your target population, selection bias can occur.Suppose your target population is college students.You advertised for volunteers off-campus.Locals who don't attend the college may not have the same characteristics as your target population.To be able to apply the results of your study to the population at large, you must have enough participants in the study.Depending on a variety of factors, such as the magnitude of the effect you're studying and its variability within the population, the sample size will vary.An online calculator that can help you determine your sample size is available at www.clincalc.com.
Step 2: Study participants who meet your criteria will be selected.
You run the risk of volunteer bias if you don't recruit more volunteers.People willing to volunteer in a study have an interest in the outcome.They may not represent your target population because of their interest.You can create a questionnaire with inclusion and exclusion criteria.If you're studying the effects of sleep on college student grades, you might want to make sure you have a balance of early morning and night students.You could ask about the volunteer's class schedule.If you wanted to include full-time students, you would want to know how many course hours the volunteer was taking.Once you have more potential participants than you need, assign them each a random number.Pick your study participants based on those numbers.The reduction of selection bias is aided by this.
Step 3: Potential problems can be identified in a pilot study.
A basic run-through of at least the first part of the study is required in a pilot study.There are flaws in your study design and selection criteria.Before you do the full study, you have the chance to correct any flaws.The sample size doesn't have to be as large as it would be for the full study since it's not the real thing.Pilot studies give you an idea of how quickly you'll be able to recruit participants for your study, and which methods seem to work the best.
Step 4: To standardize study procedures, create an operations manual.
If other people in the study are using different methods to recruit participants or measure data, that can cause selection bias to slip through the cracks of your carefully designed study.You can be certain that another researcher could reproduce your study results if all study procedures are standard.The exact questions asked would be included in the operations manual.You could coach your investigators on their tone of voice and other factors that might affect their responses.If you have a lot of people involved in the study, make sure you train them on the methods you want them to use and test them against each other.If your study is going to take place over the course of months or years, it might be necessary to have "refresher" courses to keep investigators up to speed on your protocol, especially if they are away from the study for a while.
Step 5: Randomly assign participants to intervention or placebo groups.
Random numbers are used to identify study participants.Someone who isn't working on the study should be assigned the random numbers.You can split the participants between the two groups by assigning random numbers.Randomization is aided by research support units at most universities.Randomization can be done with computer programs.If you don't have access to research support, you can use a random number generator.Larger studies often use a remote randomization facility to make sure that no one involved in the study knows which group any given participant is in.
Step 6: Each participant's assignment should be double-blind.
The investigator and participant don't know which group the person is in in a double-blind study.Sometimes this process isn't possible or cost prohibitive.It would be impossible for your participants to not know if surgery was being performed on them if your study included surgery.In that case, your investigators could be blind as to a particular subject's group while taking their measurements, but the participant could not because they would have to consent to the surgical procedure.It might break down even if you have double-blinding in place.If you're studying a drug that has dangerous side effects, you might need to know which participants are taking it so you can warn them.
Step 7: Potential participants should collect basic demographic information.
In a case-control study, you have people who have been exposed to the same disease or condition as you, but you don't have any cases.Other factors that could potentially bias your result are eliminated when you choose participants with similar background and biographic data.If you're studying a population's likelihood of contracting a disease after exposure to the virus that causes it, you would want a sample that was similar in age, socio-economic status, and access to healthcare.There is a chance that some participants' outcome was affected by their health or medical treatment.
Step 8: The controls should be used the same way as your cases.
Pick your cases first in a case-control study.To enroll controls in your study, follow the same process.This makes sure you have an accurate measure of exposure in the population you want to study.If your case population comes from patients referred to a particular hospital for treatment, you might want to look for your controls from the healthcare providers who made those referrals.
Step 9: Control from hospital populations is not a good idea.
If your cases are hospitalized, it's okay.The association between exposure and the disease will be weakened if your controls are hospitalized.If you're doing a study on smoking and chronic heart disease, having hospitalized controls would weaken the association because smoking is a factor that leads to many health problems that could also result in hospitalization.
Step 10: Match controls with cases based on the same demographic.
When choosing controls for your case-control study, include any factors that might affect the results.You can use demographic information from your cases as a profile for your controls.You don't know which restaurant is responsible for the viral outbreak.The locals who contracted the virus are your cases.To identify which restaurant is responsible, you could enroll people from the local area who matched your cases in terms of neighborhood, age, and gender, but didn't contract the virus as your controls.
Step 11: Population data can be used instead of recruiting participants.
People who don't have the disease or condition you're studying will be less likely to participate in a case-control study.If you have population information available from a national, regional, or local database, you can use it to solve the problem.The cost of your study can be decreased by using data from a publicly accessible database.For your control, choose a dataset that matches the population of the cases you're studying.If all of your cases are located in California, you might use a state database to get your population data.You wouldn't use a national database.
Step 12: The selection bias variable should be included in your analysis.
If there are variables that could potentially cause selection bias, record that information from each of your participants.In addition to your overall analysis, analyze your results based specifically on that variable.You are studying the connection between coffee and headaches.The postal surveys were sent to households in California.Older people are more interested in participating in postal surveys than younger people, so this could affect your study.To adjust for bias in the study of the coffee and migraines connection, you could separate your data so that it measured the connection in different age groups separately.The selection bias would be reduced by having too many older people in the sample.
Step 13: Weight participants are asked to correct a biased sample.
The results from the underrepresented group should be more valuable than the results of the other group if your participants don't match the demographic of your target population.You can apply your results to the entire population by adjusting your sample.You are studying the effect of sleep on college students.The student population at the school is made up of 40% male and 40% female.Your sample is 20% male.To weight the male responses, divide the population percentage by the sample percentage.Each male's response counts twice.
Step 14: Discuss the possibility of selection bias in your report.
Simply acknowledge that selection bias exists if there is no effective way to adjust your results to reduce it.Give a description of how you tried to correct the bias or why it wasn't possible given the circumstances of the study.For example, if you wanted to evaluate the association between working the night shift and having a health problem, you could compare people who work at the same factory at different times of the day and night.You can't account for the differences between these groups, such as their socio-economic status or access to health care.There are a lot of other differences that your study didn't take into account, so acknowledge them in the report.References to other studies that have analyzed those variables in depth could also be included.