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W.B.C.S. Main 2018 Question Answer – Psychology – Research Variables
1)What are research variables?Discusss their different types and explain the concept of confounding.How can confounding of research be prevented?
Research
As a researcher, you’re going to perform an experiment. I’m kind of hungry right now, so let’s say your experiment will examine four people’s ability to throw a ball when they haven’t eaten for a specific period of time – 6, 12, 18 and 24 hours.Continue Reading W.B.C.S. Main 2018 Question Answer – Psychology – Research Variables..
We can say that in your experiment, you are going to do something and then see what happens to other things. But, that sentence isn’t very scientific. So, we’re going to learn some new words to replace the unscientific ones, so we can provide a scientific explanation for what you’re going to do in your experiment.
The starting point here is to identify what a variable is. A variable is defined as anything that has a quantity or quality that varies. Your experiment’s variables are not eating and throwing a ball.
Now, let’s science up that earlier statement. ‘You are going to manipulate a variable to see what happens to another variable.’ It still isn’t quite right because we’re using the blandest term for variable, and we didn’t differentiate between the variables. Let’s take a look at some other terms that will help us make this statement more scientific and specific.
Dependent and Independent Variables
A moment ago, we discussed the two variables in our experiment – hunger and throwing a ball. But, they are both better defined by the terms ‘dependent’ or ‘independent’ variable.
The dependent variable is the variable a researcher is interested in. The changes to the dependent variable are what the researcher is trying to measure with all their fancy techniques. In our example, your dependent variable is the person’s ability to throw a ball. We’re trying to measure the change in ball throwing as influenced by hunger.
An independent variable is a variable believed to affect the dependent variable. This is the variable that you, the researcher, will manipulate to see if it makes the dependent variable change. In our example of hungry people throwing a ball, our independent variable is how long it’s been since they’ve eaten.
What is Confounding?
Confounding is a distortion (inaccuracy) in the estimated measure of association that occurs when the primary exposure of interest is mixed up with some other factor that is associated with the outcome. In the diagram below, the primary goal is to ascertain the strength of association between physical inactivity and heart disease. Age is a confounding factor because it is associated with the exposure (meaning that older people are more likely to be inactive), and it is also associated with the outcome (because older people are at greater risk of developing heart disease).
In order for confounding to occur, the extraneous factor must be associated with both the primary exposure of interest and the disease outcome of interest. For example, subjects who are physically active may drink more fluids (e.g., water and sports drinks) than inactive people, but drinking more fluid has no effect on the risk of heart disease, so fluid intake is not a confounding factor here.
Statistical Analysis to eliminate confounding effects
Unlike selection or information bias, confounding is one type of bias that can be, adjusted after data gathering, using statistical models. To control for confounding in the analyses, investigators should measure the confounders in the study. Researchers usually do this by collecting data on all known, previously identified confounders. There are mostly two options to dealing with confounders in analysis stage; Stratification and Multivariate methods.
1. Stratification
Objective of stratification is to fix the level of the confounders and produce groups within which the confounder does not vary. Then evaluate the exposure-outcome association within each stratum of the confounder. So within each stratum, the confounder cannot confound because it does not vary across the exposure-outcome.
After stratification, Mantel-Haenszel (M-H) estimator can be employed to provide an adjusted result according to strata. If there is difference between Crude result and adjusted result (produced from strata) confounding is likely. But in the case that Crude result dose not differ from the adjusted result, then confounding is unlikely.
2. Multivariate Models
Stratified analysis works best in the way that there are not a lot of strata and if only 1 or 2 confounders have to be controlled. If the number of potential confounders or the level of their grouping is large, multivariate analysis offers the only solution.
Multivariate models can handle large numbers of covariates (and also confounders) simultaneously. For example in a study that aimed to measure the relation between body mass index and Dyspepsia, one could control for other covariates like as age, sex, smoking, alcohol, ethnicity, etc in the same model.
1. Logistic Regression
Logistic regression is a mathematical process that produces results that can be interpreted as an odds ratio, and it is easy to use by any statistical package. The special thing about logistic regression is that it can control for numerous confounders (if there is a large enough sample size). Thus logistic regression is a mathematical model that can give an odds ratio which is controlled for multiple confounders. This odds ratio is known as the adjusted odds ratio, because its value has been adjusted for the other covariates (including confounders).
2. Linear Regression
The linear regression analysis is another statistical model that can be used to examine the association between multiple covariates and a numeric outcome. This model can be employed as a multiple linear regression to see through confounding and isolate the relationship of interest (5). For example, in a research seeking for relationship between LDL cholesterol level and age, the multiple linear regression lets you answer the question, How does LDL level vary with age, after accounting for blood sugar and lipid (as the confounding factors)? In multiple linear regression (as mentioned for logistic regression), investigators can include many covariates at one time. The process of accounting for covariates is also called adjustment (similar to logistic regression model) and comparing the results of simple and multiple linear regressions can clarify that how much the confounders in the model distort the relationship between exposure and outcome.
3. Analysis of Covariance
The Analysis of Covariance (ANCOVA) is a type of Analysis of Variance (ANOVA) that is used to control for potential confounding variables. ANCOVA is a statistical linear model with a continuous outcome variable (quantitative, scaled) and two or more predictor variables where at least one is continuous (quantitative, scaled) and at least one is categorical (nominal, non-scaled). ANCOVA is a combination of ANOVA and linear regression. ANCOVA tests whether certain factors have an effect on the outcome variable after removing the variance for which quantitative covariates (confounders) account. The inclusion of this analysis can increase the statistical power.
The Analysis of Covariance (ANCOVA) is a type of Analysis of Variance (ANOVA) that is used to control for potential confounding variables. ANCOVA is a statistical linear model with a continuous outcome variable (quantitative, scaled) and two or more predictor variables where at least one is continuous (quantitative, scaled) and at least one is categorical (nominal, non-scaled). ANCOVA is a combination of ANOVA and linear regression. ANCOVA tests whether certain factors have an effect on the outcome variable after removing the variance for which quantitative covariates (confounders) account. The inclusion of this analysis can increase the statistical power.
The Analysis of Covariance (ANCOVA) is a type of Analysis of Variance (ANOVA) that is used to control for potential confounding variables. ANCOVA is a statistical linear model with a continuous outcome variable (quantitative, scaled) and two or more predictor variables where at least one is continuous (quantitative, scaled) and at least one is categorical (nominal, non-scaled). ANCOVA is a combination of ANOVA and linear regression. ANCOVA tests whether certain factors have an effect on the outcome variable after removing the variance for which quantitative covariates (confounders) account. The inclusion of this analysis can increase the statistical power.
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