Example in My Field: Impact of Health Education on HIV Testing Uptake
Dependent variable (outcome):
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HIV testing uptake (binary: tested vs. not tested)
Independent (predictor) variables:
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Age (continuous, e.g., in years)
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Gender (binary: male vs. female)
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Education level (categorical: no formal education, primary, secondary, tertiary)
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Marital status (binary: married vs. not married)
Are these predictor variables related to each other?
Yes, there may be relationships between these predictor variables. For example:
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Age and Education level: Older individuals may have had less access to education, which could be correlated with lower levels of formal education. Thus, younger individuals might have higher levels of education compared to older individuals.
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Gender and Marital status: In some societies, marital status may be more strongly associated with gender roles (e.g., women might be more likely to be married at a younger age).
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Age and Marital status: Older individuals may be more likely to be married, as marital status tends to increase with age.
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Education level and HIV testing uptake: People with higher levels of education may have better awareness of the importance of HIV testing, leading to a higher likelihood of testing.
These variables could indeed be correlated and interdependent.
Do you think the relationship between pairs of predictors is linear?
Not all relationships between predictor variables are linear. For example:
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Age and HIV testing uptake: There may be a nonlinear relationship, with young people and older people being less likely to get tested compared to people in their mid-life. Young people might not see HIV testing as urgent, and older individuals might be less likely to be tested due to stigma or misconceptions.
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Education and HIV testing: This relationship might not be strictly linear either. There could be a sharp increase in testing rates from no education to primary education, with diminishing returns as education increases beyond a certain point.
Disadvantages of examining the relationship of each predictor variable with the dependent variable, ignoring the other predictors:
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Ignoring Confounding Variables: If we only examine one predictor at a time, we may fail to account for confounding variables that affect both the dependent variable and other predictors. For instance, age might be related to both education and HIV testing uptake, so examining age alone might not give a clear picture of the true relationship between education and HIV testing uptake.
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Overlooking Interaction Effects: Some predictors may interact with each other to influence the outcome. For example, the relationship between education and HIV testing uptake might differ by gender (e.g., women with higher education may have a much higher likelihood of getting tested compared to men with the same level of education). Ignoring this interaction would oversimplify the relationship.
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Omitted Variable Bias: By ignoring other predictors, we risk making biased estimates of the relationship between a single predictor and the dependent variable. For instance, marital status may influence HIV testing uptake, but if it’s not included in the model while analyzing only age and education, the effect of education could be overestimated or underestimated.
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Lack of Comprehensive Insights: A multivariable analysis that includes all predictors provides a more nuanced and accurate picture of the factors influencing the dependent variable. Ignoring other variables could lead to an incomplete or misleading understanding of the true dynamics at play.
In summary, while examining each predictor variable separately might provide some insights, it’s essential to consider the interrelationships between predictors to get a full picture of how these factors collectively influence the dependent variable, especially in public health research.