Explaining The Beta Score, Regression Analysis and Their Relationship to Engagement Drivers
For large organizations, TalentMap uses a Beta score or coefficient to measure relationships. Specifically, the beta coefficient measures the strength and direction of an independent variable (for TalentMap surveys, the dimensions in the survey represent the independent variables: e.g. compensation, immediate management, work/life balance, etc.) in relation to the dependent variable (engagement).
A Beta coefficient can be positive: a positive change in the independent variable, say Senior Leadership, means there will be a positive change in the dependent variable, in our case, engagement. Beta coefficients can also be negative; however, in TalentMap’s survey, all dimensions are assumed to have a positive relationship with engagement, which is why these dimensions are measured. The larger the Beta coefficient, the stronger the relationship is between engagement and any of the independent variables.
In statistics, standardized coefficients or Beta coefficients are the estimates resulting from an analysis performed on variables that have been standardized so that they have variances of 1. This is usually done to answer the question of 'which of the independent variables has a greater effect on the dependent variable in a multiple regression analysis when the variables are measured in different units of measurement' (for example, income measured in dollars and family size measured in a number of individuals).
Before fitting the multiple regression equations, all variables (independent and dependent) can be standardized by subtracting the mean and dividing by the standard deviation. The standardized regression coefficients, then, represent the change in terms of standard deviations in the dependent variable that result from a change of one standard deviation in an independent variable.
In a hypothetical example, the income of family ranges from $10,000 to $100,000, while the size of the family ranges from 1 to 9. It can be expected that the standard deviation of income will be several thousand dollars (for example, $6,382) while the standard deviation of family size will be 2. Thus using standard deviation as the unit of measure takes into account that a one-person change in family size is relatively more important than a one dollar change in income.
If the standard coefficients for this example were, for instance, .535 for income and .386 for family size, changing the income by one standard deviation ($6,382) while holding the family size constant would change our dependent variable (for example, food consumption) by .535 standard deviations. Changing family size by one standard deviation, holding income constant, would change food consumption by .386 standard deviations. Thus, we can conclude that a change in income has a greater relative effect on food purchase than does a change in family size.
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