Regression Table
Symbols Used in an APA-Style Regression Table
|
Source
|
B
|
SE B
|
β
|
t
|
p
|
|
Variable 1
|
1.57
|
0.23
|
.23
|
2.39
|
.020
|
|
Variable 2
|
1.26
|
2.26
|
.05
|
0.58
|
.560
|
|
Variable 3
|
-1.65
|
0.17
|
-.28
|
2.92
|
.005
|
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There are five symbols that easily confuse students in a regression table: the unstandardized beta (B), the standard error for the unstandardized beta (SE B), the standardized beta (β), the t test statistic (t), and the probability value (p). Typically, the only two values examined are the Band the p. However, all of them are useful to know.
The first symbol is the unstandardized beta (B). This value represents the slope of the line between the predictor variable and the dependent variable. So for Variable 1, this would mean that for every one unit increase in Variable 1, the dependent variable increases by 1.57 units. Also similarly, for Variable 3, for every one unit increase in Variable 3, the dependent variable decreases by 1.65 units.
The next symbol is the standard error for the unstandardized beta (SE B). This value is similar to the standard deviation for a mean. The larger the number, the more spread out the points are from the regression line. The more spread out the numbers are, the less likely that significance will be found.
The third symbol is the standardized beta (β). This works very similarly to a correlation coefficient. It will range from 0 to 1 or 0 to -1, depending on the direction of the relationship. The closer the value is to 1 or -1, the stronger the relationship. With this symbol, you can actually compare the variables to see which had the strongest relationship with the dependent variable, since all of them are on the 0 to 1 scale. In the table above, Variable 3 had the strongest relationship.
The fourth symbol is the ttest statistic (t). This is the test statistic calculated for the individual predictor variable. This is used to calculate the p value.
The last symbol is the probability level (p). This tells whether or not an individual variable significantly predicts the dependent variable. You can have a significant model, but a non-significant predictor variable, as shown with Variable 2. Typically, if the p value is below .050, the value is considered significant.
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