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
|
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 B
and 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 t
test 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.
