Peers-Brilliant Answers
Respond
t
o your colleagues’ posts and comment on the following:
1
. Do you think the variables are appropriately used? Why or why not?
2
. Does the addition of the control variables make sense to you? Why or why not?
3. Does the analysis answer the research question?
B
e sure and provide constructive and helpful comments for possible improvement.
4. If there was a significant effect, comments on the strength and its meaningfulness.
5. As a lay reader, were you able to understand the results and their implications? Why or why not?
In this discussion, multiple regression is used to determine the impact of two independent variables in predicting the dependent variable. The HS Long Study Dataset is used for this discussion. The independent variables are a)
N
umber of AP science courses offered and b)
Years science teacher has taught high school science
; The dependent variable is student science self-efficacy. According to Yancey (2019), self-efficacy is an individual’s belief that they can or cannot achieve something. The person believes they can or cannot reach a goal or accomplish a task. In this particular case, students’ science self-efficacy is their belief that one can or cannot obtain academic achievement in science.
All of these variables were measured as “scale” in SPSS. The independent variables were initially selected as one believes they had some relevance to the student’s science self-efficacy. As high schools offer more advanced placement courses, students have more options to take higher-level science courses that could better prepare them for college and increase their self-efficacy. The years’ science teacher has taught high school science may also impact student’s self-efficacy.
The X1SES mean is .0678.
Table 1 shows the descriptive statistics where
Number of AP science courses offered
(N = 1,817, M =
2.48
, and SD =
1.519
); Years science teacher has taught high school science (N = 1,817, M=
10.62
, and SD =
7.867
); and Scale of student’s science self-efficacy (N = 1,817, M =
.1024
, and SD =
.96453
).
RQ: To what extent does the number of AP science courses offered and the number of years science teacher has taught high school science predict student’s self-efficacy?
Ho: Number of AP science courses offered and the number of years science teacher has taught high school science do not predict student’s self-efficacy.
Ha: Number of AP science courses offered and the number of years science teacher has taught high school science predict student’s self-efficacy.
Complex correlational research would be an appropriate research design to explore the relationship amongst student’s science self-efficacy, the number of AP science courses offered and the number of years science teacher has taught high school science. The correlation research design allows researchers to make predictions (Burkholder et al., 2020, p. 61). This discussion includes more than one predictor variable. Thus, the statistical test used is multiple regression.
The
Model
Summary in Table 2 shows the adjusted R-squared as
.004
(Table 3). In other words, .4% of the variability of student’s self-efficacy is explained by the combination of the number of AP science courses offered and the number of years science teacher has taught high school science. 99.6% is left to be explained by other factors. The adjusted R-squared score of .004 reveals a very small effect size.
Table 3 reflects the ANOVA where the null hypothesis is rejected as p < .05. The mathematical formula is written as
F
(2,
1814
) =
4.374
; p < .05.
Table 4 shows the coefficients. The number of AP science courses offered is significant as p <.05; However, the years science teacher has taught high school science is not significant as p >.05. Because the years science teacher has taught high school science is insignificant, it is not included in the regression equation. The regression equation is as follows:
Scale of student’s science self-efficacy =
-.035
+(
.040
*Number of AP science courses offered)
Table 4 shows that as the number of AP science courses offered increases by one unit, the student’s science self-efficacy score increases by .040 while holding the other independent variable constant. Also, for every standard deviation increase of the number of AP science courses offered, the student’s science self-efficacy increases with
.063
standard deviations while holding the other independent variable constant. Lastly, the value of -0.35 shows the Scale of student’s science self-efficacy when both independent variables are equal to zero.
Based on the above analysis, the number of AP courses offered and the years science teacher has taught high school science are significant but have a very small effect on the student’s science self-efficacy. It is apparent that other variables impact the student’s science self-efficacy.
Table 1
|
Descriptive Statistics |
|||||
|
Mean |
Std. Deviation |
N | |||
|
T1 Scale of student’s science self-efficacy |
.1024 | .96453 |
1817 |
||
| Number of AP science courses offered | 2.48 | 1.519 | |||
| Years science teacher has taught high school science | 10.62 | 7.867 |
|
Table 2
Model Summary | Model | 1 | .004 |
Table 3
|
ANOVAa |
|||||
|
Sum of Squares |
df |
Mean Square |
F |
Sig. |
|
|
Regression |
8.109 |
2 |
4.055 |
4.374 |
.013b |
|
Residual |
1681.332 |
1814 |
.927 |
||
|
Total |
1689.441 |
1816 |
|||
|
a. Dependent Variable: T1 Scale of student’s science self-efficacy |
|||||
|
b. Predictors: (Constant), Years science teacher has taught high school science, Number of AP science courses offered |
Table 4
|
Coefficientsa |
||||
|
Unstandardized Coefficients |
Standardized Coefficients |
t | ||
| B | Std. Error |
Beta |
||
| (Constant) | -.035 |
.053 |
-.665 |
.506 |
| .040 |
.015 |
.063 |
2.669 |
.008 |
|
.003 |
.030 |
1.289 |
.198 |
Tonya
References:
Burkholder, G. J., Cox, K.A., Crawford, L.M., & Hitchcock, J.H. (Eds.). ( 2020). Research designs and methods: An applied guide for the scholar-practitioner. Thousand Oaks, CA: Sage.
Yancey, G. B. (2019). Self-efficacy. Salem Press Encyclopedia of Health.