Final
Part III. Describe relationships among the variables.
According to the research, the general concept for relationship variables is the “science of collecting, classifying, analyzing, and interpreting information” (McClave et al., 2022. Pg 4).
However, according to the research, the relationship between variables can be described as limitless possible caparison using various scales and tests to provide information and support related to a hypothesis theory. Variable relationships can only be authentic and supported if the information solicited is vital and used correctly in its testing methods. According to the research, variable relationships can be considered a form of analytical theory concepts.
Create a correlation matrix. Identify and discuss the strongest and weakest correlations.
The variables selected for the correlation matrix are Scaled. The comparison used is a scale rating of 1 compared to the correlations related to week and most vital.
Based on the measured intervals for scales, there is a strong correlation between age and years of experience at the .639 interval, years of knowledge before training, and years of knowledge after training at the .646 interval. The weakest correlation presented using scales as the intervals are I.D. with confidence at a -.057 and I.D. and knowledge before training with a .007.
Part IV. (Answer FIVE) - Based on the research scenario and the data, formulate the appropriate alternative and null hypotheses, conduct a proper analysis, and interpret the results for each of the following.
1. Are on-site workers more knowledgeable than off-site workers before the training begins? Asked another way, what is the difference in knowledge between on-site and off-site workers before they take the training (i.e., preintervention knowledge)?
Ha : There is a difference between the level of knowledge for the offsite worker versus an onsite worker after training begins.
The independent test reflects that the mean is 38.10 for knowledge before training and 38.87 for knowledge after training, and the two-tailed significance results are the same at .586. Per the Independent t-test provided below, the results support there is no difference between the onsite workers not being more knowledgeable than offsite workers after the training begins. Because there is no significant difference in the outcomes for onsite and offsite after the training is complete, the p-value is .112, which is greater than the .005, indicating the null cannot be rejected.
2. Does participants’ knowledge increase as a result of going through the training?
H0 : There is no increase in knowledge as a result of training completion
Ha : There is an increase in knowledge as a result of training completion
The paired t-test supports the mean is 38.38 before training and 40.22 after training for increased knowledge after training; The completed paired independent t-test provided below helps that there is an increase in knowledge after training has been completed. The difference in the knowledge after training is supported by the p-value is .004, which is less than 0.05, allowing the rejection of the null.
3. Do participants of different qualifications (professional, paraprofessional, and nonprofessional) perform differently on the certification exam? Which group performs best?
H0 : There is no significant exam performance qualification difference between the three types of professionals.
Ha : There is a significant exam performance qualification difference between the three types of professionals
The Factorial ANOVA test reflects a significate difference between groups. There is a difference between paraprofessional and Nonprofessional. Professional and nonprofessional, and professional and paraprofessional. The p-value for all groups is less than 0.05, which means the null can be rejected.
4. Does age have an impact (i.e., predict) performance on the certification exam?
H0 : Age is not a predictor of certification exam performance.
Ha : Age is a predictor of certification exam performance.
The simple regression test reflects the coefficient and the ANOVA at 0.00 and the p-value of 0.00, which supports a rejection of the null. The test supports age as a predicting factor on performance exams, with the mean at 35.38, the exam at 69.65, and the standard deviation at 8.988 for age and the exam at 10.667.
|
ANOVA |
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|
Model |
Sum of Squares |
df |
Mean Square |
F |
Sig. |
|
|
1 |
Regression |
2498.802 |
1 |
2498.802 |
34.386 |
.000b |
|
|
Residual |
4214.848 |
58 |
72.670 |
|
|
|
|
Total |
6713.650 |
59 |
|
|
|
|
a. Dependent Variable: Certification Exam |
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b. Predictors: (Constant), Age |
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Coefficients |
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|
Model |
Unstandardized Coefficients |
Standardized Coefficients |
t |
Sig. |
||
|
|
B |
Std. Error |
Beta |
|
|
|
|
1 |
(Constant) |
42.582 |
4.745 |
|
8.973 |
.000 |
|
|
Age |
.724 |
.123 |
.610 |
5.864 |
.000 |
|
a. Dependent Variable: Certification Exam |
5. In addition to age, do any of the other variables (years of experience or confidence) improve the ability to predict performance on the certification exam?
H0 : Age is not a predictor of certification exam performance.
Ha : Age is a predictor of certification exam performance.
H0 : Years of experience is not a predictor of certification exam performance.
Ha : years of experience is a predictor of certification exam performance.
The multi-regression test supports that in addition to age having an impact on exam performance, years of experience also have an impact on prediction performance on exams. The mean is 69.65 for exams, 37.38 for age, and 5.87 for years of experience. The standard deviation for the exam is 10.667, age is 8.988, and years of experience is 4,.438. The significance is 0.00, which supports the p-value being less than 0.05, allowing rejection of the null.
References
McClave, J., Benson, G., & Sincich, T., (2022). Statistics for Business and Economics (14th ed.). Boston: Pearson Education, Inc. 978-0-321-82623-7.