Task A: “What is your research question?”
The Research question for this study is: Is the Constant Family Income Affected by the number Emails per Week
Introduction
To evaluate and analyze this research study, the data sets were extracted from the General Social Survey Datasets (Frost, 2017). The ability of families and households to use computerized applications can have a very significant effect or influence on the job designation and job tasks which can influence the household income. Additionally, the ability of households to utilize computers may also influence the ability to effectively communicate hence influencing the household income.
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Task B: “ What is the null hypothesis for your question?”
For this specific research study, to help answer the research question, it is imperative to select and identify the null hypothesis (Frost, 2017). For this essay, the null hypothesis (H1) is classified according to the following:
Null Hypothesis (H1): There is no direct relationship between the Family Income and the number of Email Hours per Week
Task C: “What research design would align with this question?”
The help answer the above null hypothesis and research question, the Bivariate regression and Pearson correlation was put into practice. The correlation of this test statistics clearly depicts and demonstrates the relationship between the dependent and independent variables. Consequently, if the test statistics was found to be significant, then the correlation will be flagged (Frost, 2017). Additionally, the bivariate regression model was also utilized in identifying if the test statistics was statistically significant. Therefore, to effectively prepare for the linear regression the following equation was put into consideration:
The formula for the linear regression is as shown below:
y = 31073.87 + 671.229x
Task D: Dependent and Independent Variable
In this research study, the independent variable was the total number of email hours that are used by the family or household. Additionally, the dependent variable in this research study is Family income which was measured in dollars ($)(Frost, 2017).
Task E: “If you found significance, what is the strength of the effect?”
The Pearson correlation and bivariate regression was very important in approximating the test significance between the two main variables; Independent and dependent variables. This was vital in answering the research question and in testing the validity of the findings whether the “null” hypothesis is correct or wrong. Therefore, in spite of the reduced value of r square, the findings revealed that the correlation between the family income and the number of e-mails was meaningful and very significant (Wagner, 2016). Consequently, this relates to the affinity to the computer and employee job tasks on the computer which has valuable correlations to performing tasks for their employers may eventually lead to high household or family income from the payments received from the increased number of emails and time spent on computers (Wagner, 2016).
|
Correlations |
||||||
| E-mail hours in every week | Family Income (Constant $) | |||||
| E-mail Hours per every week | Pearson Correlation |
1 |
0.222 |
|||
| 2-tailed significance | .000b | |||||
| Sample size (n) |
1471 |
1353 |
||||
| Family Income (Constant $) | Pearson Correlation |
0.222 |
1 |
|||
| 2-tailed significance | .000b | |||||
| Sample size (n) |
1353 |
2314 |
||||
|
Model Summary |
||||||
| Model | R | R Squared | Adjusted R Squared | Std (Standard Error of the Estimate) | ||
|
1 |
0.222 |
0.49 |
0.49 |
33653.805 |
||
| EMAIL hours per week | Predictors (constant) | |||||
| Family Income (Constant $) | Dependent Variable | |||||
|
ANOVA |
||||||
| Model 1 | Total Sumation of Squares | degrees of freedom (df) | Average Square | F. | Significance | |
|
1 |
Regression |
79,490,642,027 |
1 |
79490642027 |
70.186 |
.000b |
| Residual |
1,530,113,662,364 |
1351 |
1132578581 |
|||
| Total |
1,609,604,304,391 |
1352 |
||||
| EMAIL hours per week | Predictors (constant) | |||||
| Family Income (Constant $) | Dependent Variable | |||||
|
Coeffecients |
||||||
| Model 1 | Coeffecients (Unstandardized) | Coeffecients (Standardized) | ||||
| B | Standard Error | Beta | t | Significance | ||
| Constant |
31073.871 |
1045.732 |
29.715 |
.000b | ||
| Email Hours Per Week |
671.229 |
80.121 |
0.222 |
8.378 |
.000b | |
| The dependent variable: Family Income in Constant ($) | ||||||
Table showing: the Correlations, Model Summary, ANOVA and Coefficients
Task F: “ Explain your results for a lay audience; explain the answer to your research question”
Finally, it is imperative to conclude that the Pearson correlation analyzed above revealed that there was a 0.222 correlation with the level of significance of 0.000b. Therefore, in light of this it is proper to state that the null hypothesis (H1) should be rejected (Wagner, 2016) . Additionally, according to the test statistics reveal that there was a positive correlation between the number of emails hours per week and the family income (Wagner, 2016).
References
Frost, J., (12 Mar. 2017). "How to Interpret Regression Analysis Results: P-values and Coefficients." Minitab. N.p., 01 July 1970. http://blog.minitab.com/blog/adventures-in-statistics-2/how-to-interpret-regression-analysis-results-p-values-and-coefficients
Wagner, W. E. (2016). Using IBM® SPSS® statistics for research methods and social science statistics (6th ed.). Thousand Oaks, CA: Sage Publications.
