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5PSYC001: Data Analysis for Psychology Learning Journal Coursework Sample

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5PSYC001: Data Analysis for Psychology Learning Journal Coursework Sample

Introduction

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This assessment will help to draw a conclusion from the data analysis with the help of the SPSS tool. This tool is used to determine the significance between the two variables. The ANOVA tool is used to find the anxiety test and to examine the performance among the dependent as well as the independent variables.

2-WAY ANOVA AND MULTIPLE LINEAR REGRESSIONS

Recording this analysis

Case Processing Summary

 

Cases

Included

Excluded

Total

N

Percent

N

Percent

N

Percent

Counting * Rhyming

10

100.0%

0

0.0%

10

100.0%

Counting * Adjective

10

100.0%

0

0.0%

10

100.0%

Counting * Imagery

10

100.0%

0

0.0%

10

100.0%

Counting * Intentional

10

100.0%

0

0.0%

10

100.0%

Counting * Rhyming

Counting

Rhyming

Mean

N

Std. Deviation

3.00

5.0000

1

.

6.00

7.5000

4

1.91485

7.00

8.0000

2

1.41421

8.00

7.0000

1

.

9.00

8.0000

1

.

11.00

4.0000

1

.

Total

7.0000

10

1.82574

Table 2: counting of rhyming

(Source: created by researcher)

Counting * Adjective

Counting

Adjective

Mean

N

Std. Deviation

6.00

8.0000

1

.

8.00

6.0000

1

.

10.00

7.0000

1

.

11.00

6.6667

3

2.51661

13.00

6.3333

3

1.52753

14.00

10.0000

1

.

Total

7.0000

10

1.82574

Table 3: counting of adjectives

(Source: created by researcher)

Counting * Imagery

Counting

Imagery

Mean

N

Std. Deviation

9.00

10.0000

1

.

10.00

5.0000

1

.

11.00

7.6667

3

.57735

12.00

7.5000

2

2.12132

16.00

6.0000

1

.

19.00

7.0000

1

.

23.00

4.0000

1

.

Total

7.0000

10

1.82574

Table 4: counting of imaginary

(Source: created by researcher)

Counting * Intentional

Counting

Intentional

Mean

N

Std. Deviation

5.00

8.0000

1

.

10.00

9.5000

2

.70711

11.00

6.0000

3

1.73205

14.00

6.0000

2

.00000

15.00

5.0000

1

.

19.00

8.0000

1

.

Total

7.0000

10

1.82574

Table 5: counting of intentional

(Source: created by researcher)

Reporting this analysis

From the performed test it can be analyzed that there were a total of 10 participants in this model. There were 3 participants whose standard deviation is obtained to be 1.732 and there were two participants whose deviation is obtained to be 0.70711.

  1. the test has been performed in the ANOVA model where several results have been obtained of standard deviation. The value of standard deviation is 2.12132. 

Reproducing this analysis

From the performed test in 2 ways ANOVA, it can be analyzed that there were a total 10 participants in this model from which the value of standard deviation is maximum as there were a total 3 participants. However, the second largest standard deviation is the value 1.52753.

Reflections on this analysis

From the above performed test it can be analyzed that there were a total of 10 numbers of counting. The standard deviation of the numbers is found to be 1.91485 and the other number is 1.41421. However, the mean of all the variables was close to each other. There were a total of 4 members in which the value of mean is 7.5 and there were 2 members whose mean is obtained to be 8. As per my point of view this has critical effect on outliers. From the performed test in 2 ways ANOVA, it can be analyzed that there were a total 10 participants in this model from which the value of standard deviation is maximum as there were a total 3 participants. However, the second largest standard deviation is the value 1.52753.

Conclusion

The assessment can be concluded by; the regression analysis was helpful to conduct the entire task. The analysis helps to understand the relationship between the two variables. This test was conducted in order to analyze the connection between one of the dependent variables and the other independent variable. This test is known to be helpful as these statistics can help to identify whether the test is specific and significant.

References

Campbell, Z., Bray, A., Ritz, A., & Groce, A. (2018, April). Differentially private anova testing. In 2018 1st International Conference on Data Intelligence and Security (ICDIS) (pp. 281-285). IEEE. Retrieved on 18th December 2020 from: https://arxiv.org/pdf/1711.01335

Daoud, J. I. (2017, December). Multicollinearity and regression analysis. In Journal of Physics: Conference Series (Vol. 949, No. 1, p. 012009). IOP Publishing. Retrieved on 15th December 2020 from: https://iopscience.iop.org/article/10.1088/1742-6596/949/1/012009/pdf

Fraiman, D., & Fraiman, R. (2018). An ANOVA approach for statistical comparisons of brain networks. Scientific reports8(1), 1-14. Retrieved on 16th December 2020 from: https://www.nature.com/articles/s41598-018-23152-5

Hanley, J. A. (2016). Simple and multiple linear regression: sample size considerations. Journal of clinical epidemiology79, 112-119. Retrieved on 20th December 2020 from: http://www.med.mcgill.ca/epidemiology/hanley/Reprints/SimpleMultipleLinearRegressionSampleSize.pdf

Lang, H. (2016). Elements of regression analysis. Stockholm: KTH Mathematics. Retrieved on 25th December 2020 from: https://people.kth.se/~lang/regression_analysis.pdf

Lin, L., & Dobriban, E. (2020). What causes the test error? Going beyond bias-variance via ANOVA. arXiv preprint arXiv:2010.05170. Retrieved on 15th December 2020: https://arxiv.org/pdf/2010.05170

Nyaga, V. N., Aerts, M., & Arbyn, M. (2018). ANOVA model for network meta-analysis of diagnostic test accuracy data. Statistical methods in medical research27(6), 1766-1784. Retrieved on 25th December 2020 from: https://journals.sagepub.com/doi/pdf/10.1177/0962280216669182

Ranganathan, P., Pramesh, C. S., & Aggarwal, R. (2017). Common pitfalls in statistical analysis: logistic regression. Perspectives in clinical research8(3), 148. Retrieved on 10th December 2020 from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5543767/

Xu, H., Xiao, K., Yu, J., Huang, B., Wang, X., Liang, S., ... & Huang, X. (2020). A Simple Method to Identify the Dominant Fouling Mechanisms during Membrane Filtration Based on Piecewise Multiple Linear Regression. Membranes10(8), 171. Retrieved on 20th December 2020 from: https://www.mdpi.com/2077-0375/10/8/171/pdf

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