Effect of Self-Efficacy on Procrastination

Published: 2017-08-09 09:13:26
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Harvey Mudd College
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Effect of Self-Efficacy on Procrastination

Description of Data


Participants procrastination scores were obtained by summing all the items up to produce the final composite score (6 items reverse-scored). Specifically, item 1, 2, 4, 5, 6, and 10 were reverse-scored. Low scores indicate high academic procrastination and vice versa. Table 2 below shows the participants procrastination scores per item and the total procrastination scores.


Distribution of Quantitative Data

Distribution of self-efficacy scores

Figure 1, the histogram, below shows the distribution of participants self-efficacy scores. The histogram was created in SPSS v 20 using the following procedures: First, the data file was opened in SPSS. Second, I clicked on the Graphs menu. This was followed by clicking of Chart Builder option. After that, Histogram option was clicked in the gallery section. A simple histogram was chosen and transferred to chart preview section. Participants self-efficacy scores were moved to the x-axis of the histogram. After the histogram had been produced in SPSS output, I doubled clicked the graph to enable editing. After that, I clicked elements, then clicked Show distribution curve option to display the normal curve. From the histogram below, it can be seen that self-efficacy scores are approximately uniformly distributed. There seem to be two probable outliers to the far right, around a total self-efficacy scores of 28 to 30.

Figure 1: Histogram showing distribution of self-efficacy scores

The following procedure was used to create a box plot in SPSS v 20: First, the data file was opened in SPSS. In data view window, I clicked on the Graphs menu. This was followed by clicking of Legacy dialog option. I then chose the box plot option, then clicked on the Simple option. After that, I clicked on the Summaries of separate variables option, then clicked ok to generate the box plot. Figure 2 below shows box plot for self-efficacy scores is shown below.


Figure 2: Box plot showing distribution of self-efficacy scores

Distribution of procrastination scores

Figure 3 below shows the distribution of procrastination scores. The histogram was generated in SPSS v 20 using the procedures used to generate self-efficacy histogram from the histogram below; it can be seen that the distribution is bimodal.


Z-Scores and the Normal Distribution

Self-efficacy tend to have a distribution that is closest to a normal distribution (unimodal and symmetric) as most of the bars of the histogram follow a similar pattern to the bell curve. The lowest value in self-efficacy data is 11 while the highest value is 28. Given that the standard deviation is 5.49 and the mean is 19.8, the Z-score of the lowest value can be calculated as shown below:

On the other hand, the Z-score of the highest value can be calculated as shown below.

A score of 11 is more extreme because it is 1.6 standard deviations below the mean compared to a score of 28 which is 1.49 standard deviations above the mean. For a value picked randomly, such as 19, the z-score is:

The probability of finding a score less than -.15 standard deviations below the mean is:

(0.5- .15) 100% = 85%

Linear Regression and Correlation

Linear regression

The proposed examined the relationship between self-efficacy beliefs and procrastination behavior of high school students. Participants self-efficacy scores were taken as the explanatory (x) variable, while their procrastination scores were taken as the response (y) variable. Self-efficacy was chosen as the explanatory variable because past studies have reported that it predicts procrastination behavior.

Scatter plot

Figure 5 below shows the relationship between participants self-efficacy scores and procrastination scores. From the scatter plot below, it seems that an increase in self-efficacy scores lead to increased procrastination scores. This form of the relationship between the two variables is a linear relationship. The scatterplot below shows that the correlations that are r = + 0.69. The strength of the association is approximately large or strong. The direction of the relationship is positive (i.e., self-efficacy and procrastination are positively correlated). This indicates that both variables tend to increase together. Because there seems to be a linear relationship between both variables, the association between the variables can be examined using both linear regression and correlation in SPSS v 20. From the scatter plot below, it can also be seen that there is a single outlier.

Figure 5: Scatter plot


Correlation Coefficient and Linear Regression

Correlation Coefficient

To obtain correlation coefficient, Pearson correlation analysis was conducted in SPSS v 20. The procedure was as follows: After opening the data in SPSS, I clicked Analyze, followed by Correlate, then Bivariate, then chose Pearson option. I then clicked Ok to generate Pearsons correlation output. Table 3 below shows the SPSS out for Persons correlation analysis. The results of Pearsons correlation analysis showed a statistically significant correlation between self-efficacy and procrastination, r (20) = 0.83, p< .05 (see Table 1). Because the correlation coefficient, r, is .83, the relationship between the two variables is positive. This is a strong positive relationship. The correlation coefficient of .83 confirms positive relationship displayed in the scatter plot. In the previous section, a positive correlation was also found as confirmed by a correlation of, r, of 0.69.

Table 3: Pearsons correlation results

Correlations

Participant's total self-efficacy scores

Participant's procrastination scores

Participant's total self-efficacy scores

Pearson Correlation

1

.83**

Sig. (2-tailed)

.00

N

20

20

Participant's procrastination scores

Pearson Correlation

.83**

1

Sig. (2-tailed)

.00

N

20

20

**. Correlation is significant at the 0.01 level (2-tailed).

Linear Regression

Table 4 below shows Coefficients part of SPSS output needed to write the regression equation. From the table below, the regression equation is: Procrastination = 1.60 X value self-efficacy + -.47. The SPSS procedure used to conduct linear regression analysis is: First Analyze is clicked, followed by clicking of Regression option, then Linear option is clicked. After that, the dependent and independent variable are transferred to appropriate boxes. Lastly, Ok is clicked to generate the results.

Table 4: Coefficients

Coefficients


Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

1

(Constant)

-.47

5.18

-.09

.93

Participant's total self-efficacy scores

1.60

.2

.831

6.33

.00

a. Dependent Variable: Participant's procrastination scores

Discussion

The results of linear regression and correlation analysis confirm my initial belief that these variables are associated. Specifically, it confirms my belief that high self-efficacy results in avoidance of procrastination while low self-efficacy beliefs lead to procrastination. This is because as self-efficacy scores increase, the procrastination scores tend to increase (r = .83). High procrastination scores indicate low academic procrastination. Therefore, increased self-efficacy scores lead to decreased frequency of procrastination. There is evidence to support the findings of the current study. For instance, Waschle, Allgaier, Lachner, Fink, and Nuckles (2014) reported that low perceived self-efficacy in students results in a vicious circle of procrastination.

Even though a positive relationship was found between self-efficacy and procrastination in the current study, the statistical approaches used to analyze the data (linear regression and correlation) do not indicate causation. Therefore, we cannot be sure whether self-efficacy influence procrastination or vice-versa. Another limitation of the study is its small sample size, which reduces representativeness and generalizability of the findings. Other weaknesses of the study include the presence of an outlier which might have distorted research results. It is, however, worth noting that the use of simple random sampling in picking the participants for the study helped to reduce bias. Based on this analysis, it can be concluded that self-efficacy and procrastination are correlated. However, given that data was analyzed using correlation and linear, it is not known if self-efficacy led to procrastination or vice versa.

Technology considerations

I used SPSS v 20 to create histogram and boxplot. Similarly, I created scatterplots and to computed the correlation coefficient and linear regression equation using SPSS. I chose SPSS because it is easy to use. I also preferred SPSS because it easily gives output in a format which is easy to read. However, SPSS has limitations such as its inability to analyze qualitative data limits it to quantitative data only. It is expensive for students.



References

Waschle, K., Allgaier, A., Lachner, A., Fink, S., & Nuckles, M. (2014). Procrastination and self-efficacy: Tracing vicious and virtuous circles in self-regulated learning. Learning and Instruction, 29, 103-114. doi:10.1016/j.learninstruc.2013.09.005


sheldon

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