Quantitative Research Approach for Data Collection on Chosen Topic - Essay Sample

Introduction

The study used a quantitative research approach and employed the use of both secondary and primary tools of information collection to complete the research. Secondary sources included written articles, peer-reviewed journals, and electronic media relating to our topic of study. These were researched from the library and discussed at length to gain insights into the previous research information done by others in relation to the chosen topic. This information was vital in guiding the research process and pointing the researcher towards the relevant areas to explore and even in designing a hypothesis to test. Primary sources included interviews and questionnaires with interested participants in an attempt to research the designed hypothesis and see the relevance of the information collected.

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Procedure

An online survey was completed by undergraduate students at an Australian university. This method was preferred due to the ease it offered in collecting the required data for manipulation and interpretation. To eliminate bias in the gathered data, the students were offered the required privacy to fill the questionnaires if they consented to them, with assurance being provided to them in regards to the use of the collected data. This strategy allowed the students to feel more secure and give more reliable answers to the questionnaires. Furthermore, the questionnaires were issued randomly without targeting a specific group of individuals in an attempt to further eliminate bias in the collected data. Additionally, a sample population of 206 participants was selected to participate in the research process so as to make the collected data more manageable and also to give a more reflective conclusion to the intended tests.

Manipulation of the collected data was done using the software SPSS as it was easier to get the results using this software rather than using others such as Microsoft Excel. The decision to pick SPSS as the main software to manipulate the data was also based on the fact that it was easier to work with the software when dealing with large volumes of information and the results easily interpreted. The obtained results were then interpreted and presented in the next chapters of the paper.

Factor Analysis

The factor analysis was done for the new attachment was done to check for the relationship between the variables. To attain this, a correlation matrix was done for the bivariate functions. From the matrix, it was noted that the variables were correlated with one another, given the correlation significance level of 0.05. In the analysis however, the variable "problem section" was excluded and placed in the missing variables as it originated from an improperly entered treatment and would thus result in a calculation error if not cleaned. Following this, it was crucial to do a factor reduction and use the results to explain the correlation with the other factors that had been taken into consideration in the analysis. For our analysis, the maximum iterations for convergence were increased from 25 to 100 to ensure the SPSS software captured all the necessary factors and make possible the carrying on of smooth calculations. To test the correlation of the variables making up the new attachment, a KMO and Bartlett's test was instituted and the results noted (Martin et al., 2012). From the KMO and Barlette's table, it was noted that the significance was less than 0.05, given the observed result as being 0.000. This, in essence, points us to the conclusion that the variables are significantly correlated as seen below,

However, looking at the total variance table, it is evident that two components were retained in order to explain the relationships between the variables. This suggests that the analysis would not result in a unidimensional factor as hypothesized. One of the components accounted for 45% of the variance while the other accounted for 58%.

Validity and Reliability Tests Results

Non-Attachment to Education and Non-Attachment to Self

Convergent validity is the degree to which operalizations of operations that are similar converge with each other. The guiding principle in convergence validity testing thus is that the measures of constructs that are related to each other would be strongly correlated. The variable that was tested in the exercise was non-attachment to self. A Pearson correlation test was performed to test the hypothesis that convergent validity would be demonstrated via a positive relationship between non-attachment to education and non-attachment to self. A 2-tailed significance test was then designed for the variables non-attachment to education and non-attachment to self components. A significance value of 0.05 was used to test the correlation and the results interpreted from the resulting table.

Analysis reveals that for most of the results in the table, the p-value was less than the significance value of 0.05. Save for a few variables that were more than the designated significance value, all the rest indicated a significant relationship. Thus we were driven to the conclusion that there was a significant positive correlation between the tested variables, which was in line with our hypothesis. This would confirm the presence of a convergent validity of the variables used.

Procrastination and Non-Attachment to Education

Convergent validity was also determined in this test. A Pearson correlation test was done to test the convergent reliability of the relationship between the variables, non-attachment to education and procrastination. As before, a significance level of 0.05 was applied as the basis to measure for the occurrence of a relationship between the two variables. A tabulated results table was then analyzed to check for any significance indicators. Here the principle that when the observed p is less than or equal to the designed significance level (0.05), then there would be a significant correlation, was applied. On the other hand, when p is greater than the assigned significance value, there would be no significant correlation. Applying this principle, it was observed that the majority variables had an observed p-value that was less than 0.05. The results would thus point us to the conclusion that the two variables were significantly correlated (Dweck et al., 1985).

Moreover, all the variables showed a negative correlation as evidenced by the '-sign. This would, therefore, lead us to conclude that although the two variables were significantly correlated, the correlation between procrastination and non-attachment to education would be a negative one, hence a convergent validity.

Non-Attachment to Education and Extraversion.

The fourth step involved working with the variables representing non-attachment to education and extraversion in order to determine the behavioral aspect in relation to attachment to education in the individual (Chio et al., 2017) to test the discriminate validity. From these variables, a discriminate analysis test was done using the significance level of 0.05. The independent variables were taken as the predictors and the groups used as dependent variables. As with the other tests before, the testing parameters to indicate a significant correlation between the variables was that: correlation occurs when p is less than or equal to 0.05 and when p is less than 0.05, there would be no correlation. Also just like before, the tabulated results of the tests were analyzed and interpreted.

It was noted in this particular test that apart from two or three variables tested, the majority of the variables had a significant value that was greater than our testing significance level of 0.05. This would be an indication thus that there was no correlation between the non-attachment to education by the participants and their procrastination habits (Chio et al., 2017). The conclusion thus was that there was sufficient discriminate validity.

Non-Attachment to Education and Thoughts About Withdrawing From a Study

Finally, a criterion-related validity test was performed to ascertain if the test reflected a set of abilities. Items tested included the correlation between the non-attachment to education by the participants with the tendency to think about withdrawing from a study. Here, out of the 206 participants in the sample, 36 people refused to answer, bringing n to 170. Thus from a population sample of 170 individuals, the interview collected opinions on how regularly one thought about withdrawing from a study. In an attempt to find the likely triggers to such an outcome, a correlation test was performed between these variables and the variables for non-attachment to education as the probable cause. A person significance test was then performed to address the correlation, taking the significance value of 0.01. Thus a significance value lesser than 0.01 would indicate a significant co-relation while that greater than 0.01 would indicate that there is no significant correlation.

From the Pearson correlation table generated, it was discovered that most of the variables in comparison had their significance value greater than our assigned significance level of 0.01. This was with the exception of a few isolated cases where the significance level was lower than our significance level. From this analysis, it would seem that there is no significant correlation between the nonattachment to education and the tendency to hold thoughts about withdrawing from a certain study among the participants (Sandra, 2016). Thus the test failed to confirm the criterion related validity of the variables.

Discussion

From the previous chapters, the results from the research data were manipulated and interpreted in relation to our formulated hypothesis. Various conclusions could thus be drawn from the results of the study giving more light to the theory of attachment. It was evident from these results that the perception that people have about certain attributes affect their understanding and responses as addressed by Chong (1985). From the first test, the results indicated that two factors were to be used to test the variance as opposed to one. This effectively dispelled our hypothesis that factor analysis of the new attachment would result in a one-dimensional factor, thus we had to adopt a null hypothesis in this case, that the factor analysis of the sample would not result in a unidimensional factor.

From the second test, it was apparent that there is convergent validity in line with the hypothesis as confirmed by the positive significant relationship found between non-attachment to education and non-attachment to self. This would mean that the more non-attached to self one is, the more likely they are to also be non- attached to their education or at least possess some of the traits that signify nonattachment. Thus we would be compelled in this respect to adapt our hypothesis as stated in this respect.

In testing for a relationship between procrastination and non-attachment to education, a negative correlation was established. This would give it c...