Impact Of School Closure For Case Study Of Kingston Community

Hypothesis

Write about the Impact of School Closure for Case Study of Kingston Community.

Decision making is a process which put across all the stakeholders to avoid issues such as vested interests and biases. Avoiding to involve all the stakeholders and especially the main ones, the decision might be resisted, which might result to violence and communal conflicts. In this paper, we will analyse a case study of closure of Kingston school. The community have complained that they were not involved on making such as critical decision which affects their community. According to the residents, it has been rumoured to sometime that the school was to be closed due to reduced admission levels which has been led to the reduction of the Kingston’s population. Although these might have been concrete reasons to close the school, the main concerns of the community members is failure to consult them while they were making the decision. I addition, the school has existed for a century and they feel it is not an honourable action to close a school which contributed so much to the growth of the community. Nevertheless, the student will have to be bussed to another school every day – which translates to more time and money for the families to cater.

Scientifically, this study is conducted to check whether there would be a difference in perceptions between the time before and after the school closure. The data is collected using a questionnaire, before and after the school closure and entered into SPSS system for analysis. Within the questionnaire, there is a community survey attitude measure too which is the key section to recode before and after values. Each question within the community attitude survey tool is in form of a Likert scale with 5 options from strongly agree through strongly disagree. After compiling the data, the values are added to get the pre and post totals, hence generating the two variables for effective comparison. The measurements are collected from the same individuals for the pre and post measurements, hence generating paired data. The other section of the questionnaire includes the demographic information which is collected once and can be used for further analysis. All the variables are coded in SPSS, hence transforming them into categorical variables. In this paper, the test hypothesis will be stated, the possible research method highlighted and the data analysed after reviewing the validity of the questionnaire(Trochim, 2006).

Research Methodology

The hypothesis will be based on the pre-post measurements of community attitudes. I believe that before the closure the parents are very bitter about the decision but after the decision is officially announced, most of them will feel that there is not more to be done for the purpose of reversing the decision.

Null hypothesis: There is no difference in community attitudes between pre and post measurements.

Alternative hypothesis: The community attitudes will significantly difference between pre and post-test on the community attitudes

The research design used in this study is an experimental design with a pre and post measurements. The official communication from the authorities on the closure of the school acts as the treatment. This study will focus to check the impact of the declaration about the school closure to the opinions and attitudes of the community. This can also be referred as a quasi-experimental study because the study situations are not controlled. However, there are several ways in which the study results can be improved by improving its procedures. These methods might include randomization and stratification which might be used to reduce biases by controlling for possible confounders.

Randomization criteria give every individual in the population an equal chance to be chosen in the sample. In this case, the information gathered from the target population is believed to have a minimal bias which might emerge by selecting a sample with individuals having similar characteristics. For example, Sarah put posters in public areas to allow the interested parties join the study. The selected individuals might end up to be those who are directly affected which is not the main focus for scientific studies. It is important to get a view from every individual from the target population and this might only be achieved by randomisation because in most instances it is not easy to include every individual in the study. Therefore, Sarah could have developed a better way of selecting the study participants other than putting posters for self-recruitment. Else, the study sampling would be purposive by only including individuals who are easily accessing and only those who see the posters. Since Kingston is a small town, Sarah could have requested for the administration register, hence selecting the individuals randomly and contacting them to request the fro participation. In this manner, the study would be more scientific and the results obtained would perceive better evidence about Kingston’s view and perceptions(Manly, 2010).

Randomization

Stratification is another effective method of reducing biases in scientific studies. Population characteristics such as gender distribution among other key factors can be used to stratify a target population for better results. For instance, the perceptions and opinions closure might differ based on different factors within the society such as gender and age, For example, parents within student in the school who are being affected directly might be more bitter compared to those who do not have children within school leaving age bracket. Also, mothers might have different views because they are more caring and concerned about the welfare of their children. Considering such characteristics, Sarah could have settled on an effective way of stratifying the population to get better results, which are more representative(Bhatia et al., 2016).

The questionnaire is well developed to measure the characteristics of interest in the research. However, Sarah might not have captured all the demographic information of interests such as the age and gender of the respondent. These are among the most significant factors which can be used to predict and define correlations. Sarah could have included two question to recode the age and gender of the respondent. Therefore, this could have been useful in the analysis state to conduct stratified analysis, which might bright out some important characterises of the population(Lessler et al., 2014). For instance, there might be the difference in the pre and post measurements based on gender. Therefore, the other question could have followed these ones, hence making the questionnaire more concrete. Further, the mode of collecting information could have been improved by employing several research assistants to help her in collecting the data from the field. It would be better to meet the respondents in person and avoid sending the information to the post office. According to the identified issues identified in the questionnaire, I would state that the information collected is not sufficient to measure and analyse the views of Kingston’s community(Hox and Boeije, 2005).

Table 1: Frequency distribution

	Count	Percent
Parent to secondary school age next year	Yes	60	57.7%
No	44	42.3%
Distance from home to Kingston School	0 – 1 km	28	26.9%
2 – 4 km	16	15.4%
5 – 20 km	24	23.1%
> 20 km	36	34.6%
Distance from home to High school at Beganup	0 – 20 km	12	11.5%
21 – 40 km	20	19.2%
41 – 60 km	44	42.3%
> 60 km	28	26.9%
Youngest family member age	0 – 5 years	12	11.5%
6 – 12 years	28	26.9%
13 – 18 years	24	23.1%
> 18 years	40	38.5%
Number of years residing at Kingston	0 – 1 years	20	19.2%
2 – 4 years	16	15.4%
5 – 10 years	20	19.2%
> 10 years	48	46.2%
Occupation of a family member with the highest salary	Farmer	52	50.0%
Government employee	36	34.6%
Business	16	15.4%
Unemployed	0	0.0%
Residential/Boarding high school at Kingston after Kingston Closure	Yes	40	38.5%
No	64	61.5%
What is the standard education of 8 – 10-year-olds at Kingston	Low	28	26.9%
Medium	48	46.2%
High	28	26.9%

According to the frequency table above, the proportion of the parents with students who would be proceeding to a secondary school in the next year were 57.7%(Trochim, 2006). Fifty percent of the respondents were farmers as anticipated because most of the residents in Kingston depend on farming. There were no unemployed respondents in the study which might mean that almost all the residents in Kingston town are employed(Eurostat, 2016).

Stratification

Table 2: Summary statistics by categories

	Pretest total	Posttest total
Mean	Median	Standard Deviation	Mean	Median	Standard Deviation
Parent to secondary school age next year	Yes	31	31	8	21	21	5
No	39	41	5	28	22	9
Distance from home to Kingston School	0 – 1 km	33	31	8	20	20	5
2 – 4 km	36	37	4	23	22	7
5 – 20 km	36	37	3	23	22	3
> 20 km	34	38	11	27	21	10
Distance from home to High school at Beganup	0 – 20 km	42	41	4	28	22	9
21 – 40 km	40	39	4	27	25	8
41 – 60 km	33	32	7	22	21	6
> 60 km	29	31	9	22	19	8
Youngest family member age	0 – 5 years	30	29	6	23	21	6
6 – 12 years	29	31	9	20	19	4
13 – 18 years	33	34	6	21	21	6
> 18 years	40	41	5	28	24	9
Number of years residing at Kingston	0 – 1 years	38	39	5	26	23	7
2 – 4 years	27	29	5	18	18	4
5 – 10 years	33	36	7	20	19	5
> 10 years	35	37	9	26	22	8
Occupation of a family member with the highest salary	Farmer	34	37	9	26	22	8
Government employee	36	38	5	23	22	6
Business	30	29	9	18	18	4
Unemployed	.	.	.	.	.	.
Residential/Boarding high school at Kingston after Kingston Closure	Yes	41	41	4	27	22	8
No	30	31	7	22	21	7
What is the standard education of 8 – 10-year-olds at Kingston	Low	42	42	4	27	22	8
Medium	36	37	4	23	22	6
High	24	24	5	22	19	9

(Ibm, 2012; Field, 2013)

On average, the pre-test scores seem to have been greater than the post-test, hence the conclusion that the attitudes of the residents of Kingston about the closure of the high school changed significantly after the official announcement(Devore, 2006).

Table 3: Overall descriptive statistics for pre and post tests

	N	Minimum	Maximum	Mean	Std. Deviation
Pre-test total	104	13	48	34.27	8.017
Post-test total	104	13	43	23.68	7.669

Table 4: Correlation between pre and post-tests

Paired Samples Correlations
	N	Correlation	Sig.
	Pre-test total & Post-test total	104	.355	.000

Table 5: Paired t-test output

Paired Samples Test
	Paired Differences	t	df	Sig. (2-tailed)
Mean	Std. Deviation	Std. Error Mean	95% Confidence Interval of the Difference
Lower	Upper
	Pre-test total – Post-test total	10.587	8.916	.874	8.853	12.320	12.109	103	.000

(Kent State University, 2016)

Conclusion

According to the paired t-test results in the table above, the p-value is less than the significance level, hence rejecting the null hypothesis and conclude that the average community attitudes for pre-test are significantly different compared to the post-test(Winter, 2013). Based on the descriptive statistics in tables 2 and 3, we can conclude that the average level of community attitudes towards the closure of the Kingston school is lower after the official communication of the closure compared to the time before. Therefore, the community acted more passionately before the administration communicated and they significantly changed their mentality immediately after the department of education confirmed the closure. Although the policies will affect the community they have no much to do after the department of education have made up their mind on the decision. However, there might be errors in data collection and study design which have led to wrong findings, which creates validity questions of the research.

References

Bhatia, G. et al. (2016) ‘Correcting subtle stratification in summary association statistics’, bioRxiv, p. 76133. doi: 10.1101/076133.

Devore, J. (2006) ‘Statistics for Business and Economics’, The American Statistician, 60(4), pp. 342–343. doi: 10.1198/tas.2006.s59.

Eurostat (2016) ‘Business demography statistics’, Statistics Explained, 2015(November), pp. 1–9.

Field, A. (2013) ‘Discovering Statistics using IBM SPSS Statistics’, Discovering Statistics using IBM SPSS Statistics, pp. 297–321. doi: 10.1016/B978-012691360-6/50012-4.

Hox, J. J. and Boeije, H. R. (2005) ‘Data Collection, Primary vs. Secondary’, in Encyclopedia of Social Measurement, pp. 593–599. doi: 10.1016/B0-12-369398-5/00041-4.

Ibm, A. (2012) ‘IBM SPSS Statistics 21 Brief Guide’, Ibm Spss.

Kent State University (2016) ‘SPSS Tutorials: Paired Samples t Test’, University Libraries.

Lessler, J. et al. (2014) ‘Seven challenges for model-driven data collection in experimental and observational studies’, Epidemics, 10, pp. 78–82. doi: 10.1016/j.epidem.2014.12.002.

Manly, B. F. J. (2010) ‘Randomization’, Wiley Interdisciplinary Reviews: Computational Statistics, 2(3), pp. 383–386. doi: 10.1002/wics.91.

Trochim, W. M. K. (2006) ‘Descriptive Statistics’, Research Methods, pp. 2–7. doi: 10.1016/B978-0-12-384864-2.00005-6.

Winter, J. (2013) ‘Using the Student ’ s t -test with extremely small sample sizes’, Practcial Assessment, Research & Evalutaion, 18(10), pp. 1–12. doi: Retrieved from https://pareonline.net.

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