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Introduction

Quantitative research methods play a crucial role from investigation purposes as they help in the accumulation of raw data. This is helpful in quantifying the research and providing empirical results by way of statistical, mathematical and computational methodologies (Bryman, 2017). This report aims to provide information regarding the data collected by a team funded by the local council so as to assess the ways in which young people can be encouraged to study arts in the area. For this purpose, various SPSS models have been utilised so as to achieve successful completion of hypotheses testing.

QUESTION 1

Hypotheses:

H1: Difference between Week 1 and Week 2 in attitudes exists specifically for those with lowest SES.

H0: No Difference between Week 1 and Week 2 in attitudes exists specifically for those with lowest SES.

a. Description of the Analysis

For the purpose of achieving insights regarding whether H1 is true or not, Compare Means methodology has been undertaken. Under this tool of analysis, One-Sample T-test has been conducted so as to determine the difference between the Week 1 and 2 data provided for those having Low Socio-Economic Status (SES). It is important to note that SES is a categorical variable including four sub-categories viz. Low, Medium-Low, Medium-High and High. On the other hand, Weeks 1 as well as 2 measure attitudes towards arts after first and second event respectively.

The rationale behind choosing this methodology is that One-Sample T-test helps in determination of whether a sample has a specific mean or not. Also, one of its assumptions states that there should be no significant outliers (Hinton, McMurray and Brownlow, 2014). For the data given, this is true since only those variables have been chosen which recorded low SES for Week 1 and 2, thus, enabling simplification of data to a further extent. Since there is a comparison between Week 1 and Week 2 in relation to Low SES, this tool seems to be the most appropriate.

b. Summary of the Analyses

In order to achieve the desired results, it is ascertained that out of 93 respondents, only 10 recorded a low Socio-Economic Status. The 'One-Sample Statistics' table indicates a list of variable. Here, for both weeks, number of people taken into account for the purpose of this investigation are 10. The number of valid observations taken into account for Week 1 are 9.30 whereas for Week 2 it is 9.6. As per the second table, named 'One-Sample Test' the t-statistic is 11.381 and 7.282 for Weeks 1 and 2 respectively. In both cases, the p-value (sig.) is less than 0.05 (Nardi, 2018). Thus, indicating that there exists a statistical significance for both the variables in terms of Low SES.

c. Conclusion

From the above data it can be ascertained that the Alternate Hypothesis (H1) is accepted while Null Hypothesis (H0) is rejected. This is concluded on the grounds that the p-value is less than the threshold of 0.05. Additionally, since the p-value associated with the t-test is small (0.00), it can be affirmed that a difference in mean exists for Week 1 and Week 2. This is only possible when there is a difference in attitudes towards arts for specifically those who have Low Socio-Economic Status (SES).

QUESTION 2

Hypotheses:

H1: Current art activities and Favourite Subjects influence Engagement

H0: Current art activities and Favourite Subjects do not influence Engagement

a. Description of the Analysis

For the purpose of conducting this hypothesis testing, Correlation statistic has been performed on the data. This tool helps in establishing a relationship between two or more variables in a comprehensive manner (Venkatesh, Brown and Bala, 2013). Here, the dependent variable is Engagement whereas the independent variables are 'DoingArt' and 'FavSubj'. It is important to note that both variables 'FavSubj' and 'DoingArt' are categorical variables whereas 'Engage' is representative of a higher number as the level of engagement increases among such individuals.

The rationale behind choosing this methodology is that the formulated hypotheses want to ascertain whether or not the two variables 'DoingArt' and 'FaveSubj' influence 'Engage' variable. For this to be plausible, it is only sensible to check whether the variables share any kind of relationship that is strong enough to create an influence on the dependent variable. For this purpose, 'Correlation' seems an appropriate technique to be implemented.

b. Summary of the Analyses

Out of 93 respondents' records, only 47 are currently involved in art activities. Further breaking down these statistics, it is ascertained that Current Favourite Subjects of these are in the following majorities:

  • Languages, Sports and Math is chosen by 12 respondents respectively.
  • Music is favourite of 11 respondents.

On the other hand, the data has been chosen for only those who are currently involved in doing art activities. For this purpose, the correlation statistics state that 'FavSubj' variable has a moderate correlation (R=0.376) with dependent variable while the other independent variable, 'DoingArt' do not have much influence on the engagement (R=0.00).

c. Conclusion

It can be concluded that the independent variable related to Current Favorite Subjects in terms of those individuals who are currently doing art activities influences Engagement. However, they are not the sole influencers since a person may not be mandated to be involved in Art Activity so as to have engagement. This is due to the fact that individuals with artistic tastes who are not actively involved in such activities may also be interested in such events. Thus, 'DoingArt' does not have much influence on the engagement.

QUESTION 3

Hypotheses:

H1:  Gender influences satisfaction.

H0:  Gender does not influence satisfaction.

a. Description of the Analysis

In order to conduct a successful hypothesis testing, Linear Regression Model has been performed on the selected data. This SPSS model is an extended version of Correlation which helps in forecasting a value of a dependent variable (Walliman, 2017). Here, the dependent variable is Satisfaction (Satis) whereas the independent variable is Gender. It is important to note that the independent variable 'Gender' is a categorical variables having two sub-classes viz. Males and Females.

The rationale behind choosing this methodology is that while correlation helps in the establishing a relationship between two given variables, this models helps in indicating whether this variable helps in the prediction of other variable or not. Since the formulated hypothesis aims to ascertain whether or not Gender influences Satisfaction, it seems plausible to check that whether one is able to predict another or not.

b. Summary of the Analyses

As per the output generated, the Model Summary Table states that the Correlation between two variables is very weak, R=0.255. This is evident from the ANOVA Table too where the level of statistical significance, p-value is more than threshold, 0.05 (p-value= 0.014).

c. Conclusion

From above summary of analyses, it can be concluded that the independent variable, Gender, does not influence level of Satisfaction in a significant manner. As a result, null hypothesis (H0) is accepted while alternate hypotheses (H1) is rejected.

QUESTION 4

Hypotheses:

H1: Attitudes towards arts improve over Time

H0: Attitudes towards arts does not improve over Time

a. Description of the Analysis

As per the aim of the hypothesis developed, it is required to be checked that whether or not attitudes towards art improve over time. Time is a continuous variable which tends to impact variables in a positive or negative manner. In order to successfully ascertain whether or not null hypotheses (H0) is rejected, a SPSS model called 'Compare Means' has been taken into account. For this purpose, four variables have been taken into account viz. Baseline, Event 1, Event 2 and Event 3. Each of these three events occur on a weekly basis. Thus, giving a timeline of 21 days.

Specifically, in this model, variance, measures of association as well as ANOVA Test has been taken into account. Thus, enabling the researcher to know if there is any kind of variance existent between baseline and event 3. If the variance is less and association among them is small, one can say that the level of attitude towards arts over time has decreased and vice versa (Zohrabi, 2013).

b. Summary of the Analyses

As per the output generated, three tables have been taken into account viz. Case Processing Summary, ANOVA Table and Measures of Association. Through these, one is able to ascertain three important points of information:

  • Number of variables included in the model;
  • Significance of each variable in terms of the dependent variable;
  • Correlation among all the variables.

For all the three cases that assume 'Baseline' as the independent variable and 'Event1, 2 and 3' as dependent variables, no single point of data has been excluded. Thus, the whole of the model is utilised on 93 responses recorded by the team. The ANOVA Table indicates that the p-value for event1*baseline, event2*baseline and event3*baseline is 0.00 which indicates that there is a statistical significance among all the variables. As a result, one can affirm that the attitude does improve towards art over time. The last table indicates that association is highest for Event 2 (R=0.84) and 3 (R=0.84) held in Weeks 2 and 3 respectively. While R=0.83 for event1*baseline.

c. Conclusion

Thus, one can conclude that over time, attitude towards time has been positive with more correlation occurring since baseline. This can be mainly due to the increase in level of awareness that was limited at the Baseline level and gradually increased through the events over the weeks. As a result, null hypotheses (H0) is rejected and alternate hypothesis (H1) is accepted for this scenario.

Conclusion

From the above report, it can be inferred that the Quantitative research methods such as Correlation, Linear Regression Models, Compare Means and T-tests are helpful in deriving meaningful information. This is mainly done through the selection of a sample and processing it so as to extract relevant data for performing further analysis. Thus, enabling the researcher to promptly determine whether the hypotheses formulated by them is achieved or not in a quantifiable manner.

References

Books and Journal

  • Bryman, A. (2017). Quantitative and qualitative research: further reflections on their integration. In Mixing methods: Qualitative and quantitative research (pp. 57-78). Routledge.
  • Hinton, P. R., McMurray, I., & Brownlow, C. (2014). SPSS explained. Routledge.
  • Nardi, P. M. (2018). Doing survey research: A guide to quantitative methods. Routledge.
  • Venkatesh, V., Brown, S. A., & Bala, H. (2013). Bridging the qualitative-quantitative divide: Guidelines for conducting mixed methods research in information systems. MIS quarterly. 21-54.
  • Walliman, N. (2017). Research methods: The basics. Routledge.
  • Zohrabi, M. (2013). Mixed Method Research: Instruments, Validity, Reliability and Reporting Findings. Theory & practice in language studies. 3(2).

Appendices

Question 1

One-Sample Statistics

 

N

Mean

Std. Deviation

Std. Error Mean

WEEK 1

10

9.30

2.584

.817

WEEK 2

10

9.60

4.169

1.318

One-Sample Test

 

Test Value = 0

 

t

df

Sig. (2-tailed)

Mean Difference

95% Confidence Interval of the Difference

 

 

 

 

 

Lower

Upper

WEEK 1

11.381

9

.000

9.300

7.45

11.15

WEEK 2

7.282

9

.000

9.600

6.62

12.58

Question 2

Correlations

 

Engage

DoingArt

FavSubj

Pearson Correlation

Engage

1.000

.

.376

 

DoingArt

.

1.000

.

 

FavSubj

.376

.

1.000

Sig. (1-tailed)

Engage

.

.000

.005

 

DoingArt

.000

.

.000

 

FavSubj

.005

.000

.

N

Engage

47

47

47

 

DoingArt

47

47

47

 

FavSubj

47

47

47

Question 3

Model Summaryb

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

Durbin-Watson

1

.255a

.065

.055

4.194

1.857

  1. Predictors: (Constant), Gender
  2. Dependent Variable: Satis 

ANOVAa

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

111.413

1

111.413

6.335

.014b

 

Residual

1600.286

91

17.586

 

 

 

Total

1711.699

92

 

 

 

  1. Dependent Variable: Satis
  2. Predictors: (Constant), Gender 

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

 

B

Std. Error

Beta

 

 

1

(Constant)

11.920

1.380

 

8.640

.000

 

Gender

2.189

.870

.255

2.517

.014

Question 4

Case Processing Summary

 

Cases

 

Included

Excluded

Total

 

N

Percent

N

Percent

N

Percent

event1  * baseline

93

100.0%

0

0.0%

93

100.0%

event2  * baseline

93

100.0%

0

0.0%

93

100.0%

event3  * baseline

93

100.0%

0

0.0%

93

100.0%

ANOVA Table

 

Mean Square

F

Sig.

event1 * baseline

Between Groups

(Combined)

11546.417

748.930

.000

 

 

Linearity

6533.974

423.810

.000

 

 

Deviation from Linearity

11607.544

752.895

.000

 

Within Groups

15.417

 

 

 

Total

 

 

 

event2 * baseline

Between Groups

(Combined)

11547.291

357.789

.000

 

 

Linearity

6791.327

210.427

.000

 

 

Deviation from Linearity

11605.291

359.587

.000

 

Within Groups

32.274

 

 

 

Total

 

 

 

event3 * baseline

Between Groups

(Combined)

11512.685

425.076

.000

 

 

Linearity

6726.179

248.347

.000

 

 

Deviation from Linearity

11571.057

427.231

.000

 

Within Groups

27.084

 

 

 

Total

 

 

 

 

Measures of Association

 

R

R Squared

Eta

Eta Squared

event1 * baseline

.083

.007

1.000

1.000

event2 * baseline

.084

.007

1.000

1.000

event3 * baseline

.084

.007

1.000

1.000


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