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Submitted By Marigonaa

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Words 2622

Pages 11

Purpose: The aim of this assignment is to test some of the main questions that were raised regarding to customers satisfaction about the services that they received from X Bank.

Methodology: in order to test those questions and to measure customers’ satisfaction, we used SPSS program, which is the most compatible program for social science tests. So, our analyses were focused by using SPSS tools like: Descriptive statistics and Frequencies likewise cross tabulation and chi-square, also independent test and finally correlation.

Findings: Throughout our analyses we were able to find some interesting results regarding to customers opinion for banks services and quality, for instance: There is statistical significant difference when it comes to gender loyalty to the bank, after the chi-square is less than 0.05, also it was clear that there is no correlation between age and perceptions of overall services 0.466. In addition, via our analyses there is a strong correlation between perceptions about service quality and co-operating with the bank in the future 0.867 at the 0.01 level.

Table of Contents

Introduction ……………………………………………………………………………… 3

Data analyses …………………………………………………………………………….. 4

Conclusion ………………………………………………………………………………. 14

Introduction

Nowadays, being able to measure customers’ satisfaction is the main point to develop and sustain business. Organizations which are able to fulfil customers’ needs and wants they achieve to spread their businesses and to increase the number of customers. In this regard, during this assignment we will try to test some important questions related to customers’ satisfaction due to the bank services that they received. It is obvious that as long as we keep our customers to be satisfied with our products and services, they will be loyal to us, and this has been proved especially through economic crises (downturns), who customers never thought to change their supplier. Through this assignment we will be able to be awareness about various issues that concern customers and their evaluation about different services that X Bank provides. So, some of the main questions that will take place are closely related to customers satisfaction with the Bank services that I mentioned above, other questions are linked with gender loyalty to the Bank, also some question tend to measure the correlation between age and some variables regarding to services, and finally some questions are raised in order to measure if there is any possibility for future cooperation with the bank based on the current services that the Bank provides.

Data analyses * Profile the customers that participated in the research (Personal Information variables) - Present a graph for all of them

Gender | | Frequency | Percent | Valid Percent | Cumulative Percent | Valid | Female | 6 | 40.0 | 40.0 | 40.0 | | Male | 9 | 60.0 | 60.0 | 100.0 | | Total | 15 | 100.0 | 100.0 | |

Table 1

As we can see from the table as well as from the graph 40% of customers who participate in this research are female and the remaining 60% are male.

MS | | Frequency | Percent | Valid Percent | Cumulative Percent | Valid | Single | 6 | 40.0 | 40.0 | 40.0 | | Married | 9 | 60.0 | 60.0 | 100.0 | | Total | 15 | 100.0 | 100.0 | |

Table 2

Also, from people who participated in this research the data collection tells us that 40% of respondents were single and the remaining 60% were married.

Education | | Frequency | Percent | Valid Percent | Cumulative Percent | Valid | Some College | 6 | 40.0 | 40.0 | 40.0 | | Completed College | 5 | 33.3 | 33.3 | 73.3 | | Graduate School | 4 | 26.7 | 26.7 | 100.0 | | Total | 15 | 100.0 | 100.0 | |

Table 3

Moreover, our respondents had this level of education: 40% of them had some college, 33.3% of respondents have completed the college and 26.7% were graduate school.

Descriptive Statistics | | N | Minimum | Maximum | Mean | Std. Deviation | age | 15 | 19.00 | 78.00 | 40.8667 | 18.26342 | Valid N (listwise) | 15 | | | | |

Table 4

As you can see from the table above, the minimum age of people who participated in our research is 19 years old, the maximum age of participations is 78 years old, overall mean is 40.86 years old.

Age2 | | Frequency | Percent | Valid Percent | Cumulative Percent | Valid | 1.00 | 5 | 33.3 | 33.3 | 33.3 | | 2.00 | 6 | 40.0 | 40.0 | 73.3 | | 3.00 | 4 | 26.7 | 26.7 | 100.0 | | Total | 15 | 100.0 | 100.0 | |

Table 5

So, 33.3% of people who participated in this research were between 1-30 years old, 40% were between 31-50 years old and 26.7% of respondents are above 51 years old.

* Produce the results for the following question: “How likely it is that I will make business with this bank in the future” Run the mean scores” and “I intend to keep on co-operating with this bank in the future” Run the frequencies and the mean scores

Descriptive Statistics | | N | Minimum | Maximum | Mean | Std. Deviation | i2 | 15 | 1.00 | 6.00 | 3.6667 | 1.91485 | p1 | 15 | 1.00 | 7.00 | 3.5333 | 1.88478 | Valid N (listwise) | 15 | | | | | Table 6 The possibility to continue doing business with this bank is low, because the overall mean is 3.66 out of 6 for i2 question, which is a main signal that bank services are poor and good information for customers that they need to look for another bank to make business with them. If we see frequencies we will not have a clear picture if we should keep on our cooperation with this bank or not, but the mean offers a better picture when it comes to this issue. So, the mean is 3.55 out of 7 for p1 question, which tells us that bank services are low and in the best scenario customers have to cut off their cooperation with this bank due to the future, or in a hopeful scenario they should rethink in terms of keeping on their cooperation with this bank in the future.

* Are there any differences between males and females in terms of frequency of visiting the bank? (loyalty variable)

Gender * loyalty Cross tabulation | | loyalty | Total | | Once per week | Less than once per week | | gender | Female | Count | 5 | 1 | 6 | | | % within gender | 83.3% | 16.7% | 100.0% | | Male | Count | 2 | 7 | 9 | | | % within gender | 22.2% | 77.8% | 100.0% | Total | Count | 7 | 8 | 15 | | % within gender | 46.7% | 53.3% | 100.0% |

Table 7

Chi-Square Tests | | Value | df | Asymp. Sig. (2-sided) | Exact Sig. (2-sided) | Exact Sig. (1-sided) | Pearson Chi-Square | 5.402a | 1 | .020 | | | Continuity Correctionb | 3.225 | 1 | .073 | | | Likelihood Ratio | 5.786 | 1 | .016 | | | Fisher's Exact Test | | | | .041 | .035 | Linear-by-Linear Association | 5.042 | 1 | .025 | | | N of Valid Cases | 15 | | | | |

Table 8

We can see from the Table 7 that 83.3% of females visiting the bank or are loyal to the bank, in the other hand only 22.2% of males visiting the bank. So after the chi-square is lower than 0.05 we have to say that we have statistical evidence to claim that there is gender distinction (Difference) in expressing loyalty to the bank. This means from the Bank point of view that they should do something more for males in order to attract them with their services in order to increase males loyalty to the Bank. * Are there any differences between frequent and non-frequent customers (loyalty variable) in service quality evaluations? (q1, q2, q3, q4) Group Statistics | | loyalty | N | Mean | Std. Deviation | Std. Error Mean | q1 | Once per week | 7 | 6.2857 | .48795 | .18443 | | Less than once per week | 8 | 3.2500 | .70711 | .25000 | q2 | Once per week | 7 | 6.0000 | .81650 | .30861 | | Less than once per week | 8 | 2.6250 | .74402 | .26305 | q3 | Once per week | 7 | 5.8571 | .69007 | .26082 | | Less than once per week | 8 | 3.6250 | 1.68502 | .59574 | q4 | Once per week | 7 | 3.5714 | .53452 | .20203 | | Less than once per week | 8 | 2.3750 | .51755 | .18298 |

Table 8 Independent Samples Test | | Levene's Test for Equality of Variances | t-test for Equality of Means | | F | Sig. | t | df | Sig. (2-tailed) | Mean Difference | Std. Error Difference | 95% Confidence Interval of the Difference | | | | | | | | | Lower | Upper | q1 | Equal variances assumed | .939 | .350 | 9.526 | 13 | .000 | 3.03571 | .31867 | 2.34727 | 3.72416 | | Equal variances not assumed | | | 9.772 | 12.406 | .000 | 3.03571 | .31067 | 2.36128 | 3.71015 | q2 | Equal variances assumed | .057 | .816 | 8.379 | 13 | .000 | 3.37500 | .40281 | 2.50477 | 4.24523 | | Equal variances not assumed | | | 8.323 | 12.314 | .000 | 3.37500 | .40551 | 2.49397 | 4.25603 | q3 | Equal variances assumed | 12.256 | .004 | 3.262 | 13 | .006 | 2.23214 | .68438 | .75362 | 3.71067 | | Equal variances not assumed | | | 3.432 | 9.532 | .007 | 2.23214 | .65034 | .77340 | 3.69088 | q4 | Equal variances assumed | .141 | .713 | 4.399 | 13 | .001 | 1.19643 | .27195 | .60892 | 1.78393 | | Equal variances not assumed | | | 4.389 | 12.609 | .001 | 1.19643 | .27258 | .60570 | 1.78716 |

Table 9

Based on the data analyses we can say that there is no difference between frequent and non-frequent customers when it comes to q1, q2, q3, q4 questions. The mean score is low from 1.19 to 3.75, which means that customers evaluate all these questions with negative opinion regarding to bank services that they provide, which is a signal to the bank that customers are dissatisfied with their services that they provide, and obviously they have to take further steps in order to improve their current services.

* Are there any age related differences in terms of service quality evaluations? (q1, q2, q3, q4)

Correlations | | q1 | q2 | q3 | q4 | age | q1 | Pearson Correlation | 1 | .876** | .504 | .782** | .456 | | Sig. (2-tailed) | | .000 | .055 | .001 | .088 | | N | 15 | 15 | 15 | 15 | 15 | q2 | Pearson Correlation | .876** | 1 | .701** | .716** | .539* | | Sig. (2-tailed) | .000 | | .004 | .003 | .038 | | N | 15 | 15 | 15 | 15 | 15 | q3 | Pearson Correlation | .504 | .701** | 1 | .451 | .704** | | Sig. (2-tailed) | .055 | .004 | | .092 | .003 | | N | 15 | 15 | 15 | 15 | 15 | q4 | Pearson Correlation | .782** | .716** | .451 | 1 | .357 | | Sig. (2-tailed) | .001 | .003 | .092 | | .192 | | N | 15 | 15 | 15 | 15 | 15 | age | Pearson Correlation | .456 | .539* | .704** | .357 | 1 | | Sig. (2-tailed) | .088 | .038 | .003 | .192 | | | N | 15 | 15 | 15 | 15 | 15 | Table 10**. Correlation is significant at the 0.01 level (2-tailed). | *. Correlation is significant at the 0.05 level (2-tailed). | Based on the correlation test, we can say that there is no correlation between age and q1 as well as q4 questions, because the correlation is 0.456 and 0.357 respectively, but in the other hand we can say that there is a strong correlation between age and q2, q3 questions, because the correlation for q2 is 0.539 at the 0.05 level and it is strong correlation 0.704 for q4 at the 0.01 level. * Is age related with perceptions of overall service quality (variable “qt”)?

Correlations | | age | qt | age | Pearson Correlation | 1 | .466 | | Sig. (2-tailed) | | .080 | | N | 15 | 15 | qt | Pearson Correlation | .466 | 1 | | Sig. (2-tailed) | .080 | | | N | 15 | 15 |

Table 11

As can be seen from Table 11 correlation test, the correlation is 0.466 so we claim that there is no correlation between age with perceptions of overall service quality.

* Are the perceptions about the overall service quality (“qt”) related to intentions to keep on co-operating with the bank in the future?

Correlations | | qt | p1 | qt | Pearson Correlation | 1 | .867** | | Sig. (2-tailed) | | .000 | | N | 15 | 15 | p1 | Pearson Correlation | .867** | 1 | | Sig. (2-tailed) | .000 | | | N | 15 | 15 | Table 12**. Correlation is significant at the 0.01 level (2-tailed). |

The correlation test provides the result 0.867 at the 0.01 level, which we can truly claim that there is strong correlation between the perception of overall service quality related with the intentions to keep on the collaborating with the bank in the future.

* Are perceptions of overall service quality (variable “qt”) related with questions q1, q2, q3, q4, q5, and q6?

Correlations | | qt | q1 | q2 | q3 | q4 | q5 | q6 | qt | Pearson Correlation | 1 | .918** | .887** | .589* | .846** | .757** | .589* | | Sig. (2-tailed) | | .000 | .000 | .021 | .000 | .001 | .021 | | N | 15 | 15 | 15 | 15 | 15 | 15 | 15 | q1 | Pearson Correlation | .918** | 1 | .876** | .504 | .782** | .636* | .539* | | Sig. (2-tailed) | .000 | | .000 | .055 | .001 | .011 | .038 | | N | 15 | 15 | 15 | 15 | 15 | 15 | 15 | q2 | Pearson Correlation | .887** | .876** | 1 | .701** | .716** | .642** | .463 | | Sig. (2-tailed) | .000 | .000 | | .004 | .003 | .010 | .082 | | N | 15 | 15 | 15 | 15 | 15 | 15 | 15 | q3 | Pearson Correlation | .589* | .504 | .701** | 1 | .451 | .530* | -.071 | | Sig. (2-tailed) | .021 | .055 | .004 | | .092 | .042 | .801 | | N | 15 | 15 | 15 | 15 | 15 | 15 | 15 | q4 | Pearson Correlation | .846** | .782** | .716** | .451 | 1 | .599* | .519* | | Sig. (2-tailed) | .000 | .001 | .003 | .092 | | .018 | .047 | | N | 15 | 15 | 15 | 15 | 15 | 15 | 15 | q5 | Pearson Correlation | .757** | .636* | .642** | .530* | .599* | 1 | .373 | | Sig. (2-tailed) | .001 | .011 | .010 | .042 | .018 | | .171 | | N | 15 | 15 | 15 | 15 | 15 | 15 | 15 | q6 | Pearson Correlation | .589* | .539* | .463 | -.071 | .519* | .373 | 1 | | Sig. (2-tailed) | .021 | .038 | .082 | .801 | .047 | .171 | | | N | 15 | 15 | 15 | 15 | 15 | 15 | 15 | Table 13**. Correlation is significant at the 0.01 level (2-tailed). | *. Correlation is significant at the 0.05 level (2-tailed). |

As it can be seen from Table 13, we can claim that there is strong correlation between the perceptions of overall quality (qt) related with the questions q1, q2, q3, q4, q5, q6. Moreover there are strong correlations with questions q1, q2, q4, q5 at the 0.01 level, likewise it is strong correlation with questions q3, q6 at the 0.05 level.

Conclusion

Throughout this analyses that we have done through SPSS we found some interesting points that drives us to make some conclusion regarding to different issues that we discussed above. So, the first point that we can say is that the majority of people that participated in this survey were male 60%; also it was clear that 60% of respondents were married and the remaining 40% were single. Also, the majority of participations had some college 40%, some of them had completed college 33.3% and 26.7% were graduate school. Moreover, 33.3% of respondents were between 1-30 years old, 40% between 31-50 years old and the rest above 50 years old. Furthermore, throughout our analyses we were able to understand that the possibility to make business in the future with the bank is low after the mean score 3.66 out of 6, and the possibility to develop collaboration with the bank in the future is impossible after the mean score 3.53 out of 7, which tells us that the bank is not compatible for further cooperation. In addition, we can say based on our analyses that there is a statistical difference when it comes to gender loyalty to the bank, after the chi-square were less than 0.05. Likewise, via our tests it was impossible to find differences between frequent and non-frequent customers’ loyalty when it comes to questions q1, q2, q3, q4. Furthermore, we found that there is strong correlation between age and q2 and q3 questions, 0.539 and 0.704 respectively at the 0.01 level, but in the other hand there is no correlation between age and perceptions of overall service quality (“qt”) 0.466. All in all, the correlation between perceptions about service quality and co-operating with the bank in the future is strong 0.867 at the 0.01 level, and through data analyses there is evidence that the correlation between perceptions of overall service quality is strong with all those questions: q1, q2, q3, q4, q5, q6 questions.…...

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...This final paper project is based on the data calculated using SPSS software. SPSS is the software that can give confidence predictive results of what will happen next so we can make smarter decision, solve problems and improve outcomes. For this project, I am going to use SPSS as my analyzing tool to predict and analysis the data by using several model of calculation. The database I choose is the Employee Attitudes data provided under course material. This is my interested field and I am going to use SPSS to make a prediction. Data Source: EmployeeAttitudesStudents.sav from course material. This database contains information collected in a survey of nearly 1,000 employees of Seminole County Government, Florida. Questions regarding working conditions, compensation, quality of management, etc were asked. Variables in this file are all categorical (mostly ordinal, with a couple of nominal) so they would be appropriate from multiple regression analysis. Statement of Problem According to the research material I found online, Salary Basics - Developing a Strong Compensation Philosophy, good payment attract and retain employees for the company. In order to analysis this situation, I choose two variables as my main factor. The first one is the question "I am paid as well as other organizations with similar jobs", and the second question is "Years worked for Seminole Country Government". So here H0 null hypothesis is that Pay rate compared with other company does not affect......

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...UQ Business School MKTG7510 Market and Consumer Research Introduction to SPSS Course Coordinator: Dr Ravi Pappu Introduction to SPSS This is a brief on how to use SPSS. The purpose of this chapter is to introduce you to SPSS. You are encouraged to actively access the interactive help of SPSS and ask your tutor questions if you get lost. You are expected to spend time outside of class (i) familiarising yourself with SPSS and (ii) completing the homework exercises to complete the Assignment 2 (Research Report) for assessment (20% of your MKTG7510 Grade). SPSS is accessible in all computer labs in Building 39A and 35. Please check current times of access to the computer labs and plan your study time accordingly. 1. How to create a new project? (1) Click on the ‘Start’ menu. Click on ‘All Programs’. (2) You will find the folder ‘Data Analysis and Stats Programs’ - click it to open the folder. (3) Click on the ‘IBM SPSS statistics’ folder. Click on the ‘IBM SPSS Statistics’ icon. (4) Once SPSS is open, the screen (see Fig. 1) asks you what you would like to do: either type in the data manually, or find an existing data file (as circled below). Figure 1: Create a New Project MKTG7510 Market and Consumer Research S1-2014 P. 1/20 2. What are the main components of an SPSS ‘project’? When you open a new ‘project’ you will see the Data Editor window where the raw data are visible. However, there are multiple windows that allow you to interact with the......

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...равно мат. ожиданию количества лет обучения по всей Европе, а именно 12 годам) С помощью SPSS проводим t-test для одной переменной Уровень значимости равен 0,000 , соответственно значение меньше α, поэтому мы отвергаем нашу статистическую гипотезу H0, то есть с вероятностью 95% среднее количество лет обучения в Дании и Германии не равно среднему количеству в целом по Европе. Из полученных данных мы видим, что среднее значение для Дании и Германии составляет 13,2 лет, что заметно отличается от 12. Вывод: с вероятностью 95% гипотеза о том, что среднее количество лет обучения в Германии и Дании одинаково с средним по Европе, не подтвердилась: средние значение отличаются, если точнее в Дании и Германии это значение в среднем выше на 1,2 лет. Задание 2. Различается ли средний уровень доверия полиции среди мужчин и женщин? Задача исследования: определить, различается ли средний уровень доверия полиции среди мужчин и женщин Дании и Германии Содержательная гипотеза: среди мужчин и женщин Дании и Германии средний уровень доверия одинаковый Для решения мы будем использовать T-test для независимых выборок, т.к. процедура отбора единиц в первую выборку(мужчины) никак не связана с процедурой отбора единиц во вторую выборку(женщин). Статистическая гипотеза: H0: μ1=μ2 (мат. ожидание уровня доверия полиции мужчин равно мат. ожиданию уровня доверия женщин) С помощью SPSS проводим t-test для независимых переменных Из полученных данных мы видим, что......

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... UNS/CASS DBA Program Subject: Assignment of the Quantitative and Qualitative Research Methodology Lecturer: Name: Submission Date: SPSS Assignment Suppose we have captured 1,500 records from Century 21 Real Estate Company. They have recorded the properties’ market values at Dec 31, 2010 & Dec 31, 2011. 1. Draw a histogram with properties’ building footages, and their market value in 2011. Discuss the characteristics of those 2 distributions. 2. Test whether the average property market value in 2011 is significantly different from $4,750,000 at 1% level of significance. 3. Suppose the 1,500 properties are separated into 2 districts: T.S.T. (Group 1) & Jordan (Group2). Test whether there is any difference between the 2 districts in year of 2010 at 1% level of significance (Assume Equal Variances). 4. Test whether the average property market value in 2010 is significantly different from those in 2011 at 3% level of significance. Answers: 1. Draw a histogram with properties’ building footages, and their market value in 2011. Discuss the characteristics of those 2 distributions. Answer: The histograms, one shows the properties by their market values while another by their sizes in 2011 are showed below. The values of skewness and kurtosis of the first histogram in Figure 1.1 (that is, by the property market values) are 0.201 and -1.112 respectively, while the values for a normal distribution should be both zero (Table 1.1). The......

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...Assignment 1 The aim of this assignment is for you to practise using SPSS to analyse data. You should attempt each of the questions detailed on the following pages using the SPSS Survival Manual as your guide. You will need to have read through the SPSS Survival Manual first to familiarise yourself with its contents and to revise the various statistical procedures covered. You will be asked to interpret some output generated by SPSS. For a number of the questions in this exercise you are required to report the results of analyses performed on the data file. These should be formatted as you would present them in a research report. Full details should be provided concerning the analyses performed, assumptions that were checked, and the results obtained. The more practice you get with this process, the easier it will be for you to write up the results of your analyses. Details on how to structure a report are available on AUTonline. Part A 1. A market researcher is interested in the coffee drinking habits of males and females. He asks a sample of male and female office workers to record the number of cups of coffee they consume during a week. (a) Which parametric statistical technique could the researcher use to determine if males and females differ in terms of the number of cups of coffee consumed in a week? Justify your answer and describe how you would obtain this statistic using SPSS. Independent-samples t-tests (b) What are the key values you would......

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...SPSS Statistical Package for the Social Sciences (SPSS) is a quantitative analysis software. It is a great tool for quantitative data. It can handle complex data manipulations and analyses. SPSS is among the most widely used programs for statistical analysis in social science. SPSS is the software for the numerical analysis. SPSS was acquired by IBM in 2009 for $1.2 billion. SPSS is a great for predictive analysis to help your organization anticipate change so that you can plan and carry out strategies that improve outcomes. Predictive analysis has come of age as a core enterprise practice necessary to sustain competitive advantage. By applying predictive analytics solutions to data you already have, your organization can uncover unexpected patterns and associations and develop models to guide front-line interactions. This means you can prevent high-value customers from leaving, sell additional services to current customers, develop successful products more efficiently, or identify and minimize fraud and risk. Predictive analytics gives you the knowledge to predict…and the power to act. Enterprise data is a priceless strategic asset because it represents the aggregate experience of an organization, the very history of its interactions with customers. Each customer response (or lack thereof), purchase decision, acquisition, outright defection, act of fraud, credit default, and complaint of a faulty product component provides the enterprise experience from which to......

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...Marketing Research Fall 2011 Exercise: SPSS 5. Hypothesis test The MBA programme leader is interested to know if there is any significant average age difference between males and females and if there is which is the older group. a. Suggest a null hypothesis and an alternative hypothesis for testing the mean age for male and female students. μ0: The average ages of males and females are the same. μ1: The average ages of males and females are not the same. b. Carry out an appropriate test to compare the mean age for the two sexes, and interpret your results. Since the goal is to compare two means and that the data is of ratio scale, One-Way ANOVA is the appropriate test. Here we have gender as the factor and age as the dependent variable, and we choose the common 0.05 level of significance. Figure 5.1 is the resulting ANOVA table. | | | | | | |3.131a |2 |.209 | | |3.433 |2 |.180 | | |.543 |1 |.461 | | |40 | | | Figure 6.1 Cross table of satisfaction and sex at α=0.05 The p-value, which is 0.209, is very obviously greater than our chosen level of significance, 0.05. The null hypothesis...

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