Free Essay

Submitted By Marigonaa

Words 2622

Pages 11

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.

Premium Essay

...Demonstration for solving the homework problem assigned on Jan 20. Use SPSS to compute the mean, median, standard deviation, and standard error of the mean for the following set of data. Report your results in an APA-style table of descriptive statistics. | |Score | | | | |Group 1 |4 4 5 6 4 | | | | |Group 2 |2 4 4 3 4 | | | | |Group 3 |6 4 4 5 6 | One of the first things you needed to do was figure out how to enter the data. Remember, on the SPSS data spreadsheet, rows contain cases, and columns contain variables. So, how many variables to we have. Certainly Score is a variable. Are there any other variables? Some careful thinking (also spending some time working through the various tutorials available) should have lead you to a realization that we need a group identifying variable, Group, say. So, the first thing I do, after starting SPSS, is to go to the Variable View tab (at the bottom of the SPSS Data Editor) and enter my two variable names (see Figure 1 below). |[pic] | |Figure 1 | Next, I switch to the Data View tab and enter the data for each variable (Figure 2). Everyone in group 1 gets a 1 under Group, those in...

Words: 889 - Pages: 4

Free Essay

...SPSS 1 (13-11-2012) Definitie SPSS: Statistical Package for the Social Sciences, software om data in te voeren en te analyseren Variable view: opzet van de variabelen Name: naam variabele (bijv. vraag1), let op: geen spaties! Type: soort variabele, dus numeric (getal) en string (tekst, bij open vragen) Width: aantal tekens/cijfers (bij open vraag hoger dan gesloten vraag) Decimals: decimalen worden bijna nooit gebruikt, dus 0 Label: vraagstelling Values: codes voor variabelen (bijv. 0 nee, 1 ja) Missing: respondenten zien wel eens een vraag over het hoofd, wanneer je 99 invult bij missing houdt SPSS er bij de analyse rekening mee dat een vraag die niet is ingevuld gecodeerd wordt met 99 Colums: weergaveaspect, breed of smal Align: weergaveaspect, rechts links of midden uitlijnen Measure: meetniveau (ordinal, nominal, scale) Role: niet belangrijk, input Anders, nl voer je apart in (open vraag, dus string) Data view: daadwerkelijke gegevens (data van ingevulde enquêtes) Rijen vragen komen terug in kolommen, elke rij met een nummer (case nummer) is een respondent Value labels: laat zien in de data view wat de cijfers voor betekenis hebben Meetniveaus - Nominaal: nomen = ‘naam’, meetwaarden die benoemd worden (elk getal een naam, bijv. 0 = VVD, 1 = CDA), weergave circeldiagram - Ordinaal: nominaal met rangorde (Likertschaal, zit een opbouw in: zeer mee eens, mee eens, neutraal, niet mee eens), weergave staafdiagram - Interval: getallen, getallen......

Words: 377 - Pages: 2

Premium Essay

...EMBA Advanced Market Analysis and Strategy - MAR 6087 SPSS Analysis PulteGroup, Inc. – South Florida Division By: Scott Mairn and Irina Prell November 30, 2012 Table of Contents I. Executive Summary II. Company Overview III. Research Objectives/Approach IV. Sampling Description V. Data Analysis and Interpretation VI. Implications VII. Conclusion and Future Direction Executive Summary This report will take a look the results from the Servqual survey research conducted for the PulteGroup, Inc. South Florida Division to analyze their service in September of 2012. The objective of this research is to assess how the 5 factors of the Servqual survey affect the homeowners’ attitude towards service in PulteGroup communities. Each homeowner was asked questions consistent with the intentional model of Servqual instruments which are designed to measure reliability, responsiveness, assurance, apathy, and tangibles of a service. SPSS software by IBM was used to analyze the results. The following methodologies were utilized: • Attitudinal Scores • Heavy Half Theory • T-test • Factor Analysis • Multiple Regression Analysis The third party management companies employed by PulteGroup in two communities, VillageWalk of Bonita Springs and VeronaWalk of Naples, delivered the survey to homeowners. We received 34 fully completed surveys, which were fully analyzed. The results showed a wide range in attitude with scores ranging from 125 to 725.......

Words: 2304 - Pages: 10

Premium Essay

...Comandos SPSS * Análise Fatorial: ANALYZE DESCRIPTIVE EXPLORE * Normality plots with tests Analyze Dimension reduction Factor Analysis Descriptives * Initial solution * Coefficients * KMO and Bartlett´s test of sphericity * Anti-image Extraction * Correlation matrix * Unrotated factor solution * Scree plot * Fixed number of factors * Maximum Iterations for Convergence Rotation * Direct Oblimin * Loading Plot Scores * Save as variables * Regression * Display factor score coefficient matrix TRANSFORM COMPUTE * Target Variable * Numeric Expression * Function group – All * Functions and Special Variables - Ln * Conglomerados: Graphs Legacy dialogs Scatter/Dot Simple * Y Axis * X Axis * Label Cases by Options * Exclude cases listwise * Display chart with case labels Analyze Classify Hierarquical Cluster Variables (s) Label Cases by Cluster * Cases Display * Statistics * Plots Statistics * Agglomeration schedule * Proximity matrix * None Plots * Dendogram * None Method * Nearest neighbor * Interval – Squared Euclidean distance * None Save * Single solution – Number of cluesters Analyze – Descriptive – Explore * Dependent List * Factor List * Display – Both Analyze – Classify – Kmeans * Variables * Label Cases by * Number of Clusters * Method – Iterate and......

Words: 340 - Pages: 2

Premium Essay

...Which job category has the largest mean beginning salary? ______________________________ b. Which job category has the smallest mean beginning salary? _____________________________ 3. Use the MEANS procedure to compute median beginning salaries (SALBEG) for people of different genders and races (SEXRACE). a. What is the median beginning salary for white males? ______________________________________ b. What is the median beginning salary for minority females? _________________________________ c. What is the median beginning salary for white females with 16 years of education? _____________ d. What is the median beginning salary for minority males with 16 years of education? ____________ Note: Questions 3, 4, and 5 below do not involve SPSS. You will need to consult the “Table of Areas Under the Normal Curve” handout. 3. Assume grades on an exam are normally distributed with a mean of 70 and standard deviation of 8. a. What would be the z-score for a student who received 64 on this exam? ______________________ b. How many standard deviations away from the mean is this score? ________________________ c. What would be the z-score for a student who received 81 on this exam? ______________________ d. How many standard deviations away from the mean is this score? ________________________ e. What percentage of students scored less than 64 on this exam? ________________________ f. What percentage of students scored greater than 81 on this exam? ________________________ g. What......

Words: 791 - Pages: 4

Free Essay

...เริ่มต้นทำงานกับโปรแกรม SPSS การเข้าสู่การทำงานของโปรแกรม SPSS for Windows(Statistical Package for the Social Sciences ) วิธีที่ 1. คลิก ปุ่ม Start >> All Program >> Spss for Windows >> Spss 12.0 for Windows วิธีที่ 2. เข้าสู่โปรแกรมโดยคลิกชื่อโปรแกรมบน Start Menu หรือ บน Desktop หน้าจอแรกของโปรแกรม สอนการใช้งาน พิมพ์ข้อมูลใหม่ เปิดแฟ้มข้อมูลชนิด Database Query(*.spq) สร้าง query ใหม่โดยใช้ Database Wizard เปิดแฟ้มข้อมูลชนิด SPSS (*.sav) หรือเลือกที่ 3 เปิดแฟ้มข้อมอื่นๆ (*) หรือเลือกที่ 4 ไม่ต้องการปรากฏหน้าจอเริ่มต้นการทำงานนี้อีก โปรแกรม SPSS เป็นโปรแกรมที่สร้างขึ้นมาเพื่อวิเคราะห์ข้อมูลทางสถิติโดยตรง หน้าตาโปรแกรม SPSS ดังภาพข้างล่าง ชีต สำหรับการลงข้อมูลจากแบบสอบถาม โดยในแนวคอลัมน์คือ ตัวแปรหนึ่ง ๆ ชีต สำหรับกาสร้าง ตัวแปรจากแบบสอบถาม Variable View เป็นหน้าต่างข้อมูลสำหรับการสร้างและแก้ไขตัวแปรดังภาพข้างล่าง Name = ชื่อตัวแปร Type = ชนิดของตัวแปร * Numeric สำหรับข้อมูลตัวเลขบวกหรือลบก็ได้ * Comma สำหรับข้อมูลตัวเลข กรณีค่าเกินหลักพันจะมีเครื่องหมาย , คั่น * Dot สำหรับข้อมูลตัวเลข กรณีค่าเกินหลักพันจะมีเครื่องหมาย , คั่น และก่อนทศนิยมเป็นเครื่องหมาย , * Scientific notation สำหรับข้อมูลตัวเลข และมีการใช้สัญลักษณ์ทางคณิตศาสตร์ เช่น E ตัวอย่างเช่น 9.05E+01 -3E+10 4.3E+04 เป็นต้น * Data สำหรับข้อมูลที่เป็นวันที่ * Dollar สำหรับข้อมูลที่เนรูปตัวเงิน จะมีเครื่องหมาย $ นำหน้า * Custom ......

Words: 3214 - Pages: 13

Premium Essay

...Introduction to SPSS INTRODUCTION TO SPSS Dr. Sarimah binti Abdullah. M.D(USM) MCommMed.(Epid & Biostat) (USM) • SPSS environment • Describe the menu options & toolbars Unit of Biostatistics & Research Methodology Universiti Sains Malaysia SPSS Environment • Data editor – Data view – Variable view • Viewer - output • Pivot table editor • Chart editor • Text output editor • Syntax editor • Script editor Data editor - Data view - Variable view Viewer – output – Display all statistical result • Tables • Charts – Allow to edit – Save – Access to • pivot table editor • Text output editor • Chart editor • Move between SPSS and other application Viewer - output Pivot table editor 1 Chart editor • Edit – – – – – Colour Font Axes Rotation Chart type ENTER VARIABLES ENTER VARIABLES ENTER VARIABLES ENTER DATA 2 ENTER DATA SAVE EDIT VIEW SELECT CASES 1 3 2 6 4 5 7 3 TRANSFORM : Compute ALL CASES DELETE BMI = wt(kg)/ht(m)2 TRANSFORM : Recode BMI Categorize TRANSFORM : Recode BMI_gp : 1. Underweight =23 BMI_gp 1. Underweight 2. Normal 3. Overweight 1 DESCRIPTIVE STATISTICS 6th basic data 2 2 3 9 4 6 8 5 7 4 OUTPUT Statistics N Valid Missing gender 10 0 race 10 0 gender Frequency 5 5 10 Percent 50.0 50.0 100.0 race Frequency 6 3 1 10 Percent 60.0 30.0 10.0 100.0 Valid Percent 60.0 30.0 10.0 100.0 Cumulative Percent 60.0 90.0 100.0 IHD 10 0 BMI_gp 10...

Words: 930 - Pages: 4

Premium Essay

...Interpretation of SPSS output for Car Care Inc. Jacquelynn Patterson Liberty University Online Professor Lingley Busi 331-B01 October 7, 2013 1.0 Introduction Statistics in social sciences are an important aspect in making the understanding of social behaviors plausible in organizations, governments, marketers and other cohorts with same interest. Initially, statistical manipulations were conducted manually and obliged researchers to have formulas at their fingertips. This strenuous exercise was susceptible to shortcomings in case large volumes of data were to be analyzed. In addition, manual calculations depend on human nature that is vulnerable to ill health, emotional exhaustion and fatigue. As a result, there are many chances of making errors when dealing with manual calculations. This would finally affect the end results obtained. The above mentioned problems are likely to be amplified especially when dealing with a huge number of research subjects. If this is the case, it implies that marketing research data analysis would be the most vulnerable if manual statistical manipulations were embraced. This is because marketing research depends heavily on many respondents in order for the results to valid and reliable for making inferences to the whole population. Inevitably, marketers are bludgeoned into using statistical software that can handle large volumes of cases in a single command. This does not only......

Words: 1198 - Pages: 5

Premium Essay

...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......

Words: 821 - Pages: 4

Premium Essay

...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......

Words: 3013 - Pages: 13

Free Essay

...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......

Words: 1570 - Pages: 7

Premium Essay

... 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......

Words: 1684 - Pages: 7

Premium Essay

...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......

Words: 369 - Pages: 2

Premium Essay

...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......

Words: 532 - Pages: 3

Premium Essay

...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...

Words: 659 - Pages: 3