1 277 CHAPTER VI COMPARISONS OF CUSTOMER LOYALTY: PUBLIC & PRIVATE INSURANCE COMPANIES. This chapter contains a full discussion of customer loyalty comparisons between private and public insurance companies of Ethiopia, and evaluation of the results with reference to literatures. Thus, the discussions goes on in two stages : At first, respondents profile presents in order to make comparisons of sample profile between public and private insurance sector, secondly, Relationships between demographic factors and customer loyalty; Determinant factors and customer loyalty and Correlation analysis are presented and thirdly, test for the level of loyalty comparisons between public and private insurance companies is presented. 6.1 Respondents profile in public & private sectors This section presents the respondents' profile by gender group, age group, marital status, education level, current occupation, income group, and how respondents attracted for purchasing policies. For the purpose of comparisons of customer loyalty in public and private insurance sectors respondents attitudes, opinions, and experiences were collected from both public and private insurance sectors. And then inferences are made based on responses and statistical test results. Thus, comparison of sample profile between public and private insurance sector are presented in the following paragraphs.
2 278 Table 6.1: Comparisons of sample profile: Sector * Gender distribution Gender of respondents Female Male Total Sector Public Count % within Sector 27.5% 72.5% 100.0% % within sex of respondents 18.3% 20.7% 20.0% % of Total 5.5% 14.5% 20.0% Private Count % within Sector 30.6% 69.4% 100.0% % within sex of respondents 81.7% 79.3% 80.0% % of Total 24.5% 55.5% 80.0% Total Count % within Sector 30.0% 70.0% 100.0% % within sex of respondents 100.0% 100.0% 100.0% % of Total 30.0% 70.0% 100.0% As the above table exhibits relatively large number of females of private sector (24.5%) are participated than public ((5.5%). From the total participants (30%) are female while (70%) are male respondents. Table 6.2: Comparisons of sample profile : Sector * age distribution age of respondents under or above Total Sector Public Count % within Sector 4.0% 43.5% 43.5% 9.0% 100.0% % within age of respondents 100.0% 22.8% 17.9% 14.5% 20.0% % of Total.8% 8.7% 8.7% 1.8% 20.0% Private Count % within Sector.0% 36.8% 50.0% 13.2% 100.0% % within age of respondents.0% 77.2% 82.1% 85.5% 80.0% % of Total.0% 29.4% 40.0% 10.6% 80.0% Total Count % within Sector.8% 38.1% 48.7% 12.4% 100.0% % within age of respondents 100.0% 100.0% 100.0% 100.0% 100.0% % of Total.8% 38.1% 48.7% 12.4% 100.0%
3 279 The above table reports that (.8%) of the young respondents are participated in public where there is no in private insurance sector. On the other hand, oldest participants are from private insurance sector (10.65) when in public sector is (1.8%). Table 6.3: Comparisons of sample profile : Sector * Marital status Marital status Single Married Others Total Sector Public Count % within Sector 4.5% 76.0% 19.5% 100.0% % within Marital status 31.0% 18.8% 23.8% 20.0% % of Total.9% 15.2% 3.9% 20.0% Private Count % within Sector 2.5% 81.9% 15.6% 100.0% % within Marital status 69.0% 81.2% 76.2% 80.0% % of Total 2.0% 65.5% 12.5% 80.0% Total Count % within Sector 2.9% 80.7% 16.4% 100.0% % within Marital status 100.0% 100.0% 100.0% 100.0% % of Total 2.9% 80.7% 16.4% 100.0% As the above table shows (65.5%) of the married participants are from private sector while (15.2%) married participants are from public insurance sector. Table 6.4: Comparisons of sample profile : Sector * Education level High school or less Education level of respondents undergradua te post graduate or above Sector Public Count Total % within Sector 48.5% 36.0% 15.5% 100.0% % within Education level of respondents 21.1% 19.8% 17.4% 20.0% % of Total 9.7% 7.2% 3.1% 20.0% Private Count % within Sector 45.2% 36.4% 18.4% 100.0% % within Education level of respondents 78.9% 80.2% 82.6% 80.0% % of Total 36.2% 29.1% 14.7% 80.0% Total Count % within Sector 45.9% 36.3% 17.8% 100.0% % within Education level of respondents 100.0% 100.0% 100.0% 100.0% % of Total 45.9% 36.3% 17.8% 100.0%
4 280 The table above reports that (14.7%) highly qualified participants is from private insurance sector when (3.1%) 0f the participants are from public insurance sector. Table 6.5; Comparisons of sample profile: Sector * current job distribution current job Business class Salary class Others Total Sector Public Count % within Sector 40.0% 50.0% 10.0% 100.0% % within current job 17.2% 22.9% 20.4% 20.0% % of Total 8.0% 10.0% 2.0% 20.0% Private Count % within Sector 48.2% 42.0% 9.8% 100.0% % within current job 82.8% 77.1% 79.6% 80.0% % of Total 38.6% 33.6% 7.8% 80.0% Total Count % within Sector 46.6% 43.6% 9.8% 100.0% % within current job 100.0% 100.0% 100.0% 100.0% % of Total 46.6% 43.6% 9.8% 100.0% As the above table shows (38.6%) of participants of the business class are from private insurance sector where (8%) are from public sector. Table 6.6: Comparisons of sample profile : Sector *respondents monthly income Less than Birr 5,000 Monthly disposal income Birr ,000 Birr 10,001-15,000 15,001 and above Sector Public Count Total % within Sector 9.5% 54.0% 32.5% 4.0% 100.0% % within Monthly income 23.5% 23.7% 20.0% 5.8% 20.0% % of Total 1.9% 10.8% 6.5%.8% 20.0% Private Count % within Sector 7.8% 43.4% 32.5% 16.4% 100.0% % within Monthly disposal income 76.5% 76.3% 80.0% 94.2% 80.0% % of Total 6.2% 34.7% 26.0% 13.1% 80.0% Total Count % within Sector 8.1% 45.5% 32.5% 13.9% 100.0% % within Monthly disposal income 100.0% 100.0% 100.0% 100.0% 100.0% % of Total 8.1% 45.5% 32.5% 13.9% 100.0%
5 281 The above table exhibits that (6.2%) less income people are from private sector while (1.9%) is from public income. Table 6.7: Comparisons of sample profile : Sector * How respondents attracted for purchasing policies Friends, relatives, etc. How respondents learn purchasing policies Advertisement s Government/cooper ative advice others Sector Public Count Total % within Sector 36.0% 23.0% 25.0% 16.0% 100.0% % within How respondents learn purchasing policies 22.5% 18.9% 21.2% 16.0% 20.0% % of Total 7.2% 4.6% 5.0% 3.2% 20.0% Private Count % within Sector 31.0% 24.8% 23.2% 21.0% 100.0% % within How respondents learn purchasing policies 77.5% 81.1% 78.8% 84.0% 80.0% % of Total 24.8% 19.8% 18.6% 16.8% 80.0% Total Count % within Sector 32.0% 24.4% 23.6% 20.0% 100.0% % within How respondents learn purchasing policies 100.0% 100.0% 100.0% 100.0% 100.0% % of Total 32.0% 24.4% 23.6% 20.0% 100.0% As the above table reports most participants of private insurance sector (19.8%) are attracted by insurance advertisements while large number of respondents from public insurance sector (7.2%) is attracted by their friends, relatives and acquaintances. 6.2 Analyzing Relationships Relationships deals to a connection between two or more factors which can be measured statistically, so that we can obtain information about the correspondence of two variables. Variables may be independent or dependent to each other. Variables may not have relationship in one situation while in the other situation one variable may be a cause for the other variable and go in the same direction or in the opposite
6 282 directions. Thus, in this section statistical test is conducted if there is a relationship exists between Demographic factors and customer loyalty, between determinant factors and customer loyalty in public and private insurance Chi-square analysis: Respondents demographic particulars to loyalty in private and public insurance companies. In this section attempts are made to explore the relationship between customers personal profiles and loyalty both in public and private insurance companies. In order to know the variation between demographic factors and loyalty each of the variable respondents response scores are computed as: strongly dissatisfied and dissatisfied come under low category, neither agree nor disagree come under medium and agree and strongly agree put under high. Chi-square analysis is used to identify the significant relationships between demographic factors and loyalty. Chi-square analysis helps us to make decisions about which one reflects fluke differences between demographic factors and loyalty and which one reflects true differences. The demographic variables chosen for this analysis are: respondents Sex, age, income, education level, occupation, and marital status. It was hypothesized that: H o1 : Customers Demographic factors are not related to loyalty in insurance H 11 : Customers Demographic factors are related to loyalty in insurance Ho1a: Customers gender is not related to r loyalty in insurance Ho1 b : Customers Marital status is not related to loyalty in insurance Ho1 c : Customers age is not related to customer loyalty in insurance Ho1 d : Customers educational level is not related to customer loyalty in insurance Ho1 e : Customers current occupation is not related to customer loyalty in insurance Ho1f: Customers income level is not related to customer loyalty in insurance
7 283 The hypothesis testing was carried at 95% confidence level; two tailed. Hypothesis testing was performed to know if there is relationship between demographic variables and customer loyalty (i.e. behavioral loyalty plus attitudinal loyalty)in public and private insurance companies. The chi-square test result is illustrated as below. Table 6.8: Relationship between gender and Loyalty in Public Insurance Sex Do you recommend insurance products to others? df X 2 Sig Do you intend to purchase additional policies? Will you continue as customer in the company? value low medium High Total Male 29 (14.5%) 22(11%) 94 (47%) 145 (72.55) Female 12 (6%) 8(4%) 35(17.5%) 55 (27.5%) Total 41 (20.5%) 30(15%) 129(64.5%) 200 (100) As the table 6.8 above reported, male of the public insurance company has high level of favorable perception on customer loyalty (47%) than female of (17.5%). Surprisingly, (14.5%) of male respondents disfavor loyalty which is bigger than their counterpart of female (6%). However, Pearson chi-square statistics (10.20, 16), p>.05 suggests that such variation is not statistically significant. This means gender and customer loyalty in public insurance are independent to each other; customer gender does not have association to customer loyalty, so that the Null hypothesis is accepted.
8 284 Table 6.9: Relationship between gender and loyalty in Private Insurance (α=.05) Sex Do you recommend insurance products to others? df X 2 S ig Do you intend to purchase additional policies? value 2taile Will you continue as customer in the company? d Low Medium High Total Male 149(18.6%) 54(6.7%) 352(44%) 555(69.3%) Female 54(6.7%) 6(.75%) 185(23%) 245(30.6%) Total 203(20.5%) 60(15%) 547(68%) 800 (100) The table 6.9 above exhibits those males respondents response is higher (44%) in favor of loyalty than female respondents response of (23%). Similarly, more of the male respondents (18.6%) express they have no loyalty in their current provider which is higher than of the female respondents (6.7%) Such variation is supported by Pearson chi-square statistics which suggests that such variation is statistically significant. Chi-square (1.104, 14), p<.05. According to this test p-value is lea than.05 and so the Null hypothesis is rejected and alternative hypothesis is accepted. This means gender and customer loyalty are dependent to each other. Customer gender has direct association to loyalty. In contrary to this,as table 6.8 above reported in public insurance sector gender and customer loyalty are not related, they are independent to each other.
9 285 Table 6.10: Relationship between Respondents Age and loyalty in Public Insurance Respondents Do you recommend insurance products to others? df X 2 Sig Age Do you intend to purchase additional policies? Will you continue as customer in the company? low medium high value Less than 25yrs 2 (1%) - 6 (3%) yrs 12 (6%) 16 (8%) 59 (29.5%) yrs 19 (9.5%) 11 (5.5%) 57 (28.5%) 65 yrs or above 8 (4%) 3 (1.5%) 7 (3.5%) Total 41 (20.5%) 30 ( 15%) 129 (64.5%) As it can be seen in the above table 6.10 the age group of years express they are loyal to their current insurance provider (29.5%) than the other age group. The age group of less than 25 years and age group of 65 years or above is found being low in their perception of loyalty. Such variation is supported by chi-square value (65.28, 48), p<.05. According to this test p-value is less than.05 and because of this the Null hypothesis is rejected and alternative hypothesis is accepted which means age and customer loyalty are statistically significant and dependent to each other. This result verified that there is relationship between customer age and customer loyalty in public insurance sector. There is direct association between customer age and loyalty in public insurance sector of Ethiopia.
10 286 Table 6.11: Relationships between respondents age and loyalty in Private Insurance companies Age Do you recommend insurance products to others? Do you intend to purchase additional policies? Will you continue as customer in the company? Low Medium High Less than 25yrs yrs 57 (7%) 26 (3.3%) 211 (26%) yrs 80 (10%) 28 (3.5%) 292 (37%) 65 yrs or above 12 (1.5%) - 94 (11.8%) df X2 Sig Total (6.8%) 597 (74.6%) (18.6%) The table 6.11 above presents the age group of years express their favorable attitude towards loyalty (37%) than other age group in private insurance companies. Such variation is supported by chi-square value ( ), p<.05. Thus, we can conclude that age and customer loyalty in private insurance are related and so Null hypothesis is rejected and alternative hypothesis is accepted. Table 6.12: Relationship between respondents Marital Status and Customer loyalty in Public Insurance (α=.05) two tailed Respondents Marital status Do you recommend insurance products to others? Do you intend to purchase additional policies? Will you continue as customer in the company? Low Medium High Single 4(2%) 1(.5%) 4 (32%) df X 2 Sig Married 30 (15%) 24 (12%) 98(49%) Others (Divorced, 7 (3.5%) 5 (2.5%) 27( 13.5%) 32 Total 41(20.5%) 30 (15%) 129( 64.5%) The table 6.12 above presents that marital status of married group (49%) expresses their favorable attitude towards loyalty than other marital status groups. This variation is
11 287 supported by chi-square analysis as: Chi-square (66.70, 32), p<.05, hence Null hypothesis is rejected and alternative hypothesis is accepted. This means marital status and customer loyalty are related to each other in the public insurance. Table 6.13: Relationship between Marital Status and Customer loyalty in Private Insurance (α=.05) two tailed Marital status Do you recommend insurance products to df X 2 Sig others? Do you intend to purchase additional policies? Will you continue as customer in the company? Low Medium High Single 14(1.8%) - 6(.8%) Married 121(15%) 46(5.8%) 488 (61%) Others 14 (1.8%) 8 (1%) 103(12.9%) (Divorced, etc.) Total 149 (18.6%) 54 (6.8%) 591 (73.9%) The table 6.13 above shows that married group of the marital status (61%) expresses their positive attitude towards loyalty which is higher than the other group. Surprisingly, among the groups who express their responses the married group (15%) are not loyal to their providers. Such response variation is supported by chi-square statistics as, (1.32, 28), p <.05 which is statistically significant. So, null hypothesis is rejected and alternative hypothesis is accepted. This means marital status and customer loyalty are related to each other in private insurance sector. As we observed in table 6.5 above marital status and loyalty are also related in public insurance sector.
12 288 Table 6.14: Relationship between Respondents Educational Level and Customer loyalty in Public Insurance Company (α=.05) two tailed Education level Do you recommend insurance products to others? Do you intend to purchase additional policies? Will you continue as customer in the company? Low Medium High High school or less 25 (12.5%) 19 (8.5%) 53 (26.5%) Undergraduate 10(5%) 9 (4.5%) 53 (26.5%) Postgraduate or 6 ( 3%) 2 (1%) 23 (1.5%) above Total 41 (20.5%) 30 (15%) 129(64.5% df X 2 Sig The table 6.14 above gives evidence of response variation according to respondents education level. Respondents of whose qualification is high school or less and under.014 graduate people express their positive attitude towards loyalty than the other group which is (26.5%). Such variation is supported by Chi-square value (52.09, 32), p <.05 which means the variation is statistically significant. Thus, Null hypothesis is rejected and alternative hypothesis is accepted. This means there is relationship between qualification and customer loyalty in the public insurance sector. Table 6.15: Relationship between Respondents Educational Level and Customer loyalty in Private Insurance Companies (α=.05), two tailed Education level Do you recommend insurance products to others? df X 2 Sig Do you intend to purchase additional policies? Will you continue as customer in the company? Low Medium High High school or less 93 (11%) 7(.9%) 262 (32.7%) Undergraduate 36 (5%) 39 (4.9%) 216 (27%) Postgraduate or 20 (3%) 8 (1%) 119 (14.5%) above Total 149 (19%) 54 (6.7%) 597 (74.6%)
13 289 The above table6.15 presents, those who are high school or less (32.7%) express their positive view to their current insurance provider than the others. Such variation is supported by chi-square statistics (1.28, 28), p<.05 which means the variation is statistically significant and so that qualification and loyalty are related. Hence, Null hypothesis is rejected and alternative hypothesis is accepted. Table 6.16: Relationship between Respondents Current Job and Customer loyalty in Public Insurance Company (α=.05), two tailed Education level Do you recommend insurance products to others? df X2 Sig Do you intend to purchase additional policies? Will you continue as customer in the company? Low Medium High Total Business class 20 (10%) 12 (6%) 48 (24%) 80(40%) Salary class 19 (9.5%) 17 (8.5%) 64 (32%) 100(50%) Others(agriculture. 2 (1%) 1 (.5%) 17(8.5%) 20(10%) Total 41 (20.5%) 30 (15%) 129(64.5%) 200(100) As the above table 6.16 exhibits salary groups (32%) of respondents perception on loyalty is positive than others. In the contrary (10%) of the business class respondents express their opinion that they disfavor their provider. However, such variation is not supported by x 2 analysis. Chi-square statistics (43.67, 32), p=.082 suggest that there is no relationship between customers occupation and customer loyalty. Thus, null hypothesis is accepted. This means there is no association between customer current job and loyalty.
14 290 Table 6.17: Relationship between respondents Current Job and Customer loyalty in Private Insurance Companies. (α=.05), two tailed Occupation Do you recommend insurance products to others? df X 2 Sig Do you intend to purchase additional policies? Will you continue as customer in the company? Low Medium High total Business class 79 (9.8%) 27 (3%) 280 (35%) 386(48%) Salary class 58 (7.3%) 12 (1.5% 266 (33%) 336(42%) Others 12 (1.5 %) 15 (1.9%) 51 (6%) 78(10%) (agriculture..) Total 149 (19%) 54 (8%) 597 (75%) 800(100%) As it can be seen in the table 6.17 above, respondents who are in business group (35%) express their loyalty to the current provider, which is higher than the other group response. Such variation is supported by Chi-square value (1.62, 28), p<.05, so that null hypothesis is rejected and alternative hypothesis is accepted. This means there is relationship between customer occupation and loyalty in the private insurance sector. There is direct association between customer present occupation and loyalty in private insurance sector. In table 6.9 above it was reported that there is no association between current job and loyalty in public insurance sector. Customer current job and loyalty are independent to each other in public insurance sector.
15 291 Table 6.18: Relationship between Respondents Income and Customer loyalty in Public Insurance Company (α=.05), two tailed Monthly Income Do you recommend insurance products to others? df X 2 Sig level Do you intend to purchase additional policies? Will you continue as customer in the company? Low Medium High Total Less than Birr 5,000 3 (1.5%) 2(1%) 14 (7%) 19 Birr , (15%) 12 (6%) 66 (32%) 108 Birr 10,001-15,000 7 (3.5%) 16 (8%) 42 (21%) 25 Birr 15,001 or above 1 (.5%) - 7 (3.5%) 8 Total 41 (20.5%) 30 (15%) 129 (64.5%) As it is reported in the above table 6.18 middle income customers in the group ,000 observed as (32%) expressed as they are loyalty to their current insurance provider higher than the other group. Surprisingly, this middle income class (15%) expressed that they are not loyal to their current insurance providers which is higher than the other group. Among respondents the high income class (.5%) expressed their disloyalty least of the other group. Such variation is supported by Chi-square value (90.2, 48),p<.05which means there is relationship between customer income and customer loyalty. Thus, Null hypothesis is rejected and alternative hypothesis is accepted. Hence we can infer that customer income level is related to customer loyalty in public insurance sector. Table 6.19: Relationship between Respondents Income and Customer loyalty in Private Insurance Companies. (α=.05), two tailed Income level Do you recommend insurance products to others? Do you intend to purchase additional policies? Will you continue as customer in the company? Low Medium High Total Less than Birr 7 (.9%) - 55 (6.8%) 62 5,000 Birr , (8%) 21 (2.6%) 259 (32%) 347 df X 2 Sig Birr 10, (6%) 27 (3%) 182 (22.7%) ,000 Birr 15,001 or 24 (3%) 6 (.7%) 101 (12.6%) 131 above Total 149 (56%) 54 (6.7%) 597 (75%)
16 292 The above table 6.19 reported that medium income customers (32%) response express they are loyal to their current provider higher than that of the other group. This variation is supported by b Chi-square calculation (2.31, 42), p<.05 which means there is significant relationship between customers income and loyalty in private insurance sector of Ethiopia. Thus, Null hypothesis is rejected and alternative hypothesis is accepted. To know if relationships exist between demographic variables and customer loyalty in private and public insurance companies, response rate and chi-square test were conducted. Thus, it was found that demographic variables of respondents age, marital status, and education level have related to customer loyalty in both public and private insurance companies. However, gender and current job are found independence to customer loyalty in public insurance while in private insurance they are related to customer loyalty Correlation analysis: Among variables in public and private insurance companies In this section efforts are made to know the relationship between variables in both public and private insurance companies. The statistical tools employed for computation relationships is the Pearson correlation statistics. The analysis gives us information whether the relationship is positive or negative, whether the relationship is strong or weak. The table below shows the result of correlation analysis between variables.
17 293 Table Correlation Matrix results in public insurance of Ethiopia. service quality perception customer satisfaction service quality perception N **. Correlation is significant at the 0.01 level (2-tailed). As it can be seen in the above table 6.20, variables: service quality, customer satisfaction, customer trust, and customer loyalty are positively and significantly correlated to each other. The above Pearson correlation test shows that the variables are correlated with values of above.60 considered as moderate and strong correlation. The relationship between variables is significant and so that null hypotheses are rejected. This means determinant variables are correlated with customer loyalty significantly. The correlation coefficient between variables tells us the strength of the relationship between the two variables. And also the correlation coefficient between variables tells us respondents responses are positive and linear. The table below shows correlation statistics results of private insurance companies. customer customer satisfaction trust switching cost Customer loyalty Correlation **.734 **.713 **.807 ** Sig Correlation **.675 **.821 ** Sig customer trust Correlation **.850 ** Sig N switching cost Correlation ** Customer loyalty Sig.000 Correlation.807 **.821 **.850 **.864 ** 1 Sig. (2- tailed)
18 294 Table 6.21: Correlation Matrix Private Insurance sector service quality perception service quality perception customer satisfaction customer trust switching cost Customer loyalty Correlation **.757 **.751 **.800 ** Sig customer Correlation **.798 **.821 ** satisfaction Sig customer trust Correlation **.806 ** Sig switching cost Correlation ** Customer loyalty Sig..000 N Correlation.800 **.821 **.806 **.901 ** 1 Sig. (2- tailed) N **. Correlation is significant at the 0.01 level (2-tailed). As it can be seen from the above table 6.21 there is positive and significant relationships exist between variables. The relationship between variables is strong; above the threshold.70 and it is considered as strong correlation. Results of the correlation analysis shows us the p- value <0.01. Hence, Null hypothesis is rejected and we can conclude that there are relationships between determinant factors and customer Loyalty in private insurance sector. In the above table 6.20 and table 6.21 it is found that determinant variables and customer loyalty are related significantly and positively in both public and private insurance companies. The relationships were observed strong and positive which implies customer responses are in similar trends. However, the correlation value of switching cost and customer loyalty is higher in private insurance sector while the
19 295 correlation value between customer trust and customer loyalty is higher in public insurance sector than private insurance sector Comparative analysis: loyalty in public and private insurance This study examines if there is differences in level of customer loyalty in public and private insurance sectors. Thus, statistical test is computed to identify the level of policyholders level of loyalty in that of public and private insurance companies. Samples of (200) were taken from public insurance company and 800 samples were taken from private insurance companies. The researcher was in the view that there is no significant difference in customer level of loyalty between public and private insurance companies. Thus, statistical test were conducted to know if customer level of loyalty is the same or different in public and private insurance companies. Here below is the hypothesized null and alternative hypothesize. Ho8: There is no significance difference in level of customer loyalty between public and private in insurance sectors. H18 : There is significance difference in level of customer loyalty between public and private in insurance sectors. Thus, in order to verify the hypothesized statements two levels of tests were performed: simple comparison based on mean & standard deviation, and ANOVA. The test analyses are illustrated below.
20 Comparisons of loyalty using Mean & SD: In public & private insurance companies of Ethiopia In order to know if customer loyalty is similar or different in both sectors a comparative test is employed using mean and standard deviation of each variable. The detail is presented in the table below. Table 6.22: Comparing of Public and Private Insurance( using mean & SD) Sectors Factors Mean Std. Deviation Sample size Public service quality customer satisfaction customer trust switching cost customer loyalty Private service quality customer satisfaction customer trust switching cost customer loyalty The table 6.22 above gives information of means and standard deviations for each variable group. Mean scores based on a five point scale ranging from 1(strongly disagree) to 5 (strongly agree). It is clear from the table that the mean of variables of public insurance is different from that of private insurance sector.their standard deviation is also different from public to private. Generally, in public insurance there is a bit more dispersion of standard deviation from the mean than private insurance sector. The mean score of private insurance companies in all variables are higher than its counterpart public insurance mean scores. This means the customer perception on
21 297 loyalty is higher in private insurance companies than public insurance company. From this we can conclude that level of customer loyalty is not similar in both sectors. However, to make a final decision further test is necessary so that ANOVA analysis is performed as below Regression weight between public and private insurance companies. Regression weights of public and private insurance companies are illustrated below for comparison in order to differentiate the effects of variables on customer loyalty. Table 6.23: unstandardized Regression Weight Estimates for Public and Private insurance companies. Descriptions Estimates, at 95% Public P.value Private p.value n=200 n= 800 Constant Customer Service loyalty quality Customer Customer loyalty satisfaction Customer Customer loyalty trust Customer loyalty Switching cost As it can be seen in the above table, variables affect customer loyalty significantly in both public and private insurance companies. P-value is less than.05 in both sectors and in all variables so that we can conclude that variables affect customer loyalty significantly. However, the regression weights of service quality, customer satisfaction, and customer trust on customer loyalty are greater in public insurance sector than in private insurance sectors. This means if one variable goes up by one unit
22 298 in both sectors simultaneously the effects on customer loyalty is higher in public sector than private sector. Exceptionally, regression weight of switching cost on customer loyalty is much higher in private insurance sector than public insurance sector Comparisons of respondents perceptions of behavioral & attitudinal loyalty in public & private sectors based on survey The graph below depicts respondents perception of their behavioral and attitudinal loyalty responses in public and private insurance sectors as: Respondents perceptions on recommending company reputation to other people (word of mouth behavioral loyalty) Willing to purchase additional policies from their current provider(crossselling, behavioral loyalty ) Commitment to continue doing business with their insurer (retention, attitudinal loyalty) Public n= 200 Private, n= 800 Public n= 200 Private, n= 800 Figure 6.1: Respondents behavioral and attitudinal response in public and private sectors (in percent) Pledge to advocate Willing for cross-selling Commitment for retention Public private As it can be seen in the above figure respondents perception of recommending company reputation to other people is a bit higher in private (53.7%) than public insurance (51%). In contrary, in regard to intentions for purchasing additional policies from the current provider is much higher in public (80.5%) than in private insurance
23 299 sector (65.9%). In customer retention response public respondents are a bit higher than private insurance sector ANOVA: Comparison of customer loyalty in public & private insurance companies. ANOVA is a general technique that can be used to test the hypothesis set about the mean difference among two or more groups, under the assumption that the sample populations are normally distributed (Malhotra 2008). ANOVA allows us to reject or to retain the null hypothesis, then make inferences about the population. ANOVA is based on two assumptions. 1) The observations should be random samples from normal distributions (normally distributed). 2) The populations have the same variance (possess homogeneity of variance). Therefore, before we carried out ANOVA, we should have to check whether the assumptions are satisfied. As the assumptions are checked and illustrated in chapter four above, we could proceed with the analysis of variance in order to test for significant interaction effects and main effects. Hypotheses testing of main & interaction effects i. Null and alternative hypothesis Ho8: There is no significant difference in level of customer loyalty between public and private in insurance sectors H18 There is no significant difference in level of customer loyalty between public and private in insurance sectors Significance Level: α = 0.05, that is the chance of occurring type I error is 5 %. Critical Value and Rejection Region: Reject the Null hypothesis if p-value 0.05.
24 300 The ANOVA table below shows test of significance main effects and interaction effects. Table 6.24 Tests of Between-Subjects Effects Source Dependent variable : Level of customer loyalty Type III Sum of Squares df Mean Square F Sig. ηp 2 * Corrected Model Intercept E sector * Factor * sector * Factor Error Total Corrected Total * ηp 2 = (Partial Eta Squared) which means estimate of effect size. *Sector has 2 categories (levels) then 2-1 =1 df and factor has 5 categories then 5-1=4 df. ii. Findings of Interaction effect: As it can be seen from the above table, ANOVA result reports that the overall model is statistically significant as F ratio (9, 4990) = , p <.05). P- Value is less than.05, thus we can conclude that the model is fitting for statistical computation. The table above also tells us the interaction effect and main effects. There is no a significant interaction effect between the level of sector involved and the amount of factor provided to affect customer loyalty as F ratio (4, 4990) =2.350, P- value =0.052 where p-value is greater than And also the partial Eta squared (estimate of effect size) is=.002 which means 0.2% of the variability in the dependent variable can be explained by the independent variable so that the size of the effect is very small.
25 301 iii. Inference: It can be concluded that there is no enough evidence to reject the null hypothesis. We fail to reject the null hypothesis. This means the interaction effect sector*factor is not statistically significant. The interaction effects of sector,( private and public ) and the extent of the level of factor (levels) provided are the same to affect customer loyalty. Findings of Main effects of hypothesis test: In this analysis we have two independent variables sector and factor which further classified in different levels(sectors=2 levels private and public ), factor has 5 levels ( service quality perception, customer satisfaction, customer trust, switching cost and customer loyalty) where level of customer loyalty is dependent variable The factorial two-way ANOVA found that there is a significant( difference) main effect of sector type to affect customer loyalty, F ratio (1,4990) = , P value <.05, ηp 2 =.012. In addition to this, there is a significant main effect of factors provided to affect customer loyalty, F value (4, 4990) = , P <.05, ηp 2 =.030. Inference of the main effects: As the findings reports there is enough evidence to conclude that there is difference in mean value of the sectors. That is degree of freedom is 1 and the value of Partial Eta squared is which tells us that sector accounts for 1.2% of the total variance in the effect. So that we have enough evidence to make decision that there is difference between means and we can conclude that it is statistically significant and Null hypothesis is rejected at 95% of confidence interval. Thus, we can infer that sector of the main effect is differently affect customer loyalty. The main effect of factor is also statistically
26 302 significant that shows significance mean difference in the level of factors in order to affect customer loyalty. In this study we are interested in the sector, factor and interactions (sector *factor) effects whether we have significant mean differences between our groups for our two independent variables, sector and factor and for their interaction, sector *factor of levels. Since the interaction effect did not have significant effect, pair wise comparison is performed below. Pair-wise comparison is the mean difference of public and private insurance sectors. The table below shows the Tukey post-hoc test results for the mean difference of the public and private insurance sectors. Table 6.25: Pair wise Comparisons Dependent Variable: Levels of customer loyalty 95% Confidence Interval for (I) (J) Mean Difference Difference Sector Sector (I-J) Std. Error Sig. Lower Bound Upper Bound Govt Pvt * Pvt Govt.174 * Based on estimated marginal means *. The mean difference is significant at the.05 level. The above table presents the mean difference is significant at the.05 level. As it can be seen from the above table there is a significance difference between the means of the sectors, public and private. Although, both sector means difference is statistically significant, it is observed that mean of private insurance is higher than public insurance sector. The table below shows means difference between factors of five levels of variables.
27 303 Table: 6.26: Mean Differences between Determinant factors and level of loyalty (I) factors service quality perception (J) factors Mean Difference (I-J) Std. Error Sig. 95% Confidence Interval for Difference Lower Bound Upper Bound satisfaction.246 * customer Trust Switching cost * loyalty * customer satisfaction service quality * customer Trust * Switching cost * loyalty * customer Trust service quality satisfaction.247 * Switching cost * Customer loyalty * Switching cost service quality.157 * satisfaction.402 * customer Trust.156 * loyalty Customer loyalty service quality.091 * satisfaction.337 * customer Trust.090 * Switching cost
28 304 As the above table 6.26 presents based on estimated marginal mean, the mean difference is significant at the.05 level. The mean difference of customer satisfaction is significant at 95% confidence interval. The other variables mean difference are significant in some way while in other way is not significant. Moreover, level of loyalty between public and private insurance companies are pictorially illustrated below. The graph is based on two dimensions: horizontally variables are presented and at the vertical axis mean of variables are depicted. The type of sector (private, public) is also delineated in the graph. Thus, using this two-dimensional graph it is possible to identify the level of loyalty in insurance sector both private and public. Figure.6.2: Estimated marginal means of levels of customer loyalty
29 305 The plot above gives evidence of the level of customer loyalty in public and private insurance sectors. In regard to service quality the private sector has better status while in public as observed less. In all variables customer perception is higher in private sectors than public sector. This means customers are satisfied in private sector than who are in public sectors. Customers have better trust on employees of private companies than public ones. This implies that the private insurers meets customer needs which influence customer loyalty better than public insurance company. 6.4 Chapter Summary Efforts have been made to achieve the research objective of exploring the level of customer loyalty in public and private insurance sectors. Respondents demographic factors and customer loyalty were assessed if relationships exist, so that it is verified that relationships exist between demographic factors and customer loyalty. However, sex and customer loyalty are not related in public insurance company. Private insurance mean score is reported as higher while public insurance is a bit smaller. This implies that the private insurers provide better service quality than the public insurers. Factorial ANOVA was conducted to examine whether there were statistically significant differences in level of customer loyalty between public and private insurance sectors. From the results it is observed that there is strong evidence that the mean of factors varies with the prevailing sectors operated ( df = 1, 4,990, F value=62.057, p-value <.05, ηp2 =.012.There is also significance difference between factors as : ( df =4, F value =38.248, p-value <.05, and ηp2 =.030). However, the combination effect between factors and sectors does not have sufficient evidence to reject null hypothesis (as F value =2.350, p-value=.052 and ηp2 =.002)
30 306 CHAPTER VII FINDINGS, SUGGESTIONS & CONCLUSIONS The focus area in this study was to examine the influence of determinants on customer loyalty. The literature review highlighted on describing service quality, customer satisfaction, customer trust and switching cost, as antecedents of customer loyalty. In addition to this, efforts were made in exploring the mediation effect of customer satisfaction between service quality perceptions and customer loyalty link. Besides this, efforts were made to identify whether the level of customer loyalty in public and private insurance is similar or not using the Factorial ANOVA. In order to achieve the objective of the study hypotheses were statistically tested. The detail discussions presented in chapter 5 and 6 above. This chapter presents summary of findings & implications for insurers, contribution of the study, conclusions and suggestions Findings & Implications for Insurers Respondents response survey shows that (38.8%) of respondent policyholders (table 5.29 above) believe companies facilities, equipments and communication materials are poor while (58.8%) viewed fulfillment of facilities in their respective companies. This implies insurers should have to fulfill working materials in order to attract customers and stay happily in the company. About (14.2%) of respondents believe that empathy activities are poor in their company; such situation can influence them to view themselves that they have chosen the wrong insurer. This means they will probably searching for a better or start thinking to shift to other providers.
31 307 Furthermore, the attitude response analysis indicates that (82%) customers are willing to consider repeat purchases from their existing providers (Table 5.33), but (21.6%) respondents are not inclined for cross-selling. On the other hand, the response analysis of Table 5.33 above reveals (21.6%) respondents believe they will not recommend the company good experience to their friends, acquaintance and relatives. Worryingly, (14.7%) of the respondents do not have idea to continue business with their current providers. From this insurers need to be alert, they have to make efforts to meet customer needs in order to survive and prosper. Moreover, the attitude response analysis result indicates that sustainable communication with the provider definitely could increase their satisfaction (Table 5.30). When (54.6%) of respondents believe their interactions with the respective companies are good while (34.2%) respondents revealed they had poor communication with their providers. Among respondents (24.9%) are dissatisfied with their provider due to poor service, (26.6%) of the respondents are not satisfied due to poor treatment from their provider. This can be considered as a forewarn for insurers. Customers have a genuine cause for complaint so that insurers need to listen to them. All staff in contact with customers must at all times be polite and helpful. Getting the service right will improve customer satisfaction and assist in the building of customer loyalty. It is crucial for insurers to maintain a multichannel distribution strategy in order to enhance communication with existing customers. The respondents analysis in (Table 29, 30, 31, 32) above show that improved service quality, customer trust on employees and customer trust on company as well as overall satisfaction with the provider, drive customers towards loyalty. The F- values indicate the relationship between determinant
32 308 factors and customer loyalty is significant. Therefore, if providers are unable to respond to customer changing needs, it is imminent that customers will switch to other providers. It was hypothesized that there is no relationship between customers demographic factors and customer loyalty in insurance sector of Ethiopia. Chi-square analysis was employed for significance test (Table 5.27 above). Thus, the analysis indicates that the respondents demographic particulars (gender, age, education level, income, marital status, family size, occupation) and customer loyalty are dependent.the relationship is significant at 95% significance level that Null hypotheses are rejected and alternative hypotheses were accepted. This relationship implies insurers should deploy resources based on customers demographic variables to build loyalty for their products. It was also hypothesized that there were no relationship between determinant variables (service quality, customer satisfaction, customer trust and switching cost) and customer loyalty. Hence Pearson correlation coefficient was employed to test the hypotheses. So that the result of hypotheses testing show relationship between determinant factors (service quality, customer satisfaction, customer trust & switching cost) and customer loyalty is positive, strong and significant at the 0.01 level 2-tailed (Table 5.28 above). Relationship is observed as: Service quality and customer loyalty (r=.802), customer satisfaction & loyalty(r=.823), customer trust and loyalty (.818) and switching cost to loyalty (r=.889).the relationship is observed as direct and strong linear relationships exist between variables. This means the increment in one variable would correspond to increase in customer loyalty. Such direct relationship between variables is a message
33 309 for insurers means that if they want to build customer loyalty they need to focus on determinant factors. Furthermore, respondents analysis of respondents perception on service quality, satisfaction, trust and switching cost and customer loyalty was performed based on response rate and F- statistics. The result of this analysis shows the relationship between determinant variables and customer loyalty is significant. Perceived response scores were recorded, low (strongly dissatisfied & dissatisfied), medium (neither agree nor disagree) and high (agree and strongly agree) and variation observed for each variable. These variations are supported by F- value calculation. The F- value analysis verified that the relationship between service quality, customer satisfaction, customer trust & switching cost and customer loyalty is statistically significant. The results of regression modeling including mediation analysis demonstrate that there is a close relationship between (as perceived by policyholders): Service quality and customer loyalty in the insurance sector of Ethiopia. Service quality and customer satisfaction in the insurance sector of Ethiopia. Customer satisfaction and customer loyalty in the insurance sector of Ethiopia. Customer trust and customer loyalty in the insurance sector of Ethiopia. Switching cost and customer loyalty in the insurance sector of Ethiopia. However, switching cost has the greatest influence on customer loyalty among influencing variables of service quality, customer satisfaction and customer trust.the regression analysis also suggest that customer trust has greater influence on customer loyalty following switching cost. This implies that insurers need to focus on switching cost, customer trust, and service quality and customer satisfaction for customer
34 310 retention. The other important issue the regression analyses unfold is that R 2 =.879 i.e % of the variation in customer loyalty is explained by determinant factors (effect of service quality, customer satisfaction, switching cost and customer trust on customer loyalty). The regression analysis reveals that the estimate of regression weight as (Table 5.37): When service quality perception goes up by 1, customer loyalty goes up by When perceived customer satisfaction goes up by 1, customer loyalty goes up by When customer trust perception goes up by 1, customer loyalty goes up by When switching cost perception goes up by 1, customer loyalty goes up by This means the greater the variables increase the greater is customer loyalty. Switching cost has more influence on loyalty (.531) than the other variables mentioned. This means when the costs of switching costs are high for the customer, there is a greater probability that the customer will remain loyal and purchasing additional policies. The more switching protection available the more customers go with the present providers. The result of regression analysis implies when customers are satisfied with the insurer overall service, when customers have trust on employees and company, they will be motivated for cross-selling. If the insurer is not servicing as customer expects, then all potential cross-selling opportunities will be lost. Therefore, the first point of action should be delivering quality service to the existing customers according to their needs. Efforts need to be made well before the customer lapses, not at the point they are considering leaving. This requires significant investment in understanding customer needs and deploys resources on antecedents of customer loyalty.
Chapter 5 Analysis of variance SPSS Analysis of variance Data file used: gss.sav How to get there: Analyze Compare Means One-way ANOVA To test the null hypothesis that several population means are equal,
Simple Linear Regression Inference 1 Inference requirements The Normality assumption of the stochastic term e is needed for inference even if it is not a OLS requirement. Therefore we have: Interpretation
Independent t- Test (Comparing Two Means) The objectives of this lesson are to learn: the definition/purpose of independent t-test when to use the independent t-test the use of SPSS to complete an independent
CHAPTER 5: CONSUMERS ATTITUDE TOWARDS ONLINE MARKETING OF INDIAN RAILWAYS 5.1 Introduction This chapter presents the findings of research objectives dealing, with consumers attitude towards online marketing
ANOVA ANOVA Analysis of Variance Chapter 6 A procedure for comparing more than two groups independent variable: smoking status non-smoking one pack a day > two packs a day dependent variable: number of
INTERPRETING THE ONE-WAY ANALYSIS OF VARIANCE (ANOVA) As with other parametric statistics, we begin the one-way ANOVA with a test of the underlying assumptions. Our first assumption is the assumption of
Chapter 11 Two-Way ANOVA An analysis method for a quantitative outcome and two categorical explanatory variables. If an experiment has a quantitative outcome and two categorical explanatory variables that
1 Final Review 2 Review 2.1 CI 1-propZint Scenario 1 A TV manufacturer claims in its warranty brochure that in the past not more than 10 percent of its TV sets needed any repair during the first two years
Chapter 3 Student Lecture Notes 3- Chapter 3 Introduction to Linear Regression and Correlation Analsis Fall 2006 Fundamentals of Business Statistics Chapter Goals To understand the methods for displaing
Main Effects & Interactions page 1 Main Effects and Interactions So far, we ve talked about studies in which there is just one independent variable, such as violence of television program. You might randomly
Descriptive Statistics Primer Descriptive statistics Central tendency Variation Relative position Relationships Calculating descriptive statistics Descriptive Statistics Purpose to describe or summarize
Unit 31 A Hypothesis Test about Correlation and Slope in a Simple Linear Regression Objectives: To perform a hypothesis test concerning the slope of a least squares line To recognize that testing for a
Chapter VIII Customers Perception Regarding Health Insurance This chapter deals with the analysis of customers perception regarding health insurance and involves its examination at series of stages i.e.
UNDERSTANDING THE e have seen how the one-way ANOVA can be used to compare two or more sample means in studies involving a single independent variable. This can be extended to two independent variables
Multiple Linear Regression A regression with two or more explanatory variables is called a multiple regression. Rather than modeling the mean response as a straight line, as in simple regression, it is
CHAPTER 5 COMPARISON OF DIFFERENT TYPE OF ONLINE ADVERTSIEMENTS 5.1 Descriptive Analysis- Part 3 of Questionnaire Table 8 shows the descriptive statistics of Perceived Usefulness of Banner Ads. The results
RECRUITERS PRIORITIES IN PLACING MBA FRESHER: AN EMPIRICAL ANALYSIS Miss Sangeeta Mohanty Assistant Professor, Academy of Business Administration, Angaragadia, Balasore, Orissa, India ABSTRACT Recruitment
Profiles and Data Analysis PROFILES AND DATA ANALYSIS 5.1 Introduction The survey of consumers numbering 617, spread across the three geographical areas, of the state of Kerala, who have given information
Trust, Job Satisfaction, Commitment, and the Volunteer s Psychological Contract Becky J. Starnes, Ph.D. Austin Peay State University Clarksville, Tennessee, USA email@example.com Abstract Studies indicate
Notation: Notation and Equations for Regression Lecture 11/4 m: The number of predictor variables in a regression Xi: One of multiple predictor variables. The subscript i represents any number from 1 through
HYPOTHESIS TESTING: CONFIDENCE INTERVALS, T-TESTS, ANOVAS, AND REGRESSION HOD 2990 10 November 2010 Lecture Background This is a lightning speed summary of introductory statistical methods for senior undergraduate
MINITAB ASSISTANT WHITE PAPER This paper explains the research conducted by Minitab statisticians to develop the methods and data checks used in the Assistant in Minitab 17 Statistical Software. One-Way
Chapter 7 One-way ANOVA One-way ANOVA examines equality of population means for a quantitative outcome and a single categorical explanatory variable with any number of levels. The t-test of Chapter 6 looks
THE KRUSKAL WALLLIS TEST TEODORA H. MEHOTCHEVA Wednesday, 23 rd April 08 THE KRUSKAL-WALLIS TEST: The non-parametric alternative to ANOVA: testing for difference between several independent groups 2 NON
Chapter 2 Probability Topics SPSS T tests Data file used: gss.sav In the lecture about chapter 2, only the One-Sample T test has been explained. In this handout, we also give the SPSS methods to perform
THE BASICS OF DATA MANAGEMENT AND ANALYSIS A USER GUIDE January 26, 2009 The Faculty Center for Teaching and Learning THE BASICS OF DATA MANAGEMENT AND ANALYSIS Table of Contents Table of Contents... i
Technical report How to Get More Value from Your Survey Data Discover four advanced analysis techniques that make survey research more effective Table of contents Introduction..............................................................2
Information Management 167 in nigerian companies. Idris, Adekunle. A. Abstract: Keywords: Relationship Marketing, Customer loyalty, Customer Service, Relationship Marketing Strategy and Nigeria. Introduction
Appendix D Basic Measurement And Statistics The following information was developed by Steven Rothke, PhD, Department of Psychology, Rehabilitation Institute of Chicago (RIC) and expanded by Mary F. Schmidt,
Step 1: Regression step-by-step using Microsoft Excel Notes prepared by Pamela Peterson Drake, James Madison University Type the data into the spreadsheet The example used throughout this How to is a regression
Chapter 4 One-Way ANOVA Recall this chart that showed how most of our course would be organized: Explanatory Variable(s) Response Variable Methods Categorical Categorical Contingency Tables Categorical
UNDERSTANDING The independent-samples t test evaluates the difference between the means of two independent or unrelated groups. That is, we evaluate whether the means for two independent groups are significantly
DETERMINANTS OF CUSTOMER SATISFACTION IN FAST FOOD INDUSTRY Shahzad Khan, Lecturer City University of Science & I-T, Peshawar Pakistan Syed Majid Hussain, BBA (Hons) student, City University of Science
One-Way Analysis of Variance Note: Much of the math here is tedious but straightforward. We ll skim over it in class but you should be sure to ask questions if you don t understand it. I. Overview A. We
Regression Analysis: A Complete Example This section works out an example that includes all the topics we have discussed so far in this chapter. A complete example of regression analysis. PhotoDisc, Inc./Getty
Parametric tests T-test & factor analysis Better than non parametric tests Stringent assumptions More strings attached Assumes population distribution of sample is normal Major problem Alternatives Continue
Impact of Customer Relationship Management of Hotel (A Case study Umaid Bhwan) Dr. Tauseef Ahmad Jai Narain Vays University Department of accounting Dr. Omar A.A. Jawabreh Department of Tourism and Hotels
SPSS Guide: Regression Analysis I put this together to give you a step-by-step guide for replicating what we did in the computer lab. It should help you run the tests we covered. The best way to get familiar
Mgt 540 Research Methods Data Analysis 1 Additional sources Compilation of sources: http://lrs.ed.uiuc.edu/tseportal/datacollectionmethodologies/jin-tselink/tselink.htm http://web.utk.edu/~dap/random/order/start.htm
Simple linear regression Introduction Simple linear regression is a statistical method for obtaining a formula to predict values of one variable from another where there is a causal relationship between
Multiple Regression in SPSS This example shows you how to perform multiple regression. The basic command is regression : linear. In the main dialog box, input the dependent variable and several predictors.
Contents 11 Association Between Variables 767 11.1 Introduction............................ 767 11.1.1 Measure of Association................. 768 11.1.2 Chapter Summary.................... 769 11.2 Chi
Using Excel to Solve Business Problems: Simple Predictive Analytics Curtis Seare Copyright: Vault Analytics July 2010 Contents Section I: Background Information Why use Predictive Analytics? How to use
DATA ANALYSIS AND INTERPRETATION OF EMPLOYEES PERSPECTIVES ON HIGH ATTRITION Analysis is the key element of any research as it is the reliable way to test the hypotheses framed by the investigator. This
Generalized Linear Models We have previously worked with regression models where the response variable is quantitative and normally distributed. Now we turn our attention to two types of models where the
Analyzing Data SPSS Resources 1. See website (readings) for SPSS tutorial & Stats handout Don t have your own copy of SPSS? 1. Use the libraries to analyze your data 2. Download a trial version of SPSS
Methods The link between positive call center service and high levels of customer satisfaction has been established through numerous studies such as Genyses, Right Now Technologies, McColl and Schnieder,
CS-BIGS 4(1): 66-72 2010 CS-BIGS http://www.bentley.edu/csbigs/vol4-1/chen.pdf A Statistical Analysis of Popular Lottery Winning Strategies Albert C. Chen Torrey Pines High School, USA Y. Helio Yang San
Introduction to Quantitative Methods October 15, 2009 Contents 1 Definition of Key Terms 2 2 Descriptive Statistics 3 2.1 Frequency Tables......................... 4 2.2 Measures of Central Tendencies.................
Chapter 23. Inferences for Regression Topics covered in this chapter: Simple Linear Regression Simple Linear Regression Example 23.1: Crying and IQ The Problem: Infants who cry easily may be more easily
Opening Example CHAPTER 13 SIMPLE LINEAR REGREION SIMPLE LINEAR REGREION! Simple Regression! Linear Regression Simple Regression Definition A regression model is a mathematical equation that descries the
Chapter 7 Section 7.1: Inference for the Mean of a Population Now let s look at a similar situation Take an SRS of size n Normal Population : N(, ). Both and are unknown parameters. Unlike what we used
SPSS Tests for Versions 9 to 13 Chapter 2 Descriptive Statistic (including median) Choose Analyze Descriptive statistics Frequencies... Click on variable(s) then press to move to into Variable(s): list
Final Exam Practice Problem Answers The following data set consists of data gathered from 77 popular breakfast cereals. The variables in the data set are as follows: Brand: The brand name of the cereal
IOSR Journal of Research & Method in Education (IOSR-JRME) e-issn: 2320 7388,p-ISSN: 2320 737X Volume 4, Issue 1 Ver. V (Feb. 2014), PP 52-56 Evaluating the Teacher Effectiveness of Secondary and Higher
International Journal of Economics, Commerce and Management United Kingdom Vol. III, Issue 11, November 2015 http://ijecm.co.uk/ ISSN 2348 0386 THE RELATIONSHIP BETWEEN WORKING CAPITAL MANAGEMENT AND DIVIDEND
A Study on Customer Relationship Management Practices in Selected Private Sector Banks with Reference to Coimbatore District Dr. H. Balakrishnan* & R. Krishnaveni** *Principal & Secretary, S.N.R Sons College,
2 - Manova 4.3.05 25 Multivariate Analysis of Variance What Multivariate Analysis of Variance is The general purpose of multivariate analysis of variance (MANOVA) is to determine whether multiple levels
UNIVERSITY OF MISKOLC Faculty of Economics Institute of Business Information and Methods Department of Business Statistics and Economic Forecasting PETRA PETROVICS SPSS TUTORIAL & EXERCISE BOOK FOR BUSINESS
A Basic Guide to Analyzing Individual Scores Data with SPSS Step 1. Clean the data file Open the Excel file with your data. You may get the following message: If you get this message, click yes. Delete
Chapter 340 Principal Components Regression Introduction is a technique for analyzing multiple regression data that suffer from multicollinearity. When multicollinearity occurs, least squares estimates
ABSORBENCY OF PAPER TOWELS 15. Brief Version of the Case Study 15.1 Problem Formulation 15.2 Selection of Factors 15.3 Obtaining Random Samples of Paper Towels 15.4 How will the Absorbency be measured?
106 A STUDY ON FACTORS AFFECTING CUSTOMERS INVESTMENT TOWARDS LIFE INSURANCE POLICIES ABSTRACT MS. BABITA YADAV*; DR. ANSHUJA TIWARI** *Research Scholar, Faculty of Commerce, R.D.V.V, Jabalpur. **Assistant
The Islamic University of Gaza Faculty of Commerce Department of Economics and Political Sciences An Introduction to Statistics Course (ECOE 130) Spring Semester 011 Chapter 10- TWO-SAMPLE TESTS Practice
(A Study of State Life Insurance Corporation Peshawar - Pakistan) Shahzad Khan Abstract This study primarily attempts to investigate the relationship among the variable to create rational motivation in
Research Methods & Experimental Design 16.422 Human Supervisory Control April 2004 Research Methods Qualitative vs. quantitative Understanding the relationship between objectives (research question) and
Getting Correlations Using PROC CORR Correlation analysis provides a method to measure the strength of a linear relationship between two numeric variables. PROC CORR can be used to compute Pearson product-moment
1 Calculating, Interpreting, and Reporting Estimates of Effect Size (Magnitude of an Effect or the Strength of a Relationship) I. Authors should report effect sizes in the manuscript and tables when reporting
Univariate Regression Correlation and Regression The regression line summarizes the linear relationship between 2 variables Correlation coefficient, r, measures strength of relationship: the closer r is
1 Simple ANalysis Of Variance (ANOVA) Oftentimes we have more than two groups that we want to compare. The purpose of ANOVA is to allow us to compare group means from several independent samples. In general,
Two Related Samples t Test In this example 1 students saw five pictures of attractive people and five pictures of unattractive people. For each picture, the students rated the friendliness of the person
Projects Involving Statistics (& SPSS) Academic Skills Advice Starting a project which involves using statistics can feel confusing as there seems to be many different things you can do (charts, graphs,
Roadmap to Data Analysis Introduction to the Series, and I. Introduction to Statistical Thinking-A (Very) Short Introductory Course for Agencies Objectives of the Series Roadmap to Data Analysis Provide
Advanced Degrees and Student Achievement-1 Running Head: Advanced Degrees and Student Achievement A STUDY OF WHETHER HAVING A PROFESSIONAL STAFF WITH ADVANCED DEGREES INCREASES STUDENT ACHIEVEMENT By MEGAN
Chapter 23 Inferences About Means Chapter 23 - Inferences About Means 391 Chapter 23 Solutions to Class Examples 1. See Class Example 1. 2. We want to know if the mean battery lifespan exceeds the 300-minute
International Journal of Management and International Business Studies. ISSN 2277-3177 Volume 4, Number 3 (2014), pp. 309-316 Research India Publications http://www.ripublication.com Management Information
How To Run Statistical Tests in Excel Microsoft Excel is your best tool for storing and manipulating data, calculating basic descriptive statistics such as means and standard deviations, and conducting
Institute of Actuaries of India Subject CT3 Probability and Mathematical Statistics For 2015 Examinations Aim The aim of the Probability and Mathematical Statistics subject is to provide a grounding in
JOB SATISFACTION AMONG TEACHERS OF PRIVATE AND GOVERNMENT SCHOOL: A COMPARATIVE ANALYSIS TILAK RAJ*; LALITA** *ASSOCIATE PROFESSOR, DEPARTMANT OF COMMERCE, M.D.U., ROHTAK **RESEARCH SCHOLAR, DEPARTMENT
Chapter 565 Randomized Block Analysis of Variance Introduction This module analyzes a randomized block analysis of variance with up to two treatment factors and their interaction. It provides tables of