Consumer preference for Eco-Friendly Products as compared to Regular Products.

The term “Eco-Friendly” is used to describe activities which are good for the environment. There are a range of ways in which activities can be eco-friendly, ranging from products which are constructed in an environmentally friendly way to making lifestyle changes which are designed to benefit the environment.With all the problems plastic bags are causing to the natural environment, its little wonder there’s an abundance of alternatives hitting the market. Although biodegradable plastic bags have been around for a while now, many people still don’t know they exist. On the face of it, biodegradable and degradable plastic bags seem like a good concept. They can be used just like any other plastic bag, and then when the time is right, they decompose - possibly within 6 months (or 18 months for oxo-degradable bags).

While it’s not hard to argue that these bags are much better than traditional plastic bags (which can take up to 1,000 years to fully decompose), many environmental organizations argue that biodegradable plastic bags still don’t go far enough in saving the planet. Even if a plastic bag does biodegrade within 5 - 6 months, that’s still plenty of time for it to cause serious issues. A biodegradable plastic bag can still end up in the ocean where it can kill it’s wildlife. When a plastic bag begins to suffocate a baby dolphin, that dolphin will die within minutes. The fact that the bag will decompose within 6 months means nothing - it’s already too late.

Also, given the fact that up to 1 trillion plastic bags are being used on our planet every year, this is still a problem. Even if only say, 1% of plastic bags end up in our oceans that still a lot of plastic bags. Every one of these can kill regardless of their biodegradable properties.

So, while these bags are better than traditional plastic bags, there are much more eco friendly bags around.

ACKNOWLEDGEMENT:

The survey and the report prepared is an effort of a vibrant team. We sincerely acknowledge the interest shown in our survey by our teachers, friends and other people who willingly shared their opinion on the trends, they believe in. Their contribution has helped us in drawing a conclusion about what they think about eco friendly products especially plastic bags.We specially thank our Professor for his constant encouragement and supportive ideas. In this report we have incorporated the valuable suggestions of as many people as possible. The survey was an integral part of our report. We have also used internet for more accurate and authentic information.The analysis was done on the basis of the statistics provided by the survey. The evaluations and the graphs are presented for better understanding. The questionnaire that was used for the survey is also enclosed.

Through this column, it would be our utmost pleasure to express our warm thanks to all the respondents of our survey for their valuable answers and time.

Executive Summary:

“India is one of the top three countries within Asia Pacific that have shown an affinity towards eco-friendly products”

"The Indian shopper is very much in sync with the global consumer when it comes to environmentally sustainable products, from organic products to those tested on animals, there is fairly wide awareness among Indian consumers on what practice is environmentally friendly," Report published in Business Standard.

Eco friendly products are increasingly becoming preferable as compared to normal products because the new consumer does care about the environment. Although there are many factors which have to be taken into consideration while observing the preference for consumers for eco friendly products and normal products.

1. Price difference between normal products and eco-friendly products. Consumers may not buy eco friendly products because of premium prices.

2. Income and education levels of consumers may not affect the buying behavior of eco-friendly products

Consumers in metro cities have already started using products like cars, food products and construction materials. But, the problem with green products is premium price. Most of the green products are premium priced when compared to normal products. Consumers, who don’t have awareness on the global warming and environmental protection, never look into the premium priced products unless they see additional personal benefits rather than social benefits. It does not matter even if the consumers are well educated or falls in high income brackets, their mind set will not change until they get the awareness on benefits of eco-friendly products. At the outset, companies need to start focusing on marketing the benefits of the green products and make people know why they need to use green products.

The view about scenario in India

India is still at nascent stage in using eco friendly products. Consumers started gaining the awareness about environmental protection and global warming slowly. Because people do not aware about the benefits of eco-friendly products, they are going ahead with purchasing normal products that come at cheaper price. Moreover, eco-friendly products are premium priced, which makes people to take back step while purchasing. It is the responsibility of the individuals, organizations and Government to take further steps to increase the awareness on benefits of eco-friendly products.

Introduction:

“The Indian consumer is increasingly becoming conscious of the benefits of environmentally friendly and sustainable practices... 86% Indian consumers surveyed, place faith in energy efficient products and appliances, followed by recyclable packaging (79%),” Global Online Environment and Sustainability Survey by Nielsen said.

This is the upcoming trend, which we further want to delve into.

WHAT ARE ECO FRIENDLY PRODUCTS –

The term “eco-friendly” is used to describe activities which are good for the environment. It is a shortening of “ecologically friendly,” and you may also hear terms like “environmentally friendly” or “green” used to describe similar activities.With so many products claiming to be green or friendly to the environment, it is hard to know what truly constitutes an eco friendly product these days. Whether they are called green products, sustainable products or environmentally responsible products, these eco friendly products cause minimal harm to people and the environment. The manufacturing and/or consumption of these goods have a minimal impact on the environment.

THE DILEMMA

"The sensitivities to want ecologically friendly products are in place, but the high price of items [ranging] from recycled paper to organic foods make it difficult for consumers to purchase them for everyday use," Nielsen Executive Director Dipita Chakraborty said. This is one of the issues we have tried to address through our report which is, that considering the fact that the eco friendly products would fall in a higher price range and other trade offs as compared to the normal products what would the consumer go for. A survey conducted among all the eight countries revealed that India found that advertising was helpful – 86 percent of them said that they make more informed decisions regarding green products, while 57 percent of them said that television ads have the greatest impact on their preference. Hence there is a silver lining for us.

Research Objectives :

What we are trying to do here is to see how consumers actually perceive the benefits of owning an eco-friendly product and how they rate it over a product that consumes normal sources of energy (coal, electricity, and others). Through this research we intend to establish the consumers’ preferences and reach a conclusion based on these factors of their preference.

Hypothesis : H0 Product Preference (Eco friendly) = Product Preference (Non-eco friendly).

H1 There exists some significant difference in their preferences.

Methodology:

For the purpose of finding the appropriate variables for reaching our objective, Focus Group Discussions were decided to be conducted on a sample of units of testing. So, a decision of carrying out three FGDs consisting of 6 students each (consisting of 9 juniors and 9 seniors and also 9 boys and 9 girls) was made. The possible questions to be asked to our sample were compiled and approval of the concerned professor was sought. Afterwards, three FGDs were conducted, accordingly, within a span of two days.

After the FGDs, whatever appropriate variables found were used to frame 30 questions and for the same purpose the present interim report is being presented.

The FGDs are supposed to help us to come up with an acceptable amount of items to be administered to the target audience in order to know about their individual preferences regarding the available Eco- friendly products and the normal products.

FOCUS GROUP DISCUSSION #1:

Date: 25.1.2012

The discussion group consisted of 3 girls and 3 boys [2 girls and 1 boy (senior), 2 boys and 1 girl (junior)].

The approved questions were asked to the group and thus a proper discussion was held without the interference/ prompting from the experimenter’s side.

The 25 minute discussion covered many topics of eco- friendly products that the general public is aware of and think are available in the market. Right from Carbon Credits to Electric cars to environmental pollutants to manufacturing process of plastic buckets to banning of plastic bags in the normal course of our shopping, many things were discussed. Though many things were discussed, the important variables (as far as our project was concerned) came out were:

cost

design - embarrassment

quality - odour , durability

availability- alternatives

government subsidies/initiatives

time span for lasting

strength

technology - convenience (smart phones), comfort

acceptability

awareness (banning, solar, CNG, KYOTO treaty).

The discussion, in general, did bring out the fact that the level of general awareness is not very good in the public, since the discussion seemed to fly- off in different topics and mostly the vague and ambiguous ones.

FOCUS GROUP DISCUSSION #2:

Date: 25.1.2012

The discussion group consisted of 3 girls and 3 boys [3 girls (senior), 3 boys (junior)].

The 19 minute FGD basically revolved around the candidates being very concerned about saving the environment for the future generations and being grateful to the nature by trying to use products which would not harm the nature and preserving it. One point that was found confusing was one of the candidates persisting upon the point of optimum utilization of resources in order to save the environment. Though the point seems worthy of consideration in the long run but is somehow not that convincing in the particular discussion of events and variables.

The candidates seemed to be under peer pressure since most of them were just repeating what their group mates had already talked about.

Again the electric cars came into picture and one of the candidates did not budge from his understanding of use of only paper as ‘eco- friendly’ and had decided to go against whatever the other candidates had to say.

The important variables which came out in this discussion can be noted down as:

Degradable

Awareness

Acceptance

Strength

Reusability

Preservation of resources

Price

Availability

Technology

The members discussed that according to them the awareness in their peer group was not very good and even those who knew about the benefits of using eco- friendly products were still averse to use them because of some fixed habits and being comfortable using the products they were already using. Accordingly, time and technology was a big factor in taking up the use of eco- friendly products.

FOCUS GROUP DISCUSSION #3:

Date: 26.1.2012

The discussion group consisted of 3 girls and 3 boys [2 boys and 1 girl (senior), 2 girls and 1 boy (junior)].

This FGD lasted for around 22 minutes with the respondents not exactly able to decipher the flow and direction of the questions. Though the respondents tried their level best to answer the questions asked but the same variables came out as before with an exception of a dressed up version of old variables i.e Affordability

Acceptability

Availability : as rightly pointed out by a senior “the three A’s important to the Indian consumer.

The group of candidates made it clear that using eco- friendly products was not exactly on their minds all the time and cared very less for it unless some kind of punishment or reward is attached to the terms of use.

The variables that came out in this FGD were:

cost

availibility- alternatives

government subsidies/initiatives

acceptance/time factor

social benefit- Recyclable, Morality

rewards and punishments

time span for lasting

strength

technology - convenience (smart phones), comfort

acceptability

awareness (banning, solar, cng,treaty canada)

Population below the poverty line

Analysis and Results :

Our KMO of 0.865 shows that our sample of 152 people surveyed was adequate for our purposes. The Bartlett’s Spericity is significant at all levels including 0.01 and hence we know that the correlation matrix is not an identity matrix. The Communalities table tells us that the variance in the items have been explained by the factors derived between a range of 48.8% and 80.2%, for various items.

We now see the Total Variance explained table and we see that cumulatively, 65.11% of the variance has been explained by a total of five factors, whose Eigen values are greater than 1.

Next, we look at the Rotated Component Matrix Table. We suppressed all loadings less than 0.4. Due to cross-loading, the items 6,16,18,20 and 24 were removed and we see that we now 19 items have loaded into five factors.

Based on the loadings, we have named the factors as Impact Potential, Attractiveness, Preference, Price and Government Initiatives.

The reliability checks for each of the five factors show that, out of the five, four factors are very reliable. The fifth, Government Initiatives does not appear to be reliable according to the correlation tests done with it.

We then did a Hierarchical Cluster Analysis to identify the clusters and from the agglomeration schedule, six clusters were identified but the profiling of clusters was not possible due to insignificant results obtained for the demographic factor gender.

We also did a Regression analysis using Item 9 as the dependent variable and the five factor scores as the Independent variables. With an R squared of 0.8 (adjusted : 0.793), we can say that 80% of the variance in Consumer Preference (Eco-friendly vs Non eco-friendly plastic bags) is explained by the Independent variables. A standard error of estimate of 0.525 shows the variability we can expect in our results if we predict Y with the regression equation.

The Regression is significant at 0.000 which we see from the ANOVA table.

Using the standard coefficients of the Coefficients table, we form the following regression equation

Y= 0.086f1 + 0.035f2 + 0.883f3 + .110f4 - .007f5

We also see from the significance column of coefficients that only factors 1, 3 and 4 are significant.

Factor Naming :

Factor 1 : Impact Potential

We can see from the loadings that, the items can be divided into two clear halves. The first half has factors which have the potential to affect our lives and choices, such as :

1. The banning of plastic bags will affect our lives.2. I will start using plastic bags if my friends recommend them.

The second half of items are those which a consumer feels has the potential to affect the environment, such as :

1. Recyclability is better for the environment.2. Biodegradability is better for the environment.

Hence, these items clearly have the ability to impact us and our environment and is so named.

Factor 2 : Attractiveness

The items that loaded into this factor are items as design of plastic bags, texture of the material as well as incentives and the effectiveness of laws enforcing usage of eco-friendly bags. Based on these items, we can see that incentives, designs, textures and so on might encourage consumers to consider using the eco-friendly plastic bags while laws and bans might force consumers to use them but may not win their support. It is then a matter of attracting consumers. Hence we can named the factor as Attractiveness.

Factor 3 : Preference

This factor is so named because the items loaded here point towards the inherent nature of people to gravitate towards their preferred items irrespective of the effect on the environment.

Factor 4 : Price

The 4th factor is price, given how conscious consumers in general and Indian consumers specially are conscious about prices.

Factor 5 : Government Initiatives.

The items that loaded into this factor are disposability and government initiatives towards encouraging use of such products.

Conclusion:

To conclude, we can see that Factor 1 ( Impact Potential), Factor 3 (Preference) and Factor 4 (Price) prove most important in our study with particular emphasis on Factor 3 which has a standardized beta of 0.88 followed by Factor 4 with the next highest beta of 0.11.

We can thus conclude that the consumers’ inclination towards eco-friendly products, in particular plastic bags of the eco-friendly persuasion, are influenced more by factors such as design and incentives and price rather than a concern for the environment.

Based on the above, we feel that, we may safely conclude that participants of our study are not indifferent to eco-friendly and non-ecofriendly products (plastic bags for our study), but rather, there exists a difference, that is fueled by the factors mentioned above.

Managerial Implications:

From the point of view of a manager, or rather, in our case, a policy maker aiming for a cleaner greener environment, we suggest that the focus be on the attractiveness of the product (plastic bags). Improving the attractiveness of the bags, either through better quality or designs or by providing incentives for use, the policy maker may garner a favourable response.

The second takeaway is that, Indian consumers in particular will continue to be price-conscious and so, the price of such products must also be affordable.

Emphasizing on the moral aspects, or forcing change on the consumers will not result in significant results as the study tells us quite clearly. Perhaps, Indian consumers do not rate environmental sustainability important or as we see from the study, convenience is paramount. In either case, we found from conducting our study and fgd in particular that awareness of the long term consequences of using non-eco-friendly products, is low. Thus, an entrepreneur wishing to tap into the ‘green market’ would be well advised to run an awareness campaign first to educate his potential customers.

APPENDICES

APPENDIX A - FACTOR ANALYSIS

APPENDIX B - REGRESSION

APPENDIX C - CLUSTER ANALYSIS

APPENDIX A – FACTOR ANALYSIS

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .865

Bartlett's Test of Sphericity

Approx. Chi-Square 1250.434df 171Sig. .000

Communalities

InitialExtraction

I1 1.000 .570I2 1.000 .692I3 1.000 .784I4 1.000 .731I5 1.000 .678I7 1.000 .746I8 1.000 .614I9 1.000 .800I10 1.000 .609I11 1.000 .717I12 1.000 .727I13 1.000 .508I14 1.000 .488I15 1.000 .530I17 1.000 .495I19 1.000 .602I21 1.000 .802I22 1.000 .577I23 1.000 .700

Extraction Method: Principal Component Analysis.

Total Variance Explained

Component Initial Eigenvalues

Extraction Sums of Squared Loadings

Rotation Sums of Squared Loadings

Total

% of Variance

Cumulative % Total

% of Variance

Cumulative % Total

% of Variance

Cumulative %

16.449

33.941

33.941 6.44933.941

33.941 5.355 28.183 28.183

22.123

11.174

45.115 2.12311.174

45.115 2.556 13.454 41.638

3 1.356 7.135 52.250 1.356 7.135 52.250 1.871 9.850 51.4874 1.314 6.915 59.165 1.314 6.915 59.165 1.300 6.840 58.3275 1.130 5.949 65.114 1.130 5.949 65.114 1.289 6.787 65.1146 .934 4.918 70.0327 .803 4.224 74.2568 .751 3.952 78.2089 .633 3.332 81.53910 .523 2.752 84.29111 .478 2.518 86.81012 .455 2.395 89.20513 .407 2.140 91.34514 .352 1.854 93.19915 .323 1.699 94.89916 .299 1.572 96.47117 .255 1.340 97.81118 .233 1.229 99.03919

.183 .961100.000

Extraction Method: Principal Component Analysis.

Rotated Component Matrix(a)

Component1 2 3 4 5

I1 .697I2 .806I3 .861I4 .795I5 .763I7 .750I8 .629I9 .883I10 .654I11 .826I12 .811I13 .435I14 .459I15 .626I17 .670I19 .751I21 .875I22 .575I23 .774

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.

a Rotation converged in 6 iterations.

Reliability (Overall)

Reliability Statistics

Cronbach's Alpha

N of Items

.871 19

Reliability (Fac 1)

Reliability Statistics

Cronbach's Alpha

N of Items

.907 9

Reliability (Fac 2)Reliability Statistics

Cronbach's Alpha

N of Items

.729 5

Reliability (Fac 3)Correlations

I7 I9I7 Pearson Correlation

1 .566(**)

Sig. (2-tailed).000

N152 152

I9 Pearson Correlation.566(**) 1

Sig. (2-tailed).000

N152 152

** Correlation is significant at the 0.01 level (2-tailed).

Reliability (Fac 5)

Correlations

I5 I15I5 Pearson Correlation

1 .090

Sig. (2-tailed).271

N152 152

I15 Pearson Correlation.090 1

Sig. (2-tailed).271

N152 152

APPENDIX B - REGRESSION

Regression

Model Summary

Model R R SquareAdjusted R Square

Std. Error of the Estimate

1 .894(a) .800 .793 .525a Predictors: (Constant), REGR factor score 5 for analysis 1, REGR factor score 4 for analysis 1, REGR factor score 3 for analysis 1, REGR factor score 2 for analysis 1, REGR factor score 1 for analysis 1

ANOVA(b)

ModelSum of Squares df Mean Square F Sig.

1 Regression161.101 5 32.220 116.823 .000(a)

Residual40.267 146 .276

Total201.368 151

a Predictors: (Constant), REGR factor score 5 for analysis 1, REGR factor score 4 for analysis 1, REGR factor score 3 for analysis 1, REGR factor score 2 for analysis 1, REGR factor score 1 for analysis 1b Dependent Variable: I9

Coefficients(a)

ModelUnstandardized Coefficients Standardized Coefficients t Sig.

B Std. Error Beta1 (Constant)

3.632 .043 85.255.000

REGR factor score 1 for analysis 1

.100 .043 .086 2.328.021

REGR factor score 2 for analysis 1

.041 .043 .035 .959.339

REGR factor score 3 for analysis 1

1.019 .043 .883 23.852.000

REGR factor score 4 for analysis 1

.127 .043 .110 2.974.003

REGR factor score 5 for analysis 1

-.008 .043 -.007 -.186.852

a Dependent Variable: I9

APPENDIX C – CLUSTER ANALYSIS

Ward Linkage

Agglomeration Schedule

Stage

Cluster Combined

Coefficients

Stage Cluster First Appears

Next StageCluster 1 Cluster 2 Cluster 1 Cluster 21 55 67 .000 0 0 602 54 66 .000 0 0 1043 5 20 .000 0 0 304 6 23 .037 0 0 195 33 150 .104 0 0 136 57 69 .174 0 0 907 44 137 .259 0 0 168 7 16 .349 0 0 829 39 151 .459 0 0 5110 47 119 .572 0 0 5011 40 124 .688 0 0 1512 19 34 .834 0 0 6313 26 33 .983 0 5 7214 15 36 1.155 0 0 4115 40 48 1.328 11 0 9816 44 52 1.504 7 0 3017 3 14 1.689 0 0 5818 60 94 1.891 0 0 8519 6 132 2.125 4 0 3920 1 22 2.363 0 0 3721 41 116 2.603 0 0 5122 62 106 2.849 0 0 8523 21 113 3.097 0 0 6924 11 53 3.345 0 0 6725 86 96 3.612 0 0 8326 61 92 3.887 0 0 9927 2 8 4.165 0 0 73

28 50 144 4.465 0 0 7029 102 104 4.773 0 0 9630 5 44 5.098 3 16 4031 130 138 5.426 0 0 8832 80 98 5.758 0 0 11233 97 109 6.092 0 0 7534 76 91 6.428 0 0 13235 28 152 6.765 0 0 6636 133 147 7.109 0 0 6737 1 24 7.478 20 0 10538 30 32 7.857 0 0 9539 6 141 8.238 19 0 10240 5 129 8.632 30 0 12141 12 15 9.032 0 14 7242 10 126 9.432 0 0 10043 18 139 9.833 0 0 8244 72 83 10.238 0 0 8345 9 35 10.652 0 0 5746 112 114 11.075 0 0 6347 49 110 11.499 0 0 11148 115 142 11.927 0 0 11149 4 45 12.369 0 0 12550 47 128 12.817 10 0 6851 39 41 13.318 9 21 7752 75 79 13.841 0 0 10453 87 108 14.386 0 0 11354 56 68 14.937 0 0 7555 63 90 15.496 0 0 8956 31 117 16.061 0 0 9357 9 123 16.677 45 0 11658 3 37 17.328 17 0 11959 64 99 17.981 0 0 8960 55 71 18.635 1 0 10361 13 25 19.294 0 0 8662 46 145 19.963 0 0 10263 19 112 20.640 12 46 8464 65 73 21.329 0 0 9965 84 85 22.049 0 0 11766 17 28 22.777 0 35 9467 11 133 23.514 24 36 9468 47 118 24.284 50 0 12469 21 148 25.079 23 0 10570 50 121 25.892 28 0 11071 70 82 26.708 0 0 9172 12 26 27.552 41 13 10873 2 43 28.410 27 0 8774 100 103 29.282 0 0 9775 56 97 30.169 54 33 10176 134 135 31.062 0 0 10077 39 125 31.967 51 0 12178 78 88 32.878 0 0 11879 51 120 33.803 0 0 10980 74 107 34.740 0 0 106

81 95 105 35.691 0 0 11582 7 18 36.653 8 43 12083 72 86 37.764 44 25 11884 19 149 38.885 63 0 10885 60 62 40.028 18 22 12286 13 131 41.267 61 0 12087 2 140 42.544 73 0 11488 38 130 43.882 0 31 9389 63 64 45.262 55 59 12290 57 111 46.718 6 0 12991 27 70 48.223 0 71 12792 29 136 49.761 0 0 10993 31 38 51.343 56 88 13094 11 17 52.942 67 66 13995 30 122 54.549 38 0 11096 89 102 56.170 0 29 12397 100 127 57.798 74 0 11298 40 146 59.441 15 0 11999 61 65 61.103 26 64 128100 10 134 62.830 42 76 136101 56 101 64.614 75 0 131102 6 46 66.434 39 62 114103 55 81 68.464 60 0 113104 54 75 70.563 2 52 131105 1 21 72.675 37 69 130106 74 93 74.831 80 0 126107 42 143 77.128 0 0 138108 12 19 79.505 72 84 116109 29 51 81.896 92 79 137110 30 50 84.339 95 70 133111 49 115 86.804 47 48 129112 80 100 89.307 32 97 134113 55 87 92.087 103 53 134114 2 6 94.913 87 102 125115 58 95 97.776 0 81 127116 9 12 100.660 57 108 142117 59 84 103.675 0 65 128118 72 78 106.758 83 78 123119 3 40 109.932 58 98 124120 7 13 113.560 82 86 137121 5 39 117.588 40 77 133122 60 63 121.777 85 89 126123 72 89 126.403 118 96 140124 3 47 131.136 119 68 143125 2 4 136.295 114 49 138126 60 74 141.535 122 106 132127 27 58 146.938 91 115 145128 59 61 152.360 117 99 140129 49 57 157.785 111 90 139130 1 31 163.840 105 93 142131 54 56 170.158 104 101 141132 60 76 177.577 126 34 147133 5 30 185.019 121 110 136

134 55 80 192.949 113 112 135135 55 77 202.162 134 0 145136 5 10 211.949 133 100 144137 7 29 221.940 120 109 146138 2 42 231.931 125 107 148139 11 49 243.939 94 129 144140 59 72 256.089 128 123 141141 54 59 269.790 131 140 147142 1 9 283.831 130 116 143143 1 3 301.753 142 124 146144 5 11 323.254 136 139 150145 27 55 352.702 127 135 149146 1 7 386.970 143 137 148147 54 60 431.973 141 132 149148 1 2 478.558 146 138 150149 27 54 543.210 145 147 151150 1 5 628.467 148 144 151151 1 27 755.000 150 149 0

Crosstabs

Ward Method * gender

Crosstab

Count gender

Total2 1Ward Method

1 27 20 472 8 6 143 16 19 354 8 10 185 9 16 256 2 11 13

Total 70 82 152

Chi-Square Tests

Value dfAsymp. Sig.

(2-sided)Pearson Chi-Square 9.108(a) 5 .105Likelihood Ratio 9.711 5 .084N of Valid Cases 152

a 0 cells (.0%) have expected count less than 5. The minimum expected count is 5.99.

Ward Method * age

Crosstab

Count Age Total

1 2 3 4 5 6 7 8 9Ward Method

10 4 14 19 6 1 1 0 2 47

2 0 2 3 6 3 0 0 0 0 143 1 2 10 10 7 1 3 0 1 354 1 9 5 1 2 0 0 0 0 185 6 11 5 2 0 0 0 1 0 256 0 5 3 1 1 1 2 0 0 13

Total 8 33 40 39 19 3 6 1 3 152

Chi-Square Tests

Value dfAsymp. Sig.

(2-sided)Pearson Chi-Square 72.710(a) 36 .000Likelihood Ratio 73.071 36 .000Linear-by-Linear Association

12.771 1 .000

N of Valid Cases 152

a 40 cells (80.0%) have expected count less than 5. The minimum expected count is .09.