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ABC (UK) Ltd Employee Satisfaction Survey 2012

This is an illustration of the style of the outputs you can expect if you use the Quantify Open Employee Satisfaction Survey. The reports in this illustration show only a few questions. Your report will naturally be more comprehensive.

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[email protected] quantify.co.uk

Copyright © 1990 ­ 2012 QUANTIFY! Ltd

How to use the reports

Looking at prior occasion comparison reports (if you have them)

Look in the reports for asterisks (x). These show differences between the prior occasion and the recent results which are statistically significant at the confidence level shown in the report footer after applying a design factor as shown. Don't even consider taking action about any differences not marked with asterisks. These differences might arise just through sampling error. If a result is improved by comparison with the prior occasion, then people are more satisfied with this issue than they were before. It may be worth letting them know about this ­ they may not realise that things are on an improving trend. If your result is less good than before, consider doing something to address the problem ­ if it is a problem. Factors which will influence your decision whether to do anything and if so what to do will include · · · The number of people affected. If one small subset seems to have a problem but the survey as a whole doesn't there may be no need to act. Corroborative evidence. If you have information from other sources which tells you that there is a problem, you may be more convinced of the need to act. The nature of the problem. Is it something you can change? It may be out of your control, or just too expensive to change to make it commercially wise to attempt to. Or on the other hand, it may be just the way things have evolved and it could be just as easy and cheap to do things a different way to please people. The importance of the issue. It may be something people are fed up with but which they wouldn't see as crucial. Or it might be exactly that ­ crucial.

·

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Looking at your results for this occasion

You can't tell from these reports whether your overall results are good, bad or indifferent. With no standard to compare with, you don't know what "score" would be a good one for any particular question. The reports are still useful, though. You can tell whether there are some groups of people who are more satisfied than others with the issues your survey measures. In the subset tabulations, look for asterisks (x) in the significance matrix below the row of percentile results. These show where differences between subsets are statistically significant at the confidence level shown in the report footer after applying a design factor as shown. Don't even consider taking action about any differences not marked with asterisks. These differences might arise just through sampling error. If one subset result is better than another, then these people are more satisfied with this issue than the other subset. It may be worth letting them know about this ­ they may not realise that they are better off. For the subset group whose result is less good (lower), consider doing something to address the problem ­ if it is a problem. Factors which will influence your decision whether to do anything and if so what to do will include · · · The number of people affected. If one small subset seems to have a problem but the survey as a whole doesn't there may be no need to act. Corroborative evidence. If you have information from other sources which tells you that there is a problem, you may be more convinced of the need to act. The nature of the problem. Is it something you can change? It may be out of your control, or just too expensive to change to make it wise to attempt to. Or on the other hand, it may be just the way things have evolved and it could be just as easy and cheap to do things a different way to please people. The importance of the issue. It may be something people are fed up with but which they wouldn't see as crucial. Or it might be exactly that ­ crucial.

·

Copyright © 1990 ­ 2012 QUANTIFY! Ltd

Questionnaire

We include a copy of the questionnaire for reference (only the first page in this illustration). It begins on the next page.

Copyright © 1990 ­ 2012 QUANTIFY! Ltd

Employee Satisfaction Survey

This survey is run by independent survey consultants QUANTIFY! Ltd. We designed it for any employer, however small, to use to monitor employee satisfaction in a way only big companies can usually afford.

QUANTIFY! only report back averages based on groups of people and we never report on any group smaller than three people, so your personal answers will never be revealed to your employer.

Please tick one box for each statement to show how much you agree or disagree. Then send the completed questionnaire to QUANTIFY! postage free, in the next week or so. It will cost you nothing, just five or ten minutes of your time, and it might help make things better for you at work. No one within your organisation will ever see your replies. To make sure it doesn't get forgotten could we ask you please to do it now? Because organisations and jobs vary, there may be some items which just don't apply to you. Please leave them blank (don't tick In between) and go on to the next.

In Strongly disagree Disagree between

1 2 3

The reports might show that some groups of people are more satisfied than others with particular issues about their employment. We may also be able to report how employee satisfaction in your organisation compares with other employers.

Tick one box in each row to show how much you agree or disagree. If the question doesn't apply to you, leave it blank. You and your job 1. 2. 3. 4. 5. 6. 7. 8. 9. I know what's expected of me in my job My job makes good use of my abilities My job gives me a feeling of achievement I have to do the same thing all the time My job always keeps me busy My job allows me to deliver quality results My job never requires me to do anything morally wrong I can get on with my job on my own I am allowed to use my own judgement in my work

Agree

4

Strongly agree

5

10. I have the opportunity to contribute to decision-making 11. I understand how I can contribute to organisation objectives Customers "Customers" ­ We mean the people who benefit from the work you do. If you help make a product, your customer is the user of the product. If you provide an internal service, the colleagues you support are your customers. 12. Decisions here are driven by customer requirements 13. My job allows me to meet my customers' needs 14. My job allows me to please my customers

© Copyright QUANTIFY! Ltd 2001

qfm.doc

Response account

Progress report

This report shows the responses received and when they arrived. The summary shows the response rate for your survey.

Copyright © 1990 ­ 2012 QUANTIFY! Ltd

Progress report

ABC Ltd Employee Satisfaction Survey

Summary

6000 Cumulative

Employee Satisfaction Survey

5000 4000 3000 2000 1000 0

Cumulative

Daily

Returned 5,199

Response rate

82%

Totals to date 2,380 Web 2,819 UK Foreign 5,199

Responses daily Date 08 Aug 2011 (Mon) 09 Aug 2011 (Tue) 10 Aug 2011 (Wed) 11 Aug 2011 (Thu) 12 Aug 2011 (Fri) 13 Aug 2011 (Sat) 14 Aug 2011 (Sun) 15 Aug 2011 (Mon) 16 Aug 2011 (Tue) 17 Aug 2011 (Wed) 18 Aug 2011 (Thu) 19 Aug 2011 (Fri) 20 Aug 2011 (Sat) 21 Aug 2011 (Sun) 22 Aug 2011 (Mon) 23 Aug 2011 (Tue) 24 Aug 2011 (Wed) 25 Aug 2011 (Thu) 26 Aug 2011 (Fri) 27 Aug 2011 (Sat) 28 Aug 2011 (Sun) 29 Aug 2011 (Mon) 30 Aug 2011 (Tue) 31 Aug 2011 (Wed) 01 Sep 2011 (Thu) 02 Sep 2011 (Fri) 03 Sep 2011 (Sat) 04 Sep 2011 (Sun) 05 Sep 2011 (Mon) 06 Sep 2011 (Tue) 07 Sep 2011 (Wed) 08 Sep 2011 (Thu) 09 Sep 2011 (Fri) 10 Sep 2011 (Sat) 11 Sep 2011 (Sun) 12 Sep 2011 (Mon) 13 Sep 2011 (Tue) 14 Sep 2011 (Wed) 15 Sep 2011 (Thu) 16 Sep 2011 (Fri) 17 Sep 2011 (Sat) 18 Sep 2011 (Sun) 19 Sep 2011 (Mon) 20 Sep 2011 (Tue) 21 Sep 2011 (Wed) 22 Sep 2011 (Thu) 23 Sep 2011 (Fri) Total To date 0 934 934 720 1654 925 2579 501 3080 3080 3080 602 3682 615 4297 415 4712 310 5022 110 5132 32 5164 5164 35 5199 5199 5199 5199 5199 5199 5199 5199 5199 5199 5199 5199 5199 5199 5199 5199 5199 5199 5199 5199 5199 5199 5199 5199 5199 5199 5199 5199 5199 5199 5199 5199 5199

934 720 183 74

742 427

52 230 115 72

550 385 300 238 110 32 35

Outstan ding % Notes 0% 2510 Paper questionnaires mailed 15% 5396 Web invitations sent 26% 4676 41% 3751 49% 3250 49% 3250 49% 3250 58% 2648 68% 2033 Web reminder sent 74% 1618 79% 1308 81% 1198 82% 1166 82% 1166 82% 1131 82% 1131 82% 1131 82% 1131 82% 1131 82% 1131 82% 1131 82% 1131 82% 1131 82% 1131 82% 1131 82% 1131 82% 1131 82% 1131 82% 1131 82% 1131 82% 1131 82% 1131 82% 1131 82% 1131 82% 1131 82% 1131 82% 1131 82% 1131 82% 1131 82% 1131 82% 1131 82% 1131 82% 1131 82% 1131 82% 1131 82% 1131 82% 1131

Mailed 2510 3820

Cum. mailed 2510 6330 6330 6330 6330 6330 6330 6330 6330 6330 6330 6330 6330 6330 6330 6330 6330 6330 6330 6330 6330 6330 6330 6330 6330 6330 6330 6330 6330 6330 6330 6330 6330 6330 6330 6330 6330 6330 6330 6330 6330 6330 6330 6330 6330 6330 6330

Daily

Mailed 6,330

1,000 900 800 700 600 500 400 300 200 100 -

Quantitative reports

Subset list

This shows each subset we have analysed and the number of informants who fell into each. If you add together any group of subsets which should comprise the whole survey, there may be a shortfall, which is accounted for by any informants who chose to tick no box to describe themselves for the classification system concerned. To preserve the anonymity of the informants, we set a lower limit on the size of subsets which may be reported. The smallest subset we will report is one comprising 3 informants but if you have asked us to apply a higher limit, this will be reflected in the subset reports you see. We provide two versions of the report · · Full list Shows all the subsets we have defined and analysed for you. Short list Shows only those which survived the minimum subset size test described above.

Copyright © 1990 ­ 2012 QUANTIFY! Ltd

ABC Ltd Employee Satisfaction Survey Subsets of response

Subset Number 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 Responses Description 330 32 241 87 80 50 42 216 17 16 5 28 22 6 3 0 0 0 197 59 18 45 0 240 2 11 1 0 0 1 0 0 1 1 0 0 0 1 3 Whole Survey Male Female Up to 1 year 1 year but less than 5 years 5 years but less than 10 years 10 years or more Research & development Operations / Service delivery (no customer contact) Customer contact Service delivery Customer service (After sales) Sales / Marketing Purchasing Accounts receivable / sold ledger Accounts payable / bought ledger General accounting / general ledger Other Finance / Accounting Information & Communication Technology / Systems Manual worker - unskilled Manual worker - skilled Clerical / administrative Technical / professional Manager British Irish Other white background Mixed - White & Black Caribbean Mixed - White & Black African Mixed - White & Asian Other mixed background Indian Pakistani Bangladeshi Other Asian background Black Caribbean Black African Other Black background Chinese Any other background

19 October 2012 Version: 25 May 2006

Copyright © QUANTIFY! Ltd 1990 - 2006

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ABC Ltd Employee Satisfaction Survey Subsets of response

Subset Number 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 97 98 99 100 102 104 117 Responses Description 330 32 241 87 80 50 42 216 17 16 5 28 22 6 3 197 59 18 45 240 11 3 Whole Survey Male Female Up to 1 year 1 year but less than 5 years 5 years but less than 10 years 10 years or more Research & development Operations / Service delivery (no customer contact) Customer contact Service delivery Customer service (After sales) Sales / Marketing Purchasing Accounts receivable / sold ledger Accounts payable / bought ledger Manual worker - unskilled Manual worker - skilled Clerical / administrative Technical / professional British Other white background Any other background

19 October 2012 Version: 25 May 2006

Copyright © QUANTIFY! Ltd 1990 - 2006

Page 1

Quantitative reports

Response Tally Report

This report shows the response options available for each item and the number and percentage of informants who ticked each one. The percentage is based on the total number of informants shown in the report, which excludes those who made no intelligible response. We can produce such a report for any or all of the subsets we have created, or indeed for any others we create on your instructions, but we expect that you will find the Subset Tabulation reports make comparisons between subsets easier.

Copyright © 1990 ­ 2012 QUANTIFY! Ltd

Response Tally Report ABC Ltd

Subset:

Questions in Normal order Employee Satisfaction Survey

79 Whole Survey

Number of respondents answering as indicated Total Strongly disagree Disagree In between Agree Strongly agree

Question Number Question text

1 2 3 4

I know what's expected of me in my job My job makes good use of my abilities My job gives me a feeling of achievement I have to do the same thing all the time (r)

327 326 324 315 9 2.76% 4 1.23% 29 9.21%

2 0.61% 19 5.83% 13 4.01% 134 42.54%

17 5.20% 51 15.64% 63 19.44% 77 24.44% End of Report

162 49.54% 156 47.85% 170 52.47% 61 19.37%

146 44.65% 91 27.91% 74 22.84% 14 4.44%

Report version: 14 Nov 2006 Printed: 19 October 2012

Copyright © QUANTIFY! Ltd 1990 - 2006

Page 1

Quantitative reports

Subset Tabulations

Each report shows the subsets within one of the classification systems used in the survey. It shows the result for each item from each subset. Results are expressed as percentiles, and are inverted for negatively keyed items marked (r), so that in all cases a higher value represents a higher level of satisfaction. In case of doubt, please refer to the Key to Percentiles which precedes the actual subset tabulation reports below. In the headings, Subset number is the subset's reference number corresponding with the number shown in the subset listing; Subset size is the number of informants in this subset. Significance Below the row of percentile results, there is a significance matrix. The numbers at the left-hand end of the rows are the subset numbers; note that they are the same as the subset number at the top of the column they appear in. When the difference between two subsets is statistically significant an asterisk (x) appears in the matrix where the two subsets intersect. These significance indicators are based on the confidence level and design factor indicated at the foot of the report. We include the whole survey "subset" on the tabulation by gender to provide you with whole survey results expressed as percentiles. The comparison with other subsets isn't valid in this case because any other subset comprises people who are also included in the whole survey. Important Note The figures shown on these reports are average results expressed as percentiles.

They are not percentages. They do not set out what percentage of informants ticked any

particular option, or combination of options. Please see the next page for an explanation of percentiles.

Copyright © 1990 ­ 2012 QUANTIFY! Ltd

Percentiles

Why use them? Expressing results as percentiles has two main advantages over the use of percentages. It means that every response is taken into account and given its appropriate weight and it facilitates comparison of results within and between surveys.

Comprehensiveness - Results expressed as percentiles get more information out of your data. Survey results are often expressed as the percentage of informants who ticked, say, the favourable and very favourable options in a scale. This approach can conceal real and significant differences between subsets or between one occasion and another. Occasion I am proud to work here I am proud to work here 2011 2012

Strongly disagree Strongly agree Total Agree & disagree

Disagree

In between

Agree

Percentile

13% 6%

19% 12%

27% 41%

29% 17%

12% 24%

41% 41%

52.0 60.3

If we express these results as the percentage who ticked Agree or Strongly agree, these two occasions appear the same, with 41% and we might conclude that there had been no improvement. But look at the shift from Strongly disagree and Disagree into the In between category and the shift from Agree into Strongly Agree. This is good news that any organisation would surely be pleased about, but it doesn't register if all we are interested in is the total of the Agree and Strongly agree ticks. If, instead, we work out an average response, and express it as a percentile, we get different results, which take account of everyone's answers, not just those who ticked two of the options. So although survey results are often expressed as percentages, QUANTIFY does not recommend it. Why bother gathering data on a five point scale if we are then going to ignore, anyone who ticked three of the five boxes? Why invite the different Agree and Strongly agree responses if we are going to treat them as if they were both the same? Results expressed as percentiles get more information out of your data. Some more examples

Strongly disagree Disagree In between Agree Strongly agree Total Agree & disagree Percentile

I am proud to work here I am proud to work here I am proud to work here

0% 0% 0%

0% 0% 0%

0% 0% 0%

100% 50% 0%

0% 50% 100%

100% 100% 100%

75 87.5 100

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Comparability

A percentile (abbreviated to %ile) also provides a way of converting results measured using different scales (response frames) to a common scale of 100 points. Even when different scales have not been used, it can often be easier to understand a result expressed as a percentile than a raw score. Imagine, for example that we want to compare results from one survey, or two or more different surveys and some results are on a scale from 1-5 and others on a scale from 1-7. The same answer can mean different things according to which scale applies. Say two questions had the answer 3. On the first scale (1-5), this is exactly the midpoint, but on the second (1-7) it is closer to the lowest possible score (1) than to the highest (7). By expressing them all as percentiles, all the results are rendered onto a scale from 0 to 100 and can easily be compared.

Percentiles; How do we calculate them?

Strongly In Strongly A tick Using the same example as above, we allocate a points disagree Disagree between Agree agree for score to each of the options on a scale, so we might say a tick in Strongly disagree is worth 1 point, Disagree, 2 is worth 1 point 2 points 3 points 4 points 5 points points and so on until Strongly agree, which is worth 5 points. These points are referred to as Raw Scores. Taking each informant's Raw score for a question, we can work out an average raw score for any question in any subset.

We then work out how far along its possible scale each average result lies, and express it as a percentage of the way from the worst possible result toward the best possible, we can say at which percentile point on its scale the average lies, and make the results comparable one with another. To calculate a percentile from a raw score, calculate

100 ×

(RawScore - Min )

Range

Some examples Scale 1-5 1-7 1-5 1-7 1-7 0-1 1-5 Average Raw Score 3 3 2.3 2.3 4 0.45 1 Percentile 50 33.33 32.5 21.67 50 45 0

where RawScore is the average raw score; Min is the minimum of the scale; Range is the maximum of the scale minus the minimum of the scale. Taking as an example the fourth line in the table (right), a Raw score of 2.3 on a scale from 1 to 7; RawScore = 2.3; Min = 1; Range = 7 - 1 = 6. %ile = 100 × (RawScore - Min ) = 100 × (2.3 - 1) = 100 × 1.3 = 21.67 Range 6 6

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Sub-set Tabulation Results expressed as Percentiles ABC Ltd Employee Satisfaction Survey

Autonomy Subset number Subset population (where known) Subset (sample) size Subset name 79 0 330

Whole Survey

Significant differences marked x at intersections.

82 0 87

Up to 1 year

83 0 80

1 year but less than 5 years

84 0 50

5 years but less than 10 years

85 0 42

10 years or more

8 I can get on with my job on my own Subset results expressed as Percentiles

82

77

80

89

83

Differences between subsets: significance matrix Subset number

Whole Survey ______ |______ |______ |______ |______ 79 x Up to 1 year ______ |______ |______ |______ 82 x 1 year but less than 5 years ______ |______ |______ 83 x 5 years but less than 10 years ______ |______ 84 73 71 72 75 77

79

82

83

84

85

9 I am allowed to use my own judgement in my work Subset results expressed as Percentiles

Differences between subsets: significance matrix Subset number

Whole Survey ______ |______ |______ |______ |______ 79 Up to 1 year ______ |______ |______ |______ 82 1 year but less than 5 years ______ |______ |______ 83 5 years but less than 10 years ______ |______ 84 78 77 77 79 75

79

82

83

84

85

13 My job allows me to meet my customers' needs Subset results expressed as Percentiles

Differences between subsets: significance matrix Subset number

Whole Survey ______ |______ |______ |______ |______ 79 Up to 1 year ______ |______ |______ |______ 82 1 year but less than 5 years ______ |______ |______ 83 5 years but less than 10 years ______ |______ 84

79

82

83

84

85

TOPIC AVERAGE 670 Autonomy Subset results expressed as Percentiles

Differences between subsets: significance matrix Subset number

77

75

76

81

78

Whole Survey ______ |______ |______ |______ |______ 79 Up to 1 year ______ |______ |______ |______ 82 1 year but less than 5 years ______ |______ |______ 83 5 years but less than 10 years ______ |______ 84

79

82

83

84

85

Differences marked x are significant at the 95% confidence level after applying a design factor of 1.30 The results are expressed as percentiles. They are NOT PERCENTAGES. Please refer to the accompanying explanation of percentiles.

If population sizes are shown, significance indicators take account of Finite Population Correction. Printed: 19 October 2012 Copyright © 1990 - 2011 QUANTIFY! Ltd Report version 04 Nov 2011 Page 1

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