Tony Veal, Research Methods in Leisure and Tourism, 4 th Edition © Pearson Education Limited 2011 Slide 13.1 Chapter 13: Sampling: Quantitative and Qualitative

Tony Veal, Research Methods in Leisure and Tourism, 4th Edition © Pearson Education Limited 2011

Slide 13.1

Chapter 13:

Sampling: Quantitativeand Qualitative


Slide 13.2

Contents

• Samples and populations• Representativeness• Sample size• Weighting• Sampling for qualitative research


Slide 13.3

Samples and populations

• Population: – Total category of subjects that is the focus of attention in

a particular research project (can be non-human)

• Sample:– A number of subjects drawn from the population

• Two key issues:1.What procedures must be followed to ensure that the

sample is representative of the population?

2.How large should the sample be?


Slide 13.4

Representativeness

• Achieved by Random sampling• A systematic selection process which ensures that

all members of the population have an equal chance of inclusion in the sample

• Designed to ensure representativeness• An unrepresentative sample is: biased• How is random sampling achieved in practice?


Slide 13.5

Sampling for household surveys

• Ideally – E.g. 40 million population – sample of 1000: all 40 million

names put in a drum and 1000 drawn

• In practice:– For national/regional surveys – multi-stage sampling used

1. Select states/regions

2. Within state/region select local government area (lga) or constituencies/electorates

3. Within lgas or constituencies/electorates for face-to-face interviews select streets (telephone surveys select numbersat this point)

4. Select ‘clusters’ of 10–15 houses


Slide 13.6

Sampling for telephone surveys

• Telephone numbers selected at random from telephone directory

• For large-scale surveys: automated by Computer-Aided telephone Interviewing (CATI)

• Requires access to electronic directory with residential/business numbers identified

• No directories for mobile phones• For household and telephone surveys: select

person in household randomly: e.g. person with next birthday


Slide 13.7

Sampling for site/user/visitor surveys

• Alternative 1: Stationary interviewer - mobile user: – E.g. interviewing at entrance/exit– Sample by selecting: ‘next person to pass entrance/exit point’

• Alternative 2: Stationary user - mobile interviewer – E.g. interviewing people on a beach– Interviewers should have a set route/rules to follow – e.g.

‘interview every third person/group’

• Alternative 3: Handouts– Handing out questionnaires to (all) visitors for self-completion– Not generally recommended unless closely supervised –

generally very poor response rates


Slide 13.8

Sampling for street/quota surveys

• Can be used when data are available on key characteristics of population:– Age/sex structure of a community – from Census

• Interviewing target numbers determined by population characteristics– E.g. If Population Census indicates 12% retired: if

overall sample size is 100: interview 12 retired people


Slide 13.9

Sampling for mail surveys

• Sample from mail-out list• 100% sample often used


Slide 13.10

Sampling for complex events and destination surveys

• Different components will conform to above guidelines – mostly site surveys

• Problem lies in combining data from different sources for an overall result, if required


Slide 13.11

Sample size

• Required sample size is not related to population size (except for small populations – see later)

• Criteria:– The required level of precision in the results– The level of detail in the proposed analysis– The available budget


Slide 13.12

Level of precision – confidence intervals

• A statistic (finding) from a sample survey is an estimate of the population statistic

• In a randomly drawn sample the sample value has a certain probability of being in a certain range either side of the population value– E.g. 95% probability of being within 2 ‘standard

errors’

• See ‘Normal distribution’– Theoretical: imagine drawing lots of samples: some would

be accurate, some not– Discussed further in Chapter 17


Slide 13.13

NUMBER OF SAMPLES

-4 -3 -2 -1 +1 +2 +3 +4

Popn Value

2.5%2.5%

95%

Standard errors Standard errors

Figure 13.1 Normal curve


Slide 13.14

Sample size (N)

Percentages found from sample (‘results’)

50% 40/60% 30/70% 20/80% 10/90% 5/95% 2/98% 1/99%

Confidence intervals (CIs) ± %

500 ± 4.4 ± 4.3 ± 4.0 ± 3.5 ± 2.6 ± 1.9 ± 1.2 ± 0.9

So CI for 20% finding is 30% ±4.0 = a range of: 26.0% – 34.0%CI is not related to population sizeNB. • CI for p = CI for 100-p – e.g. CI is the same for 40% and 60%• CI for 50% is the largest in absolute terms• This table refers to 95% probability CIs – others can be calculated –

e.g. 99%

Table 13.1 Confidence intervals (CIs)


Slide 13.15

Sample size (N)


50% 40/60% 30/70% 20/80% 10/90% 5/95% 2/98% 1/99%

Confidence intervals (CIs) ± %

500 ± 4.4 ± 4.3 ± 4.0 ± 3.5 ± 2.6 ± 1.9 ± 1.2 ± 0.9

2000 ± 2.2 ± 2.1 ± 2.0 ± 1.7 ± 1.3 ± 1.0 ± 0.6 ± 0.4So to halve the CI it is necessary to increase the sample fourfold

Table 13.1 Confidence intervals (CIs) (Continued)


Slide 13.16

Table 13.1 can be changed to present necessary sample size for a given CI – see Table 13.2


Conf.Interval

50% 40/60% 30/70% 20/80% 10/90% 5/95% 1/99%

Necessary sample sizes

+1 9600 9216 8064 6144 3456 1824 380

+2 2400 2304 2016 1536 864 456 *

+4 600 576 504 384 216 114 *

+8 150 144 126 96 53 * *

Table 13.2 Necessary sample sizes to achieve given confidence intervals


Slide 13.17

Suggested appendix on sample size and CIs

• See Appendix 13.1• Table indicating levels of CIs• Statement indicating that they have been taken

into account


Slide 13.18

Detail of proposed analysis

Sample size

% CI Range, % Comment

Survey with sample of 200

200 Bowling 20 +5.5 14.5 – 25.5 Ranges overlap

Tennis 30 +6.3 23.7 – 36.3

Survey with sample of 500

500 Bowling 20 +3.5 16.5 – 23.5 Ranges do not overlap

Tennis 30 +4.0 26.0 – 34.0


Slide 13.19

Budget

• Key issue: halving the CI requires 4-fold increase in sample size– E.g. N = 250 CI for 50% = ±6.2 Survey Cost = 200 ×$20 = $5000

N = 1000 CI for 50% = ±3.1 Survey Cost = 1000 × $20 = $20,000

• If resources not available for adequate sample size, consider:– Pilot/exploratory study– Qualitative study


Slide 13.20

Confidence intervals applied to population estimates

• Example:• Survey with sample 1000• Findings: 12% visit a national park, on average 2.5 times a year• CI for 12% of a sample of 1000 is ±2%: range; 10 –14%• Population is 500,000• No. of people visiting = 12% of 500,000 = 60,000• At 2.5 times a year = 150,000 visits• What is the CI? ±2% of 150,000 = ±3000? NO• CI % applies to the population• i.e. ±2% of 500,000 = ±10,000 persons• With 2.5 visits per person pa = ±25,000 visits• to have a CI of ±2% of visits would require a sample size of

75,000


Slide 13.21

Sampling for small populations

• CIs are affected by population size if population is below about 50,000

• See Table 13.3


Slide 13.22

Table 13.3 Sample size and population size: small populations

Population size Minimum sample size to achieve CI of ±5% or ±1% on a sample finding of 50%

±5% ±1%

Infinite 384 9602

5 million 384 9584

1 million 384 9511

500,000 384 9422

100,000 383 8761

50,000 381 8056

10,000 370 4899

5000 357 3288

1000 278 906

100 80 99


Slide 13.23

Time # of Interviews % Actual # of users (counts)

%

9–11 am 10 22.2 25 5.7

11.01–1 pm 12 26.7 240 55.2

1.01–3 pm 11 24.4 110 25.3

3.01–5 pm 12 26.7 60 2.7

Total 45 100.0 435 100.0

Sample does not reflect the pattern of use

Example: one survey at a site

Table 13.4 Interview/usage data from a site/visitor survey


Slide 13.24

Table 13.5 Weighting

A B C D

Time No. of Interviews

No. ofUsers

Weighting Factors

Weighted Sample No.

Source: Survey Counts B/A CxA

9–11 am 10 25 2.5 25

11.01–1 pm 12 240 20.0 240

1.01–3 pm 11 110 10.0 110

3.01–5pm 12 60 5.0 60

Total 45 435 — 435


Slide 13.25

Sampling for qualitative research

• Number of subjects generally be small, but:– sampling process is still important– should be fully described in research report

• A range of approaches is possible


Slide 13.26

Method Characteristics

Convenience Conveniently located persons or organisations -

Criterion Selected on key criterion – e.g. age-group.

Homogeneous Deliberately homogeneous group: e.g. university-educated male cyclists aged 20–30.

Opportunistic Taking advantages of opportunities as they arise –e.g. a major sporting event taking place locally.

Maximum variation Deliberately studying contrasting cases. Opposite of ‘homogeneous’.

Purposeful Similar to ‘criterion’ but may involve other considerations, such as ‘maximum variation’, typicality.

Snowball Interviewees source of suggestions for contacts.

Stratified purposeful A range of cases based on set criteria, e.g. representatives of a range of age-groups or nationalities.

Figure 13.2 Sampling for qualitative research

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Tony Veal, Research Methods in Leisure and Tourism, 4 th Edition © Pearson Education Limited 2011 Slide 13.1 Chapter 13: Sampling: Quantitative and Qualitative