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THE APPLICATION OF SAMPLING METHODOLOGY FOR MIGRATION BEHAVIOURAL SURVEY IN THE KLANG VALLEY REGION

Mohd Fadzil Abdul Rashid

1

abrfazil@gmail.com

Ishak Ab. Ghani2

ishakghani@unisza.edu.my

(1)

Universiti Teknologi MARA (Perak), 32610 Seri Iskandar (2)

Universiti Sultan Zainal Abidin (UniSZA), 21300 Kuala Terengganu

Phone No.: 05-3742807 (Off) Mobile Phone No.: 016-3805159

Abstract:

Very few sources of migration data are available for urban planning studies. The most important source is from a census. But the census data are actually underestimated results due to the definition applied for migration has been excluded from the temporary migration streams. Subsequently, there is a need for specific researches of migration especially for urban planning purposes. This paper shares an experience of applying the sampling methodology for migration behavioural survey in the Klang Valley region, Malaysia. Detailed descriptions on the sampling processes are presented in this paper. Keywords: Migration behavioural survey, Klang Valley Region, sampling methodology, urban planning, missing link 1.0 INTRODUCTION The unavailability of appropriate data on migration gives a lot of challenges to urban planners to plan for what potential migrants need at destinations. In practice, migration data which are used for urban planning process are always obtained from a census of population that is carried out every 10 years. However, the data are actually underestimated results (Wilson and Bell, 2004; Wissen et al. 2002; Skeldon, 1998, 2002; Chamratrithirong et al. 1995). This is because in a census of population, either through de jure or de facto census approach, a migrant (lifetime migration) is referred to an individual who changes place of usual residence in five years prior to the census (i.e. 1995-2000, for census in year 2000). This kind of migrant is called the five-year internal migrant (Malaysia, 2004, 1995, etc.) or permanent migrant. It shows that temporary migration is always excluded in the most national data-gathering systems or census.

The implication of that occurrence would underestimate the migration rate in census especially in urban areas. With reference to Skeldon (1998) and Plane and Rogerson (1994), the underestimation is identified by comparing the results from census and migration survey whereby temporary migration is taken into consideration. This underestimation happened in countries such as in Thailand, China and also Malaysia (Skeldon, 1998). Thus, there is a need for other sources of migration data to fulfil the requirements of urban planning process. Among them is through implementation of specific researches of migration namely migration behavioural survey.

This paper aims to share an experience of applying the sampling methodology for migration behavioural survey in the Klang Valley region, Malaysia. The samples of the survey were selected among the households in the study area. 2.0 A SURVEY PROCESS IN GENERAL FRAMEWORK Discussions on process of the survey are based on sampling methods which are available in many statistical or other related field textbooks. Generally, a survey process can be illustrated as in Figure 1.

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Figure 1: A general framework of sampling process

Figure 1 shows 4 major steps involved in a sampling process. It includes aim of survey (or study), designing questionnaire, administering questionnaire and acquiring samples. The next section will explain in detail on relevant matters based on migration behavioural survey in the Klang Valley region. 3.0 SAMPLING METHODOLOGY FOR MIGRATION BEHAVIORAL SURVEY By adopting the framework in Figure 1, the process of migration behavioural survey in the Klang Valley can be illustrated in Figure 2. What follows are descriptions of the major processes as shown in Figure 2.

3.1 The Aim of the Survey The purpose of the survey is to obtain actual information on the phenomenon of migration distribution in the Klang Valley. It includes the trends and distribution of migration within the areas of the Klang Valley, the characteristics of migration distribution in terms of economic, physical and social aspects, and the relative importance of migration decision-selectivity factors.

Aim of Survey

Designing Questionnaire

Questionnaire ready to be used

Acquiring a Sample

A Set of Collected Data

Define question types

Organised (contents, language, graphics, logical

order)

Pilot survey

Select Sampling Technique & Determine Sample Size

Area of study

Select sampling frame

Administering Questionnaire

Draw Sample

Refine sampling frame

Revised

Develop sampling frame

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Figure 2: The process of conducting the migration behavioural survey

3.2 Designing Questionnaire The questionnaire design is one of the most the critical parts of the overall survey process. The failure to produce the appropriate questionnaire will have implications on the reliability of the data and results. Thus, the task of designing a questionnaire should follow a certain process to ensure that the questionnaire is useable and analysable to achieve the objectives. The process of designing questionnaire for the survey is illustrated in Figure 3.

Combined

Aim of the survey: to understand the characteristics of migration

distribution in urban areas

Questionnaire design

Collected data

Multi-stage cluster sampling

The Klang Valley region

Households of the Klang Valley

Multi-stage sampling frame

Face-to-face interview method

Households Sampled

Probability techniques: random & systematic

sampling

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Figure 3: Designing questionnaire process for achieving aim of survey Generally, there are three types of questions commonly used in the questionnaire

design, namely, the open-ended questions; a fixed-alternative question (or also called a closed-ended question or restricted items); and rating scales. In practice, these three types of questions are often used in the same questionnaire. Thus, for the survey, a combination of the three types of questions was used. This combination approach is used to ensure that all the important information of migration behaviours set earlier can be obtained.

The questionnaire content (especially for closed-ended questions, i.e. chosen answers for migration decision-selectivity factors) was determined based on the findings from migration literatures. The scope of the content depends on the types of analysis. Note that, to produce the questionnaire which is analysable and able to achieve the objectives, the “missing link” approach was implemented (see Figure 4). This approach is developed by Ghani (2003) to link questionnaire design and data analysis. This is because sometimes there is no method in the software program which can be applied to the data collected and thus the data are wasted. Also, good questionnaire design principles is not enough to achieve the criterion of analysable and meeting the objectives of the research. The missing link will ensure that all the data or information which has been obtained from the questionnaire survey is analysable towards achieving the objectives. For this study, the missing link is used for understanding the objectives, variables, measure scales, analysis methods, and results for every question in the questionnaire (see Table 1). Consequently, the scope of the questionnaire can be defined. Hence, the application of the missing link will save valuable time and budgets.

Examine on the relative importance of migration

decision-selectivity factors and select the most

important factors

Survey of opinion

Analyses on characteristics of migration distribution in

terms of physical, economic and social aspects including

demographic profiles

Examination of relevant literatures

Migration behaviours, factors

Improve Missing link

Expected findings of survey

Questionnaire design

Questionnaire

Content, graphics

Objectives of survey

Pilot survey

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Figure 4: Designing questionnaire with the missing link (Source: Ghani, 2003)

Table 1: Example of implementing the missing link

(1) Q

No.

(2) Objective Analysis

(3) Variable Involved

(4) Type of

Scale

(5) Table

(6) Analysis Method

C32(i) Descriptive statistics (Examination of the most important of migration decision constraint factors: migration within Klang Valley)

The MGP model variables (Constraint factors/ obstacles)

Nominal Variables Freq --------- --- --------- ---

Freq. table

C32(ii) Descriptive statistics (Examination of the relative importance of migration decision constraint factors: migration within Klang Valley)

The MGP model variables (Constraint factors/ obstacles)

Nominal

-

Mode, median, mean

Note: Follows the procedures that have been suggested in Ghani (2003).

Having determined the content of the questionnaire, it was organised into five sections:

(A) background of respondents; (B) background of respondents that moving to Klang Valley; (C) migration within the Klang Valley; (D) characteristics of migration distribution; and (E) general opinions and suggestions. The respondents are not required to answer all the questions (all the sections), but only the sections which are related to them based on the status of migration.

Besides that, in order to attract respondents to give full cooperation during the interviewing process, the questionnaire was organised into a coherent visually pleasing format, presented in appropriate language and graphics, and presented in logical order. In addition, the private questions such as type of job and income were put in the mid-section of the questionnaire.

Finally, to ensure that the questionnaire is appropriate to be used for the actual survey, a pilot survey was conducted. Twelve sets of questionnaire were distributed in the pilot survey. All mistakes (including the lack of the content of the questionnaire, i.e. list of answers for migration decision-selectivity factors) which were found in the pilot survey were corrected or improved (added) before implementing the actual survey.

3.3 Administering Questionnaire

There are two steps involved in the process of administering a questionnaire that are defining population and target respondents, and selecting an appropriate administering method.

3.3.1 Defining population and target respondents The population can be defined by geographical, demographic, economic, and social

characteristics. These characteristics include country, residence, age, sex, race, marital status, income, household size, etc. For this survey, the population was defined based on the households (residential units) of the Klang Valley. From the total household, the samples were randomly selected on the basis of the multi-stages cluster sampling method. The selected samples consist of migrant and non-migrant households.

In this survey, a migrant is defined ‘as a person who is residing at a place other than where he/she was born or where he/she has changed as the place of residence for at least six

Questionnaire design

Data collection methods

Analysis of data

Missing link: questionnaire is analysable

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months prior to the survey. The boundary of the migration refers to the changes of areas of the Planning Local Authority (PLA). The Klang Valley has eight Planning Local Authority, namely, the Kajang Municipal Council (MPKj), the Ampang Jaya Municipal Council (MPAJ), the Selayang Municipal Council (MPS), the Shah Alam City (MBSA), the Petaling Jaya City (MBPJ), the Subang Jaya Municipal Council (MPSJ), and the Klang Municipal Council (MPK).

Besides that, the questionnaire was structured in such a way that migrants would be able to be reclassified according to the period that they changed their place of residence. Thus, migrants can be both permanent and temporary (for at least 6 months). From that, the migration was classified into three types: (a) migration within the Klang Valley, (b) migration from outside the Klang Valley, and (c) migration from outside and within the Klang Valley. Other types of households (such as non-movers or changes the place of residence for less than six months) are considered as non-migrant households.

The target respondents were the heads of the households. This is because decisions to migrate (or destination choice) are always taken by the heads of households. However, family members also participated in the survey when the head of household was not available. This approach is accepted in migration studies (DeJong and Fawcett, 1981).

3.3.2 Selecting methods for administering questionnaire

Generally, four popular methods for administering questionnaire are face-to-face interviews, telephone interviews, mail questionnaires, and internet questionnaire (McBurney and White, 2004; Ghauri and Gronhaug, 2005). Telephone interviews can gain a high response rate from respondents, but lacking in data accuracy. However, a high response rate from mail methods is rather difficult to obtain. Furthermore, the information acquired from this method is questionable. The internet survey method though may be applicable in other areas of studies, may not provide information that represents the population migration study. Thus, the face-to-face interview method was applied for the survey in this study.

By using face-to-face interview, it is possible for the interviewers to meet the targeted respondents, or, heads of households. Probing for additional information in cases where the initial response is brief or incomplete is possible. The advantage of this approach compared the others is that this approach allows the interviewers to make observations on the objects of interest during the survey. For migration behavioural studies, the observation is useful in understanding the actual scenario of the migration phenomenon.

To perform this approach and maintain high quality of data or information, the interviewers need to appoint people who have sufficient experience in interview surveys. Interviewers who do not have necessary experience will have to be briefed and trained.

3.4 Acquiring Sample

For this survey, the process of acquiring samples involves three steps that are, selecting sampling techniques, developing sampling frames and determining the sample size.

3.4.1 Selecting sampling techniques The sampling techniques can be divided into two groups: probability; and non-probability sampling (Cooper and Schindler, 2006; Bordens and Abbott, 2005; McBurney and White, 2004; Bryman and Bell, 2003; Ray, 2003; Piaw, 2006; Zamalia, 2011). The probability sampling consists of simple random sampling, systematic sampling, stratified random sampling, cluster sampling (including multi-stage cluster sampling), and double sampling. The non-probability sampling on the other hand includes convenience sampling, quota sampling, and snowball sampling.

Most of the previous studies rarely used the non-probability sampling techniques. This is due to the limitation of data interpretations of the entire population especially involving the investigation on areas of study such as migration studies.

Hence, the multi-stage cluster sampling technique was applied for the survey. The simple random sampling is often impractical (Cooper Schindler, 2006). This is because the technique requires a sampling frame which is often not available, and expensive to implement

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(in both time and money). On the other hand, the multi-stage cluster sampling is easier to be applied. In addition, it can be applied in situations where the entire population list is unavailable. Based on the multi-stage cluster sampling technique, data obtained from the survey represents the entire population of the study area.

The ways of applying the multi-stage cluster sampling are shown by Bryman and Bell (2003), McBurney and White (2004), and Bordens and Abbott (2005). For this survey, five stages of cluster (sampling frames) were developed for selecting the sampling units (households). The five stages of sampling frames are:

a) list of districts or areas of Klang Valley; b) list of areas of planning local authority; c) list of community/ towns growth centre; d) list of residential areas/ sections; and e) list of household units.

To select a sample for each stage of sampling frames, two other techniques of probability

sampling were applied. The two techniques are the simple random sampling and systematic sampling. The simple random sampling technique was applied for selecting the sample elements for stage 1 to 4 by using single digits of random number tables. Meanwhile, the systematic sampling technique was applied for selecting the sampling elements (units) in stage 5 or the final stage of sampling frames.

3.4.2 Developing sampling frames By using the multi-stage cluster sampling, the sampling frames or lists for the survey can

be shortened and they are organised into the cluster sampling (systems). As mentioned above, approximately five stages of cluster (sampling frames) were developed for the survey. The five stages of cluster sampling and the selected sampling elements for every stage are shown in Table 2a and Table 2b.

Table 2a: Stages of cluster sampling and the selected elements for stage 1 to 3

Stages Sampling Frame Sampling Elements Selected Sampling

Elements 1 Districts or area of Klang

Valley Region 1. District of Klang (K) 2. District of Petaling (P) 3. District Gombak (G) 4. Federal Territory of Kuala

Lumpur (FTKL) 5. District Hulu Langat (HL)

Hulu Langat (HL) Petaling, and (P)

2 Planning Local Authority (PLA)

HL 1. MPKj 2. MPAJ

MPKj

P 1. MBPJ 2. MPSJ 3. MBSA

MPSJ MBSA

3 *Community/ towns growth centre

MPKj 1. Semenyih 2. Bangi 3. Kajang & Sungai Chua

Bangi Semenyih

MBSA 1. Shah Alam

Shah Alam

MPSJ 1. Seri Kembangan/ Serdang/ Kinrara 2. Subang Jaya

Subang Jaya

Note: MPKj = Kajang Municipal Council, MPAJ = Ampang Jaya Municipal Council, MBSA = Shah Alam City, MBPJ = Petaling Jaya City, MPSJ = Subang Jaya Municipal Council.

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*Selection of towns/ community growth centres is based on census of Population and Housing in year 2000 with population 5,000 and more.

Table 2b: Stages of cluster sampling and the selected elements for stage 4 to 5

Stages 4 Town/

Community Growth Centre

Sections/ Areas

Selected Sampling Units

Bangi/ Bandar Baru Bangi

1. Taman Universiti 2. Section 1 Bandar Baru Bangi 3. Section 2 Bandar Baru Bangi 4. Section 3 Bandar Baru Bangi 5. Section 4 Bandar Baru Bangi 6. Section 5 Bandar Baru Bangi 7. Section 7 Bandar Baru Bangi 8. Section 8 Bandar Baru Bangi 9. Section 9 Bandar Baru Bangi 10. Section 10 Bandar Baru Bangi 11. Section 14 Bandar Baru Bangi 12. Section 15 Bandar Baru Bangi

1. Taman Universiti 2. Section 15 Bandar Baru

Bangi

Semenyih 1. Taman Paling Jaya 2. Taman Semenyih Mewah 3. Tmn Bukit Semenyih 4. Taman Hew Tai 5. Taman Dusun Jaya 6. Taman Semenyih Indah 7. Taman Semenyih Jaya 8. Pekan Semenyih

1. Taman Semenyih Jaya

Shah Alam 1. Section 2 2. Section 3 3. Section 4 4. Section 5 5. Section 6 6. Section 7 7. Section 8 8. Section 9 9. Section 10 10. Section 11 11. Section 12 12. Section 13 13. Section 16 14. Section 17 15. Section 18 16. Section 19 17. Section 20 18. Section 23 19. Section 24 20. Section 25 21. Section 26 22. Section 27 23. Section 28 24. Section 29

25. Section 30 26. Section 31 27. Section 32 28. Section 33 29. Section 34 30. Section 36 31. Section U1 32. Section U2 33. Section U3 34. Section U4 35. Section U5 36. Section U6 37. Section U8 38. Section U9 39. Section U10 40. Section U11 41. Section U12 42. Section U13 43. Section U16 44. Section U17 45. Section U18 46. Section U19 47. Section U20

1. Section U18 2. Section 12 3. Section U10 4. Section 3

Subang Jaya 1. USJ 1 2. USJ 2 3. USJ 3 4. USJ 4 5. USJ 5 6. USJ 6 7. USJ 8

20. USJ 21 21. USJ22 22. USJ 23 23. USJ 24 24. USJ 25 25. USJ 26 26. USJ 27

1. USJ 4 2. USJ 18 3. USJ 13

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8. USJ 9 9. USJ 10 10. USJ 11 11. USJ 12 12. USJ 13 13. USJ 14 14. USJ 15 15. USJ 16 16. USJ 17 17. USJ 18 18. USJ 19 19. USJ 20

27. SS 12 28. SS 13 29. SS 14 30. SS 15 31. SS 16 32. SS 17 33. SS 18 34. SS 19 35. PSJ 7 36. PSJ 9 37. PSJ 11

Stage 5 Selected Sampling Units/ Sections Total Samples Size/ No. of Households 1. Taman Universiti 2. Section 15 Bandar Baru Bangi

Calculation of the total size of the samples and the number of elements (household numbers) for each section is described in Sub Section 3.4.3.

3. Taman Semenyih Jaya 4. USJ 4 5. USJ 18 6. USJ 13 7. Section U18 8. Section 12 9. Section U10 10. Section 3

For stages 1 to 3, the sampling elements were selected based on every two sampling

elements, where 1 is selected. For stage 4, the selection of sampling elements (sections) for every town was carried out as follows:

(a) 10% of the total sections (i.e. 0.1 x 104 = 10 sections). The reasons are: i. to ensure that the actual survey can be carried out practically in relation

to the time and budget constraints; ii. to minimise difficulty (or technical errors) during the implementation of

the actual survey. This is because there are many constraints which are expected to occur in the fields (i.e. unavailable of sampling lists, geographical and weather conditions, manpower, etc.); and

iii. to sample small areas of the survey contributes more ease for the organisation and handling of the survey,

(b) the number of sections for town is proportionate to the total number of sections in each town (i.e. for town Bangi/ Bandar Baru Bangi: 12 / 104 x 10 = 1.1). The overall results of the calculation are shown in Table 3.

Table 3: Results of an actual number of sections for every sampling element

Town Total No. of Section No. of Sections Actual No. of

Sections Sampled

Bangi/ Bandar Baru Bangi 12 1.1 2 Semenyih 8 0.8 1 Shah Alam 47 4.5 4 Subang Jaya 37 3.6 3 Total 104 10.0 10

From the results shown in Table 3, it is found that the town of Bangi/ Bandar Baru Bangi

is over sampled. This is because Bangi/ Bandar Baru Bangi is a new potential area to focus on migration in the Klang Valley. Shah Alam and Subang Jaya are somewhat under sampled. The actual number of sections for every town is shown in the last column in Table 3.

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As mentioned earlier, the application of the simple random sampling technique for selecting the sample elements from stage 1 to 4 is based on the single digits of random number table. The steps in using the random table number are as follows:

a) assign a number to each sample elements (or units/ sections) in the list for each stage;

b) determine the number of columns (digits) to read. For this application, the single digit random number is used;

c) select the number from the table. Any number over the number of sample elements for each stage or any duplicate numbers is discarded; and

d) select numbers until the desired sample elements size for each stage is obtained.

In stage 5, the selection of the sampling units (household units) for every cluster or section was carried out by using the systematic sampling technique. This technique is widely used in social science surveys. This is because this technique can be applied without complete sampling frame or list. The sample can be selected or determined during the survey process. According to Cooper and Schindler (2006), this is the major advantage of the systematic sampling technique. Thus, it is easier to instruct enumerators (interviewers) to choose the dwelling unit listed based on every kth element or skip interval. The kth element is determined by dividing the sample size into the population size (dwelling units) to obtain skip interval pattern applied to the sampling frame (Cooper and Schindler, 2006; McBurney and White, 2004). The steps to draw a systematic sample are as follows:

a) identify the number of the elements in the sampling units, b) identify the skip interval (k), c) select the first element randomly (between 1 to 9) in the sampling units, d) draw a sample by choosing every kth entry until the desired sample size of the

sampling unit is obtained.

3.4.3 Determining samples size The final step in the sampling methodology is to draw a sample. The size of the sample can

be determined based on published tables or simple calculation. The sample size for stage 5 was determined by using Formula (1) as follows:

n = 2

1 eN

N

(1)

where n is the sample size, N is the population size, and e is the level of precision or sampling error.

Formula (1) is widely used for calculating the sample size especially in social science studies. The published tables also applied this formula such as Yamane (1967). Israel (2003) had shown the calculation for using this formula.

The sample size by using the Formula (1) is shown in Table 4. It indicates that the total sample size should cover 400 households. The 400 samples were based on 5% precision level. To find the minimum number of the sample size, it is possible to use a 7% level of precision. Hence, the minimum size based on the 7% level of precision is 204 samples. This leads to the minimum and maximum number of the sample size for the survey to be in the range of 204 to 400 samples. However, this study targets to achieve the maximum number that is 400 samples.

Table 4: The distribution of samples size

Selected Sampling Units (From Table 2b) (i)

Estimates Dwelling Units (Si)

(ni)

Sample Size (Households)

(si) Taman Universiti Section 15 Bandar Baru Bangi

488 216

220 140

46 29

Taman Semenyih Jaya 273 162 34

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USJ 4 USJ 18 USJ 13

815 340 993

268 184 285

56 38 59

Section U18 Section 12 Section U10 Section 3

89 91

3,057 258

73 74

354 157

15 16 74 33

Sub-total 6,620 1917 - Total Household/ Sample size *1,400,000 - 400

Note: *Total household of the Klang Valley is estimated about 1.4 millions (year 2008).

Based on the total 400 households, the distribution of households for every sampling

unit/ section is defined as:

10

1

400

ii

i

i

n

ns (2)

where si is the sample size for section i, where i = 1,2,…,10 (the number of sections), 400 is the total sample size (households), and ni is the number of dwelling units for section i, which is adjusted for sampling error. However, ni = Si/[1+(Si)(e)2], where Si is the population (dwelling units) size for section i, and e2 is the sampling error (5%) or (0.05)2. The values for the ni and Si are shown in Table 4. By applying Formula (2), the 400 samples were distributed into 10 sections (Table 4). Finally, the systematic sampling technique was used to implement the survey on the 400 households. Detailed descriptions on the implementation of the systematic sampling technique can be found in Rashid and Ghani (2008).

As commonly happens in a household survey, this survey faced some problems. Based on the difficulty, only about 364 households of the total 400 households were collected in the survey. The distribution of samples by sections is shown in Table 5.

Table 5: The distribution of samples by selected areas of study

Areas of Study Number % Out of Target

Number

Taman Universiti Bangi 46 100.0

Section 15 Bandar Baru Bangi 29 100.0

Taman Semenyih Jaya Semenyih 34 100.0

USJ 4 Subang Jaya 56 100.0

USJ 18 Subang Jaya 29 76.3

USJ 13 Subang Jaya 49 83.1

Section U18 Shah Alam 15 100.0

Section 12 Shah Alam 3 19.0

Section 3 Shah Alam 29 88.0

Section U10 Shah Alam 74 100.0

Total collected samples N = 364 90.4

4.0 CONCLUSIONS This paper has described the overall process that is involved in the application of sampling methodology for migration behavioural survey in the Klang Valley. There are four main steps that have been involved in the survey, namely to determine the aim of survey, designing questionnaire, administering questionnaire and acquiring samples. They are interrelated with one another. The aim of this survey, for instance, is to determine the scope of the content of questionnaire. This is because the achievement of study is highly depends to the appropriately

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of the questionnaire. The selection of techniques for conducting and acquiring the samples is also influenced by the aim of study. So the aim of study must be clear, measurable and it can be achievable in the given time. The missing link that has been mentioned in this paper provides researchers a more suitable approach on how to design the questionnaire which is analysable and linked to the objectives. At the same time the missing link also will help the researchers to clarify the aim of study based on the availability of data to be collected and analysed. Then it would be utilised to contribute to reliable data and results.

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