Child Labour Survey Data Processing and Storage of Electronic Files

Transcription

1 Statistical Information and Monitoring Programme on Child labour (SIMPOC) International Programme on the Elimination of Child Labour (IPEC) Child Labour Survey Data Processing and Storage of Electronic Files A Practical Guide Revised December 2003 International Labour Office Geneva

2 Copyright International Labour Organization 2004 Publications of the International Labour Office enjoy copyright under Protocol 2 of the Universal Copyright Convention. Nevertheless, short excerpts from them may be reproduced without authorization, on condition that the source is indicated. For rights of reproduction or translation, application should be made to the ILO Publications Bureau (Rights and Permissions), International Labour Office, CH-1211 Geneva 22, Switzerland. The International Labour Office welcomes such applications. Libraries, institutions and other users registered in the United Kingdom with the Copyright Licensing Agency, 90 Tottenham Court Road, London WIT 4LP [Fax: (+44) (0) ; cla@cla.co.uk], in the United States with the Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA [Fax: (+ 1) (978) ; info@copyright.com] or in other countries with associated Reproduction Rights Organizations, may make photocopies in accordance with the licences issued to them for this purpose. ISBN First published 2004 The designations employed in ILO publications, which are in conformity with United Nations practice, and the presentation of material therein do not imply the expression of any opinion whatsoever on the part of the International Labour Office concerning the legal status of any country, area or territory or of its authorities, or concerning the delimitation of its frontiers. The responsibility for opinions expressed in signed articles, studies and other contributions rests solely with their authors, and publication does not constitute an endorsement by the International Labour Office of the opinions expressed in them. Reference to names of Firms and commercial products and processes does not imply their endorsement by the International Labour Office, and any failure to mention a particular firm, commercial product or process is not a sign of disapproval. ILO publications can be obtained through major booksellers or ILO local offices in many countries, or direct from ILO Publications, International Labour Office, CH-1211 Geneva 22, Switzerland. Catalogues or lists of new publications are available free of charge from the above address. Photocomposed in Switzerland Printed in Switzerland BRI VAU

3 Foreword and acknowledgements The production of survey results in a presentable format is often delayed, one of the main reasons being that data processing issues are addressed neither properly nor early enough. Emphasizing the importance of careful and informed data processing, this guide provides detailed guidelines for survey planners, data processors, and computer system administrators with respect to data processing planning, actual data processing activities, and the storage of generated files. The guide also outlines the requirements and procedures for transferring electronic files to the ILO at the completion of child labour surveys, a process that is contributing to a growing global child labour data repository. The main aim is to facilitate the generation of high-quality micro-data derived from child labour surveys. This guide has been prepared by Muhammad Q. Hasan of SIMPOC/IPEC, ILO. Many people involved with child labour surveys helped in the exercise. We would like to express sincere thanks to all concerned. We particularly wish to thank Mr. Sylvester Young, Director of the ILO Bureau of Statistics and Mr Farhad Mehran of the ILO s Department of Policy Integration for their valuable comments and suggestions. This guide is planned to be revised and reproduced on a regular basis. For this suggestions and comments are always welcome. Users should direct any feedback to simpoc@ilo.org iii

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5 Contents 1. Introduction 1.1 Background Field data collection: A brief overview Importance of data processing Planning 2.1 Introduction Data processing policy planning Defining the relevant aspects of a dataset Selection of hardware and software Identification of personnel Scheduling the data processing Data preservation strategy and access procedure Data processing 3.1 Introduction Data entry and preliminary validations Appending/merging/splitting files Data validation Final decisions on errors Completion of data processing and generation of data file(s) Preparation of public use datasets Final documentation Final tabulation Conversion of data files to other formats Storage of all files Data preservation 4.1 Introduction Organization of files Transfer of files to a preservation machine Backups Transfer of files to the ILO Bibliography and further ressources v

6 Glossary Annexes Annex I Comparison of statistical packages Annex II English country names and code elements Annex III Zambia end of decade and child labour questionnaire (education module) Annex IV A sample codebook for ASCII data created in SAS Annex V Structure of dataset vi

7 1. Introduction 1.1 Background ILO/IPEC s Statistical Information and Monitoring Programme on Child Labour (SIMPOC) supports child labour surveys conducted in a large number of countries. One of the most important aspects of this programme is the collection, archiving, and dissemination of credible, well-documented, and easily accessible micro-data. This requires the extensive planning, organization, and execution of planned activities, especially at the country level, where, it is expected, collected data will be archived for an indefinite period. At the ILO, meanwhile, this information will provide the basis of a global child labour data repository for use by a variety of people in a variety of countries in a variety of computing environments. Thus, the data must be clean, with no inconsistencies, and well documented, readily accessible for use at any time in research and policy-making activities. The dataset received by the ILO also needs to be complete incorporating codebooks, questionnaires and so on and ready for straightforward use by any analyst in any computing environment. Child labour surveys include three phases. First of all, data are collected through interviews with children and other family members. Data collection is followed by data processing, where the collected information is checked for errors, and micro-data and relevant documentation files are created. Finally, data analysis is performed in the light of any additional requirements or policy. Data processing is a difficult and complex process, but in many cases this is the stage that receives least attention. Data processing activities such as planning for equipment, software, and training of personnel can be conducted concurrently with such activities as survey design and field-data collection. Since all child labour surveys are carried out under strict time constraints, it is recommended that all planning, training, and testing procedures are completed before the field data collection is undertaken. The data processing phase includes several distinct stages, each of these comprising multiple steps where errors can and do occur. Child labour surveys are smaller operations than censuses, but, since most are first-time surveys and collect a greater amount of information than many other general household surveys, they tend to be more complex. While overall data processing activities are in many respects similar to other general householdtype surveys, child labour surveys, given their larger sample sizes and questionnaires, sometimes make greater demands on time and other resources. Presentable survey results are commonly delayed because data processing issues are addressed neither appropriately nor early enough. This guide presents a brief overview of the data collection phase before going on, first, to highlight the importance of data processing and, second, to provide detailed guidelines for its conduct, with particular emphasis on issues pertinent to child labour surveys. Chapter 2 addresses planning issues involved in data processing. Chapter 3 looks at the conduct of data processing and, immediately upon completion of a child labour survey, the generation of files, including well-documented public use datasets. One main purpose of this guide is to help data processors at the country level to produce clean, reliable datasets together with all the necessary documentation for use by secondary analysts, at the conclusion of surveys, in producing reliable aggregate data. Chapter 4 provides information on how to preserve datasets, allowing continued ease of access over an indefinite period. Survey design issues, data analysis, and data dissemination lie beyond the scope of this guide. 1

8 The information presented in the following chapters should be viewed only as guidelines the procedures outlined here may of course be adapted in the light of available national resources and experience. This guide as a whole is intended for planners and technical experts supervising data processing activities. Chapter 3, however, is designed specifically for those who perform the actual data processing, while Chapter 4 is for computer system administrators responsible for storage of child labour survey data. The guide also provides an overview of data processing activities that can be carried out at the survey design stage. 1.2 Field data collection: A brief overview In general, data collection can involve a variety of methods, from face-to-face or telephone interviews to aerial photography. Child labour surveys, however, involve only faceto-face interviews, and only two such methods are feasible. PAPI. With paper-and-pencil interviews, enumerators apply questionnaires on paper, recording the data with pencils. Data entry operators then key data into computers or convert them into machine-readable form through some scanning technique coupled with character recognition technology. No matter which method of data entry is selected, the information needs to be rechecked. Various means are used to ensure that data are entered properly. Much of this will be explained in the following chapters. CAPI. With computer-aided personal interviews, enumerators are supplied with handheld electronic devices (e.g. palmtop or laptop computers), permitting the direct digital recording of data. This method offers advantages, compared to PAPI, since major errors occur only while keying in the data, which can be rechecked immediately after data collection. Data are then transferred to computers, with almost no time needed for additional data entry, and data cleaning can begin immediately. This guide primarily addresses PAPI, which is the data collection method applied in most child labour surveys. 1.3 Importance of data processing Child labour typically remains a hidden issue, in many respects; and surveys seek reliable quantitative data about the various related concerns. National surveys require important sums of money and enormous organizational efforts involving ministries, national statistical offices, and other agencies. The resultant data are then provided to policy-makers, researchers, global estimators, and campaigners responsible for publicizing the adverse effects of child labour. All of these mentioned above need easily accessible, reliable data on the various aspects of child labour. Both non-sampling and sampling errors appear in survey datasets. Sampling errors are handled during the sample design phase, and are not addressed in this guide. Non-sampling errors may originate with respondents, interviewers, data-entry clerks, or processing programmers. One main objective of data processing is to find these errors and fix them in the shortest possible time. Where irreparable errors are found, they should be flagged with explanations. Unidentified and unflagged errors can corrupt interpretations of data and, ultimately, may result in the adoption of inappropriate policies. Competent and thorough data processing activities including error correction, logical checks, and compilation of information as the basis of documentation are vital to reliable 2

9 survey information. Otherwise, output from a successful survey (the collected field data) may be limited only to a few tables. Secondary analysts will find it difficult, if not impossible, to use the data, while national and international policy-makers may be misguided by the survey results. One key to successful data processing is careful planning. The various related activities need to be detailed as early as possible, and should include fallback plans. Data processing is immensely important to the survey output, and cleaning and verifying the data is essential. 3

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11 2. Planning 2.1 Introduction The preparation of high-quality datasets requires proper planning, and involves two essential elements: Statistical method. One should employ good data collection tools and a well-developed survey methodology. Processing and subsequent storage of datasets. A second essential element involves the informed use of established data processing tools, processing methodology, and up-to-date computer hardware and software where applicable. In most cases, child labour surveys are conducted either as stand-alone operations or are attached as a module to some form of national household survey. In stand-alone surveys, children and their parents are usually interviewed. On the basis of initial investigations, this manual assumes that all child labour surveys employ paper-and-pencil interviews (PAPI) for data collection. Survey planning and data cleaning are discussed in light of this assumption. Upon completion of the interviews, the collected data are entered on a computer. Data entry in the field may occur under the supervision of field office supervisors or at survey headquarters, which is normally the national statistics office. If data are entered in the field, there will be a minimum of one file at each field location. Since the same survey questionnaire is used, all files generated in field locations will be similar with respect to the number of variables. No matter how the data are entered, different files are either appended before data cleaning or cleaned and then appended. These activities are normally conducted at survey headquarters. Where a child labour survey is conducted as a module attached to some other household-based survey (e.g. a household member health and education module), child labour data may be collected together with other modules (as with stand-alone surveys) or as a complete module without the household information (which is collected as part of another module). The data may also be collected at different times (e.g. if attached to a quarterly labour-force survey, the total sample will be covered over a period of one year). In such cases, household information needs to be extracted from the other data file(s) and then combined with the child labour data. Such cases entail both appending and merging of data. (Merging and appending are described in greater detail in later sections.) Completion of a data file is followed by data cleaning (partial cleaning may also occur in each modular file). It should be noted that child labour is difficult to define unless all relevant information about children is thoroughly investigated understanding the causes and consequences of child labour requires analysis of information about the household and other family members. Another scenario 1 presents itself when the survey is conducted in phases, with a series of questionnaires referring to different entities or differing in their respective coverage. In this situation, data may have to be presented in separate files, with no merging or appending. All of the situations described above warn us that careful planning is needed before the collected information is processed and made available for analysis. All planning issues can 1 One example for this would be the SIMPOC assisted country report Survey of activities of young people in South Africa 1999, report/rep1999.pdf. 5

12 be addressed while the survey design is in progress. If financial and time constraints are not an issue, all data processing activities should be tested during the pilot survey. (If the CAPI data collection method is used, this is essential.) It should be noted that careful advance planning considerably reduces actual processing time. The following sections discuss planning issues that need consideration before the actual data processing. 2.2 Data processing policy planning Two areas of planning are important to data processing. On the one hand, we must decide how the actual data processing is to be accomplished, and this is treated in detail in Chapter 3. But first we must ask what resources and definitions are required for effective and efficient data processing. We may term this initial step policy planning. Policy planning comprises the following essential features: defining the relevant aspects of a dataset; selecting hardware and software; identifying personnel; scheduling the time needed for data processing; formulating a data preservation strategy; and designing an access procedure. 2.3 Defining the relevant aspects of a dataset If analysts are to use a dataset effectively, the micro-data must first be properly processed. This involves a number of stages. Preliminary planning is essential, and includes the identification and definition of such aspects of the dataset as the following. Record identification variable To identify a case or record, an identifying variable is usually created and encoded with a unique value. The encoding method and the elements that constitute this variable have to be determined, and the variable often referred to as the unique record identifier should be named in accordance with the procedure described later in this chapter. This identification variable will provide the only linkage between the original dataset containing all the variables and a public use dataset (where many identification variables may have been deleted for reasons of confidentiality) or when data are in different files, but a cross comparison of information is required. For example, a combination of state or provincial code, enumeration area code, and house number appended one after another may be enough to identify a house uniquely. A line number (position of a person in a house) can be used to identify a person in the house uniquely. Other approaches may achieve the same goal, but care should always be taken when appending these numbers, and each household, as well as each person living in a household, should always have a unique identifier. 6

13 File structure In child labour surveys, the unit of analysis is the child or the person, whereas the medium is the household, because information about the child or person is collected by first identifying a house. Thus, it is worth deciding what the final data files should look like. The structure of data files may vary considerably in format and organization when, upon completion of data entry, they become available to secondary analysts. Is a large data file with one long data record preferable (describing both a child and the house in which he/she is living, for example), or does one want several small data files with short data records (where child and house information, for instance, reside in different files with a linking variable)? This decision will depend on factors such as how the survey is conducted and what statistical software is being used for data entry and processing. The following considerations may serve as guidelines. A data file may contain one long record or several smaller records. A large number of records slows processing speed. Some statistical packages (e.g. Stata) limit records to a maximum number of variables. On the other hand, one advantage of long records in a single file is that secondary analysts do not have to merge files at a later date. Annex I describes limitations associated with statistical packages such as SPSS, SAS, and Stata. Data may be organized in a file such that household records are followed by person records (with different record types in an ASCII hierarchical file). Alternatively, there may be two separate files: one for a house and one for persons living in that house, with welldefined linking variables common to both files in a package-specific format. There may also be a single merged file with long records. The values for many variables will be repeated for members of the same house in such files, thus occupying more storage space. Each system has its pros and cons, and one planning decision must address the questions of how many data files are to be included in the dataset and what the structure of each should be. Because of the way specific software handles data files, processing large data files within a Windows environment may be a problem. A child labour survey data file may become large when associated with a labour force survey, so the data file may need to be split before analysis. The file structure should be chosen according to available computing resources and the experience of the data processors. Because of its simplicity, however, a single flat data file is recommended where possible for child labour surveys. Naming files As soon as a file is created, it must be named, and it is worth deciding beforehand how all files are to be named. This means, at a minimum, adopting a naming convention. It is always recommended, for one thing, that names reflect the file contents. The version number of the file can also be included. (In Chapter 3 we see how different versions may be generated.) For child labour surveys, specifically, it is recommended that the following information is included in file names: file content (data, documentation, questionnaire, etc.); to whom the file relates (child, parent, both); version number; relevant country; and whether the file is for general or restricted use. 7

14 Such a standard naming convention greatly assists users in choosing the correct file from the dataset. In general, it facilitates processing the contents, often at a much later date, of computer-based storage systems that may contain thousands of files. Other information, such as survey year and survey round, may also be included in the file name. However, there are generally restrictions on the number of characters used in naming a file, with most computer systems allowing 8.3 structures i.e., eight characters for the actual file name and three characters for the file extension (e.g. MY_FILE.DOC). The extension is usually allocated by the package that created the file. (MSWord, for example, will use DOC as the extension.) In other words, only eight characters can be manipulated to express as much information as possible about the nature of a file. In view of these limitations, the following naming convention is recommended. All filenames should start with a country code (Annex II lists the two-character codes) followed by the abbreviations C for child or P for parents or F for family (both parents and children) and H for house (dwelling). The version number follows and, since more than nine versions can easily evolve over time, two characters should be used. G indicates the file is available for general use, and R marks it as restricted. Finally, the eighth character D, Q, or C representing data, questionnaire, or codebook respectively indicates the file contents. If any field in the file name is not applicable, that should be replaced with an underscore (_), thereby simplifying manipulations during computer processing. In summary, when naming files according to an 8.3 structure, use the following convention. The first eight characters: first and second characters country code third and fourth characters child/parent (person), house (dwellings) or both C_ for child only F_ for child and parent (family) H_ for house P_ for parent only FH a single file containing information on child, parent and house (dwellings) combined Note: an underscore (_) is used to fill the blank space of the fourth character fifth and sixth characters version number 01 first or original version 02 second version, and therefore not the original version and so on seventh character file use G for general (public) use R for restricted (internal) use (in case of data only) eighth character file contents C for codebook (normally associated with an ASCII data file) D for data I for summary of classification of industries 8

15 L internal consistency check rules Q for questionnaire S for summary of classification of occupations V for variable list The last three characters, following the decimal point, denote the type of file (proprietary or otherwise). The following examples should clarify the convention: BDC_01RD.DOC/SAV/POR A file containing data about children in Bangladesh, and which is the original version, might be named BDC_01RD, where BD stands for Bangladesh; C stands for child; _ indicates there is no information regarding the house or dwelling; 01 marks this file as the first version; R shows that the file is restricted; and D stands for data. The corresponding public use data file derived from the above would carry the name BDC_01GD. The associated questionnaires would be called BDC_01GQ. (Since the questionnaires are for general public use, they would always include the G code.) The extension would say whether it is a package-specific data file or documentation. For example, a SPSS data file takes a SAV or POR extension, while documentation in MSWord takes a DOC extension. UAFH04RD.[xxx] Similarly, a file containing data about parents, children, and their house in the Ukraine, and which is the fourth version, can be named UAFH04RD. The public use version would then be named UAFH04GD. Associated questionnaires are named UAFH04GQ, while a variable description file is named UAFH04GV. A summary classification of occupations file would be named UAFH04GS. All the file names would include appropriate three-character extensions. PAFH02RD.txt An ASCII data file that contains data about parents, children, and households in Panama, and is the second version, can be named PAFH02RD.txt, and the public use version would be PAFH02GD.txt. The associated codebook file should be named PAFH02GC with a TXT or DOC extension, depending on file type. Creation and naming of variables Once a survey is completed, a set of variables is created from the questionnaire (primary variables). At a later stage, manipulating the primary variables may produce derived variables. Unless conventions are followed, naming these variables can prove awkward. Here are a few rules of thumb: Variable names should convey the meaning of the data content they represent. Any potential analyst should be confident that the same variable names apply to the same data. For example, if two questions are used to determine the work status of a respondent e.g. enquiring as to both current work and usual work variables representing these questions should never be named work1 and work2, since this leaves it unclear which variable refers to which question. Ideally, questionnaires should be prepared such that each question comes with a predesignated variable name. For example: How old are you? would be annotated 9

16 with the variable name AGE. This type of questionnaire is often referred to as an annotated questionnaire. As with files, naming variables often depends on statistical packages that restrict the number of code characters to eight or fewer (SPSS for example). 2 The prevailing computing environment in any particular country will also influence naming conventions. Each answer in a multiple-choice question should also be assigned a variable name. For example : If question number 9 has 2 multiple-choice answers then variables may be named as Q9A and Q9B. Several different methods may be applied for naming variables 3. One-up numbers. In this approach, variables are numbered sequentially. Thus if there are 100 variables in a data file, they can be numbered from 1 to 100. However, many statistical software packages do not allow a digit to be the first character in a variable name (e.g. in SPSS), as such a letter can be added as a first character (e.g. in SPSS, variable names will be automatically assigned either v1 to v100 or var0001 to var00100.) Variable names can be changed manually afterwards. However, the problem with this method is that it is often impossible to comprehend the meaning of the variable or to match some variable names with the respective questions without additional labels. Errors can easily happen if variables are named in this way. Question numbers. A possible alternative to the one-up number method is to name variables with the respective question number; for example, Q1 is the variable that corresponds to question 1. Since multiple answer questions would require more than one variable to be created for a single question, a letter can be appended after the question number, Q4a, Q4b etc. Since all child labour questionnaires consist of multiple sections, the first letter can be chosen to represent the section (A1, A2 B4a, B4b etc, where A, B are two different sections) Again, additional labels can also be used to explain the actual meaning of the variables Mnemonic names. In this method, variables are named with words representing the concept of the variable. However, the same word may offer different meanings to different users. Also, the maximum of eight permissible characters in the variable name may impose severe restrictions to conveying the actual meaning. It is also hard to assign manually the same word to different variables conveying the same type of meaning. Prefix, root, suffix systems. A possible alternative to the mnemonic method of constructing variable names is to use predefined abbreviated words and join them as prefix, root and suffix. For example, all variables related to children may use CH as a prefix; WW and WY, to denote last week s work and last year s work respectively, as a root; and GRP, to group cases, as a suffix. Derived variables. As mentioned earlier, derived variables are created from primary variables or by combining multiple primary variables. For example, age may be a primary variable, but analysts might need information about children in the 5- to 9-year age group. Information about individual children s ages can then be grouped to form the derived variable age group. It is always recommended that primary and derived 2 See Annex I for maximum number of characters allowed in naming a variable in some statistical packages. 3 This follows the approaches outlined in: Inter-university Consortium for Political and Social Research (ICPSR), Guide to Social Science Data Preparation and Archiving. Retrieved from 10

17 variables are distinguishable. For a variety of reasons, it is also advised that public use datasets should not contain large numbers of derived variables: they are costly in terms of data-processing time; if they are to be properly used, they need adequate explanations; and the datasets may become too large and unwieldy. Moreover, data analysts may not have occasion to use these derived variables at a later date, and prefer to tailor derived variables to their own requirements. Remember that the weight factor included in a dataset is not a variable from the questionnaire, and it should be treated separately. It should be named WEIGHT, using the naming convention applied to a primary variable. Individual countries are of course free to choose the naming convention appropriate to their variables. With the aim of establishing international consistency with regard to child labour data, however, the following rules are recommended: use the question-number method in naming variables, with the character representing the section appearing as the first character in the variable name; use the prefix method in naming derived variables; use capital letters for primary variables, when possible; use lower-case characters for derived variables; and the weight factor should be named according to the rules for primary variables, but at the same time be distinguished from a primary variable. Variable labels It is more difficult to understand a dataset if attributes associated with the variables for example the literal question asked are not properly described inside that dataset. People who want to perform secondary analyses of child labour surveys prefer that all information be contained in the dataset. One sign-posting method is to provide an adequate label for each variable. Since nowadays almost all data processing software (e.g. SPSS) provides the option to add labels, this option should be used to describe each variable. If no suitable labels can be found, the literal question together with the appropriate question number should be used as a label. If the variable is a derived variable, a label can be added to express which variable or variables are used to create this new variable and if possible indicate the reason for creating such a variable. Coding A statistical software package is used to analyse the information collected through collection of field data. Thus, the information needs to be transformed into data that the software can handle. To this end, each answer is coded, and the process that determines which symbol represents what item is known as coding. Coding should be undertaken during the survey design process, and it is important that the data processors themselves are involved. Child labour surveys should be pre-coded before data entry. All possible values including those such as not available, not applicable, refused to answer ought to be included in the questionnaire, and interviewers should receive proper training. These measures will greatly reduce the time that data entry or data processing personnel need to spend on coding. Following are a few guidelines drawing on the ICPSR Guide to Social Science Data Preparation and Archiving 4 and the Audience Dialogue Survey analysis 5. 4 ibid. 5 Audience Dialogue: Survey analysis. Retrieved from kya5.html 11

18 Should the need for additional codes arise (for example, assigning a specific code for open-ended questions), this is to be carried out with proper consideration to the coding scheme defined during the questionnaire design. It is particularly important to ensure that there are no overlaps between code categories and that each code fits into only one category. For open-ended questions, major categories/classifications should be identified by examining the number of responses and should be used for additional coding. The meaning of each code should be clearly documented. During the additional coding procedure it is also good practice to preserve as much information as possible in the data as they are collected (i.e. no collapsing or bracketing etc.). With occupational coding, it is important to follow a standard format defined by an accepted standards institution e.g. the International Standard Classification of Occupations, ISCO-88 and to use as many digits and, therefore, include as many details as possible. Specify all possible missing values (such as no response or not applicable ). Assign the same value (99, for example) to each type (e.g. not applicable ) in the same dataset. One of the following factors is usually responsible for missing values in child labour survey data, and a different code should be assigned to each case. Refused to answer. A child or parent did not answer the question. Don t know. A child or parent was unable to answer the question. The respondent might not have had any concept of time or arithmetic, for example, and replied Don t know to the question: What was your total income last year. (Respondents should be discouraged from answering, Don t know.) Not applicable. For some valid reason, the question was not asked. Following the response Not working, for example, any questions related to income were not asked. It has been observed in many child labour surveys that missing values were left blank or coded with a zero that was not pre-defined. It is of paramount importance, therefore, that all cases are assigned different codes during the coding process; and these should then appear pre-coded in the questionnaire. If for any reason missing values are assigned codes, the documentation should include clear descriptions. It is often quite difficult to code such items as occupations and industries. Where codes are developed, some classifications (occupation, for example) may be missed, making the jobs of enumerators and data processors even more difficult. Consequently, countries are encouraged to consult the following resources for help: International Standard Classification of Occupations (ISCO) 6 International Classification of Status in Employment (ICSE) 7 International Standard Industrial Classification of all Economic Activities (ISIC) 8 Classifications of Occupational Injuries 9 6 retrieved from 7 retrieved from 8 retrieved from retrieved from 12

19 This list, which is not exhaustive, can be accessed through the Bureau of Statistics web page. 10 Child labour classifications, the relevant categories varying from country to country, are not yet in a finalized form, and additional coding schemes may need to be developed. Consistency and logic check rules It is important to develop as many logic check rules as possible by going through the questionnaire. This requires a detailed understanding of the questionnaire and its flow, and will greatly help computer programmers at later stages. First, consistency check rules have to be generated by studying the routing of each question (e.g., if the answer to question 20 is yes, enter skip pattern as answers to questions 21 and 22). Sample responses from questionnaires that suggest other consistency checking rules include these: A child aged younger than six years is reported as having completed secondary school. A child is reported as not working but as nevertheless bringing cash into the household. A child did not work, but reported a work-related injury. Another type of logic check rule needs to be developed where data contains a legal value but nevertheless does not look right. For example, a parent is reported as having 11 children. This may be true, but may not look right, and could well represent a typographical error. The correct value may more likely be 1 child. The corresponding rule could read: Flag cases where parents reported having more than 10 children. These flagged cases then need to be checked manually. Imputations Once consistency checks are performed, many missing values can be replaced following imputation rules. Imputations estimate what would otherwise be missing values, where survey respondents failed to provide responses to given items. One rule might indicate, for example, that a person s income can be imputed by generating a formula involving age, type of work, wage rate, and number of days worked in a particular geographical area. As many of these formulae as possible should be developed by going through the questionnaire. It must be decided how imputed variables are to be incorporated in the dataset, and, where needed, relevant computer programs may be developed and tested. For simplicity, a completely new variable can be created, one which includes imputed values for missing codes, or where missing codes are replaced with imputed values together with a flagged variable with a value of 1 for imputed, and a value of 0 if not. Weights Since all child labour surveys are sample surveys, weights need to be calculated in order to produce national estimates. In choosing a sampling procedure, we should ask whether standard errors based on simple random sampling are appropriate, or whether more complex methods are required. If weights are required, they should be described. A clear indication of the response rate should be provided in the documentation, indicating what proportion of those sampled actually participated in the survey. The retention rate, if applicable, should also be noted. Weights are usually developed by specialists, and it is essential that a weighting formula with descriptions of all its elements is obtained well before data processing begins. 10 Details may be obtained from 13

20 Documentation Documentation should be as much a part of overall planning as is analysis. It has to be decided who is responsible for keeping a log of what is happening during data processing, including such considerations as problems encountered, major decisions taken, and any imputation method adopted. A more detailed account of this process is presented in Final documentation, (Section 3.8). 2.4 Selection of hardware and software Marshalling resources for a child labour survey strongly depends on what hardware, software, and national statistics office personnel are available. Given those constraints, the following aspects must be considered when selecting hardware and software for data processing: computers and printers data entry and data cleaning statistical processing and tabulations documentations and other tabulations software utility tools automation tools (to perform repeated tasks) Computers and printers tools for transferring files among different computers. virus-checking software hardware accessories cables, disks, CD, UPS, etc. Since data will be entered in batches and probably in parallel, one PC is needed for each data entry operator. Different data entry operators, however, can often share the same computer at different times. Printers capable of printing landscape format are also necessary. If line printers/ dot matrix printers are used, they should have a capacity of 120 characters per line. A Pentium computer with a 1GB hard disk is more than enough for data processing and temporary storage of child labour survey data. A permanent computer is also needed where the final dataset will be archived. It is highly recommended that the computer used for permanent storage of data is not the same one used for day-to-day work, even where this computer may be a central one, shared by different sections in the national statistics offices to store their data on a permanent basis. Data entry and data cleaning A great number of staff-hours are sometimes devoted to developing custom software for checking data entry errors. A better solution can be to use automatic data entry software, most of which has some form of built-in checking facility. Over the years, a variety of organizations have developed data entry software, and many national statistics offices use one or all of the following programs for data entry and initial data validations (this list is not exhaustive): 14

21 Blaise. 11 A flexible, relatively powerful system developed by Statistics Netherlands for computer-assisted interviewing, data entry, and data editing, Blaise is a software system for survey processing on microcomputers. Blaise also simplifies subsequent processing of the collected data. This software is being used primarily by European Union countries. IMPS. 12 Developed by the US Census Bureau, the original DOS-based Integrated Microcomputer Processing System has been superseded by a Windows-based version. Many developing countries are using this software for data entry. ISSA. 13 Integrated Systems for Survey Analysis is produced jointly by SerPro Ltd of Chile and Macro International of the USA. A number of developing countries are using this software for data entry. Evidence suggests that ISSA does not have a wide user base in SIMPOC countries and offers limited support in the form of training courses and documentation. EpiInfo. 14 This word-processing, database, and statistics program for public health on IBM-compatible microcomputers is produced by the Centre for Disease Control and Prevention, in the USA. Many developing countries are using this software for data entry. CSPro. 15 The Census and Survey Processing System was also developed by the US Census Bureau. Incorporating many features of IMPS, ISSA, and EpiInfo, CSPro is designed to replace both IMPS and ISSA, eventually. Detailed evaluation of the above software lies beyond the scope of this manual. In general, however, availability of financial resources, trained personnel, and microcomputers are all-important considerations in choosing any child labour survey software. Where no other data entry software is available and trained national statistics office personnel are lacking, CSPro (see above), public domain software from the US Census Bureau, can be used for entering, tabulating, and mapping survey data. This software, together with its documentation, is free online, although online registration may be required. The US Census Bureau can arrange training programmes, but charges for them. According to the software documentation, it is possible to handle child labour survey data with this software. Nevertheless, although some national statistics offices reportedly use versions of this software, they have yet to be tried on child labour surveys specifically, and the training may be worth the cost. An alternative is Blaise (see above), a user-friendly, high-speed data entry and data manipulation software with an interactive editing facility and survey management capabilities. The software is not free, but is offered at a discounted price to developing countries. However, it has a number of characteristics that can make it harder for non-programmers to learn. One such characteristic is the use of advanced programming concepts such as data typing and procedure parameters. Another is the lack of structured forms to aid in defining questionnaire forms and variables. Blaise is not widely used outside Europe, moreover, so an established user base in developing countries does not yet exist. 11 Details may be obtained from Statistics Netherlands 12 Details may be obtained from U.S Census Bureau index.html 13 More information is available at SERPRO 14 Details may be obtained from Centre for Disease Control and Prevention gov/epiinfo/ 15 Details may be obtained from U.S Census Bureau cspro/index.html 15

22 In any case, software should be tested beforehand, and data entry operators should be both trained with the software and familiar with child labour surveys before actual data entry. Processing and tabulations Evidence suggests that virtually all national statistics offices have access to either SAS or SPSS or both statistical packages. Where that may not be the case, national statistical offices should try to adopt one standard statistical software package (e.g. SPSS, SAS, or Stata). Where that is not possible, data entry software can also be used for child labour survey data processing purposes. (Data analysis can be performed using EpiInfo, for example.) See Annex I for a comparison of the SAS, SPSS, and Stata statistical packages. Documentation and other tabulation Microsoft Office Suite, comprising Word, Excel, and Access, is being used by many statistical offices, and is adequate for creating the appropriate documentation, including creation of the questionnaires. Both MSExcel, a spreadsheet program, and Access, a database program, are user-friendly means to preparing tables. TPL, table generation software from QQQ software, 16 can also be used. Again, availability of resources and trained personnel should be the main criteria for choosing a particular software. Software utility tools The following list of software utilities is not an exhaustive one, and many other utility tools may currently be in use in various countries. Databases. General users are often unfamiliar with statistical packages, and they might prefer to have a subset of the data (or even the entire dataset) in a database format. Many statistical packages allow data to be saved in a database format, and database programs such as Microsoft Access are sometimes quite helpful. File compression software (e.g. WinZip, PKZIP, gzip). This software is used for compressing files. It is sometime possible to reduce the file sizes as much as 80 per cent or more using these kinds of software. Compression is useful where a hard disk is short of storage space or when using floppy disks to transfer files between computers. Compiling software (e.g. Visual Basic, FoxPro, C++). This is programming software other than that incorporated in the statistical package. Compilers can be used to develop user-friendly front-end for data entry, for example, or to produce customized, in-house automation software for performing repetitive tasks. Conversion software. Utility software such as STAT Transfer and DBMScopy converts files from one specific statistical package to another. SAS proc convert statements can easily convert SPSS portable files into SAS datasets. File transfer software. This is software that allows files to be transferred between computers, whether networked or not. These utilities include Direct Cable Connection, which is included in the Windows operating system, or LL3 for non-windows-based transfers. FTP programs are also helpful in transferring files among networked computers. 16 More details are available at QQQ Software, Inc 16