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- Group discussion of thc questionnairc, with dctailed consideration of appropriatc phrases, the meaning and purpose of questions, format, ctc.;

- Timed group interviews of one individual;

- Individual practice intcrviews conducted at the surveyor's homc; and

- Individual practice interviews conducted near the office.

Lastly, it is essential to stress the importance of building good morale and an esprit de corps amongst the surveyors. The success of the survey depends on their understanding and acceptance of the procedures, and on their initiative in handling problems properly. It is necessary to build a real survey team by: (a) continually reminding them of the purpose of the survey, the importance of getting accurate information, and the link between a good database and good policies/projects; (b) involving them in formulating questions and improving the questionnaire, both during training and after the pilot test; (c) convoking regular debriefing meetings, and asking interviewers for a final written report assessing their work, overall survey organization and recommendations for improvement; and (d) informing them of preliminary results, especially if day-to-day processing is conducted in the field as soon as completed questionnaires are returned.

# Data Processing and Analysis

The most common administrative problem for household surveys is the underestimation of data cleaning and analysis requirements. The results of surveys that cost tens or hundreds of thousands of dollars frequently are used only for very simple calculations of trends, if they are used at all. Once the preliminary analysis has been completed, the survey results are often abandoned and never used again. People, in many cases, seem to be satisfied that they have generated new information but are less willing to fully analyze it, and budgets usually reflect this bias. An accurate outline of what actually needs to be done to get, analyze and present good results is outlined here.

Cleaning the Data. Checking survey forms, coding, editing, keypunching and tabulating data is a time-consuming process. It can take several months just between the collection of data and its availability for analysis. Editing is a major stage in survey data processing. Its objective is to detect and correct errors so that a satisfactory quality of raw data can be obtained. Correction may cause other errors and, whenever possible, these should be documented. Where possible, editing should be done by computers which can use data entry programs. These use a set of files that store the characteristics of the questionnaire, possible variable values, skip patterns, and shape and functions of the data entry screens. As data are keyed in, they are submitted to a set of standard checks contained in the data entry files. Numeric variables are constrained to lie between minimum and maximum values, qualitative variables can only have certain valid codes, and chronological variables must contain valid dates. In this manner, improperly recorded or keyed in data can be automatically identified.

Computer Processing. Computer processing facilitates and speeds up the analysis of data, quickly calculates statistical significance, and helps establish relationships among variables. Several computer packages are available for this purpose. Two of the most common are SAS (Statistical Analysis System) and SPSS (Statistical Package for the Social Sciences). SAS and SPSS can tabulate answers by absolute, relative and cumulative frequency, and they automatically calculate values such as the mean, median, mode, standard deviation, variance, minimum, maximum, range, number of valid cases and bias. However, there are other software packages which may be more appropriate depending on the type of computer used, length of the questionnaire, number of variables and number of respondents.

For utilizing the data, computers are especially useful for conducting multiple regression analysis to test the important hypothesized relationships between a set of independent and dependent variables. Computer processing facilitates cross-tabulation of, for example, answers about stove characteristics which need to be broken down according to type of stove. It is also useful to use computer packages to analyze the statistical limits of the data by dividing response error frequency into those attributable to the enumerator, and respondent error due to bias, ignorance and memory lapse. Finally, an optical scanner can be used with a computer and