DataManagement_GoodPractice - Case Study No. 6 Good...

Info iconThis preview shows pages 1–3. Sign up to view the full content.

View Full Document Right Arrow Icon
1 Case Study No. 6 Good practice in data management This Case Study is drawn from the DFID-funded Farming Systems Integrated Pest Management (FSIPM) Project conducted between 1996 and 1999 in Blantyre-Shire Highlands in Southern Malawi. It describes procedures for data management that are designed to achieve project outputs based on high quality data. The case study also highlights resources needed for this purpose. 1. Background A large number of information collection exercises took place during the FSIPM Project. They included diagnostic surveys, a baseline survey, meetings with farmers, transect surveys, scoring and ranking exercises, monitoring exercises and numerous measurements on research plots in farmers’ fields to evaluate pest attack and crop yields. Managing this large volume of data through a well defined data management system was crucial to the success of the project. Methodological aspects of the data management process are described below using examples drawn from the FSIPM project’s activities. These relate largely to the experimental trials conducted on farmers’ fields but the general concepts are equally applicable to other data collection exercises. 2. Steps towards good data management practices 2.1 Preparing forms for data collection Different types of data collection forms were constructed each activity. For example, Pre-coded questionnaires were used in formal survey work, (e.g. the Baseline Survey). Where open-ended questionning was envisaged (e.g. study of networks of communication), the form had broad discussion headings with space between to enter notes. In recording pest damage and crop yield measurements on experimental plots in farmers’ fields, the data collection form typically contained background information to identify the plot location (e.g. village, farm identification, field number, plot number) and blank columns with headings to enter the measurements. Additional “check” columns were included where necessary, e.g. total grain weight was recorded in addition to damaged grain weight and usable grain weight; the number of plants with pods was used as a check on grain yields. Units of measurement were made clear on the recording sheet. Space was included for comments so that unusual occurrences in the field could be recorded (e.g. waterlogging causing stunting of plants). Pre-testing of recording forms in the field was important and the forms were modified where needed. Separate recording forms or questionnaires were used for collecting plot level information (e.g. crop yields, pest damage), field level information (e.g. soil type) and farm level information (e.g. socio-economic variables). Recording sheets were set up in a way that allowed the data to be entered directly into a computer. Designing the data entry form required a skilled person with a good awareness of
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
2 the objectives of the data collection process. This is rarely the data entry person. Figure 1 shows a typical example. Figure 1
Background image of page 2
Image of page 3
This is the end of the preview. Sign up to access the rest of the document.

Page1 / 6

DataManagement_GoodPractice - Case Study No. 6 Good...

This preview shows document pages 1 - 3. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online