Rome, Italy Executive summary FAO is mandated to provide reliable figures of the true extent of the problem of undernutrition to help Member Nations in monitoring trends, determining priorities and evaluating the effectiveness of intervention programmes. In order to do that, there is a need to detect undernutrition in individuals and to assess the severity of the problem in the community. This paper evaluates the use of nutritional anthropometric measures to estimate the numbers of undernourished while highlighting the advantages and limitations of nutritional anthropometric approaches.
Data collection is a crucial step in the process of measuring program outcomes. By measuring outcomes, an organization can better recognize the effectiveness and value of its programs, and pinpoint where changes or improvements need to be made.
Before collecting data, your organization should have a solid understanding of the purpose of the program you wish to evaluate. You should have a working logic model that identifies your desired outcomes, the resources and activities necessary to accomplish these outcomes, and a detailed list of the specific measures you will take to implement them.
Once this piece is complete, you can begin gathering relevant data through surveys, interviews, focus groups, or other methods. Data collection happens before analysis and reporting. Valid and reliable data is the backbone of program analysis. Collecting this data, however, is just one step in the greater process of measuring outcomes.
The five steps include: Identify outcomes and develop performance measures. Create and implement a data collection plan discussed in this lesson. Reflect, learn, and do it again. This lesson will illustrate effective options and techniques for data collection.
At the end of this lesson, you will be able to understand how to plan for and implement data collection for a specific program; identify the most appropriate and useful data collection methods for your purposes; and manage and ensure the integrity of the data you collect.
Data Collection Methods Your data collection process will include attention to all the elements of your logic model: In collecting indicator data, you are likely to use one or more of these four methods: Surveys are standardized written instruments that can be administered by mail, email, or in person.
The primary advantage of surveys is their cost in relation to the amount of data you can collect. Surveying generally is considered efficient because you can include large numbers of people at a relatively low cost.
There are two key disadvantages: First, if the survey is conducted by mail, response rates can be very low, jeopardizing the validity of the data collected. There are mechanisms to increase response rates, but they will add to the cost of the survey. We will discuss tips for boosting response rates later in this lesson.
Thorough survey pre-testing can reduce the likelihood that problems will arise. Here are some examples of ways to use surveys: Survey all organizations receiving technical assistance to learn about changes in their fundraising tactics and the results of their efforts to raise more money.
Interviews are more in-depth, but can be cost-prohibitive. Interviews use standardized instruments but are conducted either in person or over the telephone. In fact, an interview may use the same instrument created for a written survey, although interviewing generally offers the chance to explore questions more deeply.
You can ask more complex questions in an interview since you have the opportunity to clarify any confusion. You also can ask the respondents to elaborate on their answers, eliciting more in-depth information than a survey provides.
The primary disadvantage of interviews is their cost. It takes considerably more time and therefore costs more money to conduct telephone and in-person interviews. Often, this means you collect information from fewer people.
Interview reliability also can be problematic if interviewers are not well-trained. They may ask questions in different ways or otherwise bias the responses. Here are some examples of ways to use interviews: Talk to different grassroots organizations to learn about the way in which they are applying new knowledge of partnership development.
Interview individuals within an organization to explore their perceptions of changes in capacity and ability to deliver services. Focus groups are small-group discussions based on a defined area of interest. While interviews with individuals are meant to solicit data without any influence or bias from the interviewer or other individuals, focus groups are designed to allow participants to discuss the questions and share their opinions.
This means people can influence one another in the process, stimulating memory or debate on an issue.Welcome to the e-learning lesson on Creating and Implementing a Data Collection Plan. Data collection is a crucial step in the process of measuring program outcomes.
By measuring outcomes, an organization can better recognize the effectiveness and value of its programs, and pinpoint where changes or improvements need to be made. Four Errors That Are Made When Developing Community Diagnosis From Survey Data and causes of organizational ineffectiveness.
Recognize the various techniques for gathering information from client systems. Telephone survey houses historically have routinely made 20 or more call-backs to households that do not answer the telephone.
This practice has dwindled due to a combination of the expense of conducting so many call-backs, and the dramatic growth of online surveys, where it is just easier to replace non-responders with fresh sample.
With regards to identifying cases, four CHWs adopted a house to house approach whilst the remaining utilised the community announcement system to raise awareness if the survey.
One individual that employed the house to house technique stated “This (hydrocoele) is a secret disease, therefore you must approach people sensitively one by one”.
errors are errors in diagnosis (Table 1).1–3, 5, 11, 13–21 A recent review of 53 autopsy studies found an average rate of percent major missed diagnoses (range = . Example: A retail store would like to assess customer feedback from at-the-counter purchases. The survey is developed but fails to target those who purchase in the store.
Instead, results are skewed by customers who bought items online. Now you know how to avoid common errors in the research process, read 5 Ways to Formulate the Research .