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A common approach to needs analysis involves the development of a self-report questionnaire administered to members of the intended audience. Such an approach poses a problem however: Concern for data reliability demands polling as many respondents as possible, while concern for cost-effectiveness dictates minimizing the size of the sample. The solution involves a sub-group of the population responding to all items in the questionnaire, and parameters estimated from the sample data. Yet this approach may not be the most effective and efficient one. Multiple matrix sampling (MMS), a technique whereby both the population of respondents and the universe of items are jointly sampled, may be the answer. Basically, this technique involves the random assignment of some items to a random sample of respondents, with other random samples of respondents receiving different random items. A computer estimates the parameters from the sample data gathered. The study reported herein begins with the pool of data generated by a self-report questionnaire needs analysis and proceeds to estimate those same parameters through the use of MMS. These estimates are then compared to the known values and to corresponding estimates from traditional respondent-sampling techniques. The paper concludes with a summary of the advantages and disadvantages of MMS. (Author/BL)

A common approach to needs analysis involves the development of a self-report questionnaire administered to members of the intended audience. Such an approach poses a problem however: Concern for data reliability demands polling as many respondents as possible, while concern for cost-effectiveness dictates minimizing the size of the sample. The solution involves a sub-group of the population responding to all items in the questionnaire, and parameters estimated from the sample data. Yet this approach may not be the most effective and efficient one. Multiple matrix sampling (MMS), a technique whereby both the population of respondents and the universe of items are jointly sampled, may be the answer. Basically, this technique involves the random assignment of some items to a random sample of respondents, with other random samples of respondents receiving different random items. A computer estimates the parameters from the sample data gathered. The study reported herein begins with the pool of data generated by a self-report questionnaire needs analysis and proceeds to estimate those same parameters through the use of MMS. These estimates are then compared to the known values and to corresponding estimates from traditional respondent-sampling techniques. The paper concludes with a summary of the advantages and disadvantages of MMS. (Author/BL)

A common approach to needs analysis involves the development of a self-report questionnaire administered to members of the intended audience. Such an approach poses a problem however: Concern for data reliability demands polling as many respondents as possible, while concern for cost-effectiveness dictates minimizing the size of the sample. The solution involves a sub-group of the population responding to all items in the questionnaire, and parameters estimated from the sample data. Yet this approach may not be the most effective and efficient one. Multiple matrix sampling (MMS), a technique whereby both the population of respondents and the universe of items are jointly sampled, may be the answer. Basically, this technique involves the random assignment of some items to a random sample of respondents, with other random samples of respondents receiving different random items. A computer estimates the parameters from the sample data gathered. The study reported herein begins with the pool of data generated by a self-report questionnaire needs analysis and proceeds to estimate those same parameters through the use of MMS. These estimates are then compared to the known values and to corresponding estimates from traditional respondent-sampling techniques. The paper concludes with a summary of the advantages and disadvantages of MMS. (Author/BL)

A common approach to needs analysis involves the development of a self-report questionnaire administered to members of the intended audience. Such an approach poses a problem however: Concern for data reliability demands polling as many respondents as possible, while concern for cost-effectiveness dictates minimizing the size of the sample. The solution involves a sub-group of the population responding to all items in the questionnaire, and parameters estimated from the sample data. Yet this approach may not be the most effective and efficient one. Multiple matrix sampling (MMS), a technique whereby both the population of respondents and the universe of items are jointly sampled, may be the answer. Basically, this technique involves the random assignment of some items to a random sample of respondents, with other random samples of respondents receiving different random items. A computer estimates the parameters from the sample data gathered. The study reported herein begins with the pool of data generated by a self-report questionnaire needs analysis and proceeds to estimate those same parameters through the use of MMS. These estimates are then compared to the known values and to corresponding estimates from traditional respondent-sampling techniques. The paper concludes with a summary of the advantages and disadvantages of MMS. (Author/BL)

A common approach to needs analysis involves the development of a self-report questionnaire administered to members of the intended audience. Such an approach poses a problem however: Concern for data reliability demands polling as many respondents as possible, while concern for cost-effectiveness dictates minimizing the size of the sample. The solution involves a sub-group of the population responding to all items in the questionnaire, and parameters estimated from the sample data. Yet this approach may not be the most effective and efficient one. Multiple matrix sampling (MMS), a technique whereby both the population of respondents and the universe of items are jointly sampled, may be the answer. Basically, this technique involves the random assignment of some items to a random sample of respondents, with other random samples of respondents receiving different random items. A computer estimates the parameters from the sample data gathered. The study reported herein begins with the pool of data generated by a self-report questionnaire needs analysis and proceeds to estimate those same parameters through the use of MMS. These estimates are then compared to the known values and to corresponding estimates from traditional respondent-sampling techniques. The paper concludes with a summary of the advantages and disadvantages of MMS. (Author/BL)