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Related Concept Videos

Surveys02:16

Surveys

Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally. Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.
Ordinal Level of Measurement00:55

Ordinal Level of Measurement

The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using an ordinal scale are similar to nominal scale data, but there is one major difference. The ordinal scale data can be ordered. An example of ordinal scale data is a list of the top five national parks in the...
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
Nominal Level of Measurement00:56

Nominal Level of Measurement

The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. Not every statistical operation can be used with every set of data. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
The data that cannot be measured but can be grouped into categories fall under the nominal level of measurement. Data that is measured using a nominal scale is...

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Related Experiment Video

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Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

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Constructing quantitative implementation scales from categorical services data. Examples from a multisite evaluation.

R G Orwin1, L J Sonnefeld, D S Cordray

  • 1R.O.W. Sciences, Inc., USA.

Evaluation Review
|March 8, 1998
PubMed
Summary

This study demonstrates deriving quantitative implementation scales from qualitative data. This approach enhances analysis when individual-level data is impractical, improving program evaluation.

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Area of Science:

  • Program evaluation
  • Implementation science
  • Quantitative and qualitative research methods

Background:

  • Client-level quantitative data is ideal for implementation and outcome measures but often impractical to collect.
  • Feasible data collection often relies on cruder, categorical service data.
  • Evaluators need methods to maximize insights from available, less granular data.

Purpose of the Study:

  • To describe a method for deriving quantitative, client-level implementation scales from qualitative data.
  • To support cross-site synthesis of implementation and outcome analyses in a multisite evaluation.
  • To suggest broader applications for creating quantitative implementation scales from qualitative service data.

Main Methods:

  • Development of quantitative, client-level implementation scales.
  • Derivation of scales from qualitative (categorical) service data.
  • Application of scales in a multisite evaluation for synthesis of implementation and outcome analyses.

Main Results:

  • Successfully derived quantitative implementation scales from qualitative data.
  • Enabled robust cross-site synthesis of implementation and outcome analyses.
  • Demonstrated a viable strategy to compensate for data collection limitations.

Conclusions:

  • Quantitative implementation scales can be effectively derived from qualitative data.
  • This method enhances the analytical power of feasible data collection strategies.
  • The approach offers a valuable tool for program evaluation and implementation science.