So that frequency measures can be meaningfully compared across observation sessions when the target response is opportunity-bound or trial-bound (i.e., the response can't meaningfully occur in the absence of a specified stimulus), frequency measures should

A. be converted to response rate.
B. be converted to percent of opportunities to respond.
C. be reported as an average across all observations.
D. not be used.

Question

21-07-2024
Social Studies

Answered

So that frequency measures can be meaningfully compared across observation sessions when the target response is opportunity-bound or trial-bound (i.e., the response can't meaningfully occur in the absence of a specified stimulus), frequency measures should

A. be converted to response rate.
B. be converted to percent of opportunities to respond.
C. be reported as an average across all observations.
D. not be used.

Answer :

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GinnyAnswer GinnyAnswer · Answer 1 · 2024-07-21T12:45:13-04:00

To ensure that frequency measures can be meaningfully compared across different observation sessions when the target response is either opportunity-bound or trial-bound, we need to standardize how we report those measures. In such cases, where the response cannot meaningfully occur unless a specific stimulus is present, converting the raw frequency measures into a more standardized form is essential. This allows for a fair comparison across different sessions regardless of the varying number of opportunities or trials presented in each session. Here's the detailed reasoning:

1. Problem Context:
- Target response is dependent on specific opportunities or trials (i.e., it can't occur without a specified stimulus).
- Goal is to make the frequency measures comparable across different observation sessions.

2. Options:
- Response rate: This measure typically involves calculating the frequency of the response per unit of time. While useful in some contexts, it doesn't account for variations in the number of opportunities to respond.
- Percent of opportunities to respond: By converting the frequency of responses into a percentage of the total opportunities to respond, we normalize the data. This approach enables meaningful comparisons regardless of the actual number of opportunities.
- Reported as an average across all observations: While averaging can provide some insights, this method may not address the variability in the number of opportunities across different sessions.
- Not used: Discarding the frequency measures entirely is not practical, as they provide valuable data about response patterns.

3. Best Solution:
- Converting the frequency measures to the percent of opportunities to respond is the most effective solution. This standardization method ensures fairness and comparability across various observation sessions, even if the number of opportunities differs from session to session.

Conclusion:
To ensure frequency measures are comparable across different observation sessions, the correct approach is to convert them to percent of opportunities to respond. This way, we account for the variability in the number of opportunities or trials presented, leading to more meaningful and interpretable data.

Thus, the answer is:
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be converted to percent of opportunities to respond.
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