Multiple time-series designs refer to the use of more than one set of data that were collected over time, as in accident rates over time in several states or cities, so that comparison can be made.

Multiple time-series designs are a type of research design used in psychology to study changes in behavior or other variables over time. These designs involve collecting multiple measurements of a behavior or variable over time, and analyzing the data to identify patterns or changes in the behavior or variable of interest.

Here are some examples of how multiple time-series designs might be used:

  1. Treatment evaluation: In clinical psychology, multiple time-series designs might be used to evaluate the effectiveness of a treatment or intervention for a particular condition. Researchers might collect data on the behavior or symptoms of participants before, during, and after the treatment, to determine whether the treatment has an effect on the behavior or symptoms.

  2. Program evaluation: Multiple time-series designs might also be used to evaluate the effectiveness of a program or policy over time. For example, researchers might collect data on crime rates before and after the implementation of a new policy, to determine whether the policy has an impact on crime.

  3. Developmental studies: In developmental psychology, multiple time-series designs might be used to study changes in behavior or abilities over time. For example, researchers might collect data on a child's language development at multiple time points, to identify patterns or changes in language acquisition.

To analyze multiple time-series data, researchers might use statistical techniques such as regression analysis, time-series analysis, or interrupted time-series analysis. By using multiple measurements over time, researchers can gain a more detailed understanding of how behavior or variables change over time, and can identify factors that might influence these changes.

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