Podcast
Questions and Answers
What is the purpose of inferential statistical analysis?
What is the purpose of inferential statistical analysis?
Why are experiments performed according to the text?
Why are experiments performed according to the text?
What does it mean if dependent variables must be stable?
What does it mean if dependent variables must be stable?
Who does data belong to, according to Sidman (1960)?
Who does data belong to, according to Sidman (1960)?
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What is the primary reason for using Small-N Designs?
What is the primary reason for using Small-N Designs?
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What is raw data as defined in the text?
What is raw data as defined in the text?
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What makes Mixed Designs unique?
What makes Mixed Designs unique?
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Why is it important for an experimenter to choose independent variables carefully?
Why is it important for an experimenter to choose independent variables carefully?
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Who is credited with the systematic development of Mixed Designs?
Who is credited with the systematic development of Mixed Designs?
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What is the primary characteristic of Small-N designs?
What is the primary characteristic of Small-N designs?
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When should Control Condition Designs be used?
When should Control Condition Designs be used?
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What is a common problem associated with Small-N designs?
What is a common problem associated with Small-N designs?
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What is the historical origin of mixed methods research according to prevalent views?
What is the historical origin of mixed methods research according to prevalent views?
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In Small-N designs, how is data typically analyzed?
In Small-N designs, how is data typically analyzed?
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What drives the use of Mixed Designs in research?
What drives the use of Mixed Designs in research?
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What distinguishes differential carryover effects from general practice effects?
What distinguishes differential carryover effects from general practice effects?
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Why are Small-N designs particularly useful in clinical studies?
Why are Small-N designs particularly useful in clinical studies?
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Which field commonly utilizes Small-N designs for research purposes?
Which field commonly utilizes Small-N designs for research purposes?
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Study Notes
Data and Science
- Data is considered tainted at birth and belongs either to Chance or to Science, but never both.
- Inferential statistical analysis is used to exercise the demon Chance and ensure data belongs to Science.
Experiments
- An experiment is a controlled procedure to investigate the effects of one or more independent variables on one or more dependent variables.
- Experiments aim to eliminate or minimize extraneous variation and are performed to test theories, replicate and expand previous findings, or contradict prior research.
Independent and Dependent Variables
- Independent variables are chosen because they are thought to control behavior, and their manipulation is inadequate if they do not produce the desired effect.
- Dependent variables must be stable and consistently produce the same results under the same conditions.
Behavioral Research Designs
- Small-N Designs are used when interested in minute details of individual performance or behavioral change, or when few subjects are available.
- Mixed Designs combine within-subjects and fully-crossed factorial designs and are used when both quantitative and qualitative data are needed.
Mixed Designs
- Mixed Designs originated in agricultural research and were popularized by Campbell and Fiske's work on triangulation in 1959.
- Data analysis in Mixed Designs involves combining quantitative and qualitative data.
Control Condition Designs
- Control Condition Designs ensure each treatment appears equally often in different positions within the sequence to prevent differential carryover effects.
- Complete counterbalancing makes sure all possible treatment orders are used to balance out order effects.
Small-N Designs
- Small-N Designs involve one or a few subjects, run individually, with long experiment duration, and data analyzed visually with minimal use of inferential statistics.
- Small-N Designs are used in clinical studies, education, and training, and have a long history dating back to Sir Francis Galton's work on correlations and Sir Ronald Fisher's development of ANOVA.
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Description
Explore the fundamental concepts in data analysis in psychology through the lens of Sidman (1960). Learn about the process of inferential statistical analysis, reporting data, and working with raw data.