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Related Concept Videos

Case Studies01:22

Case Studies

There are many research methods available to psychologists in their efforts to understand, describe, and explain behavior and the cognitive and biological processes that underlie it.
One-Way ANOVA01:18

One-Way ANOVA

One-way ANOVA analyzes more than three samples categorized by one factor. For example, it can compare the average mileage of sports bikes. Here, the data is categorized by one factor - the company. However, one-way ANOVA cannot be used to simultaneously compare the sample mean of three or more samples categorized by two factors. An example of two factors would be sports bikes from different companies driven in different terrains, such as a desert or snowy landscape. Here, two-way ANOVA is used...
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures from...
Observational Studies01:11

Observational Studies

Observational studies are a type of analytical study where researchers observe events without any interventions. In other words, the researcher does not influence the response variable or the experiment's outcome.
There are three types of observational studies – Prospective, retrospective, and cross-sectional.
Prospective Study
Prospective studies, also known as longitudinal or cohort studies, are carried out by collecting future data from groups sharing similar characteristics. One example of...
Longitudinal Research02:20

Longitudinal Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
Two-Way ANOVA01:17

Two-Way ANOVA

The two-way ANOVA is an extension of the one-way ANOVA. It is a statistical test performed on three or more samples categorized by two factors - a row factor and a column factor. Ronald Fischer mentioned it in 1925 in his book 'Statistical Methods for Researchers.'
The two-way ANOVA analysis initially begins by stating the null hypothesis that there is an interaction effect between the two factors of a dataset. This effect can be visualized using line segments formed by joining the means for...

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Related Experiment Video

Updated: May 18, 2026

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

Augmenting visual analysis in single-case research with hierarchical linear modeling.

Dawn H Davis1, Phill Gagné, Laura D Fredrick

  • 1Georgia State University, Atlanta, USA. ddavis2@gsu.edu

Behavior Modification
|September 15, 2012
PubMed
Summary
This summary is machine-generated.

Hierarchical linear modeling (HLM) enhances visual analysis of single-case research (SCR) by quantifying group-level effects and individual variability. This approach strengthens the identification of evidence-based interventions for diverse learners.

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Last Updated: May 18, 2026

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Area of Science:

  • Educational Psychology
  • Applied Behavior Analysis
  • Quantitative Research Methods

Background:

  • Single-case research designs (SCR) are crucial for evaluating interventions.
  • Visual analysis is the traditional method for interpreting SCR data.
  • Limitations exist in visual analysis for complex designs and detecting subtle individual differences.

Purpose of the Study:

  • To demonstrate how hierarchical linear modeling (HLM) can enhance visual analysis in SCR.
  • To quantify group-level effects and individual variability in SCR data.
  • To highlight the combined utility of HLM and visual analysis for identifying evidence-based interventions.

Main Methods:

  • Growth modeling using HLM was applied to a delayed multiple baseline design with an embedded changing criterion design.
  • Repeated-measures HLM and visual analysis were used with simulated data from an ABAB design.
  • Data involved students with moderate intellectual disabilities (MoID) in a literacy project.

Main Results:

  • HLM quantified group-level functional relations and revealed significant variability in baseline probes and growth trajectories.
  • Receptive vocabulary and print knowledge were significant predictors of sight-word acquisition and growth rates.
  • HLM confirmed functional relations in simulated data and identified significant participant-level variance undetectable by visual analysis alone.

Conclusions:

  • Combining HLM with visual analysis offers a more robust approach to SCR data interpretation.
  • HLM provides quantitative insights into individual differences and intervention effects.
  • This integrated methodology supports the identification and validation of evidence-based interventions.