Repeated Measures Designs

1999 ◽  
Vol 27 (4) ◽  
pp. 552-578 ◽  
Author(s):  
Michael V. Ellis
Keyword(s):  
Repeated Measures ◽  
Single Subject ◽  
Psychology Research ◽  
Applied Psychology ◽  
Single Subject Research ◽  
Repeated Measures Design ◽  
Wide Range ◽  
Research Designs ◽  
Repeated Measures Designs

To facilitate innovation in applied psychology research, investigators need to be well-informed about available research designs. The purpose of this article is to provide an overview of repeated measures research designs (e.g., participants exposed to more than one treatment or measured on more than one occasion). My intent is twofold. First, I underscore the wide range of repeated measures research designs available to researchers in applied psychology. Second, I argue that the differentiation and polarity of group and single-subject research designs is largely arbitrary. I use examples to illustrate each repeated measures design and present its strengths and limitations.

SSD for R

2021 ◽  
Author(s):  
Charles Auerbach ◽  
Wendy Zeitlin
Keyword(s):  
Data Analysis ◽  
Visual Analysis ◽  
Statistical Significance ◽  
Single Subject ◽  
Single Subject Research ◽  
Level Data ◽  
Wide Range ◽  
Research Designs ◽  
Range Of Functions ◽  
Statistical Programming

Single-subject research designs have been used to build evidence to the effective treatment of problems across various disciplines, including social work, psychology, psychiatry, medicine, allied health fields, juvenile justice, and special education. This book serves as a guide for those desiring to conduct single-subject data analysis. The aim of this text is to introduce readers to the various functions available in SSD for R, a new, free, and innovative software package written in R, the robust open-source statistical programming language written by the book’s authors. SSD for R has the most comprehensive functionality specifically designed for the analysis of single-subject research data currently available. SSD for R has numerous graphing and charting functions to conduct robust visual analysis. Besides the ability to create simple line graphs, features are available to add mean, median, and standard deviation lines across phases to help better visualize change over time. Graphs can be annotated with text. SSD for R contains a wide variety of functions to conduct statistical analyses traditionally conducted with single-subject data. These include numerous descriptive statistics and effect size functions and tests of statistical significance, such as t tests, chi-squares, and the conservative dual criteria. Finally, SSD for R has the capability of analyzing group-level data. Readers are led step by step through the analytical process based on the characteristics of their data. Numerous examples and illustrations are provided to help readers understand the wide range of functions available in SSD for R and their application to data analysis and interpretation.

A Classroom Demonstration of Single-Subject Research Designs

1997 ◽  
Vol 24 (3) ◽  
pp. 188-190 ◽  
Author(s):  
James E. Carr ◽  
John Austin
Keyword(s):  
Repeated Measures ◽  
Single Subject ◽  
Single Subject Research ◽  
Psychology Majors ◽  
Research Designs ◽  
Undergraduate Psychology ◽  
Reversal Design ◽  
Single Subject Designs

This article provides a brief overview of single-subject designs and describes a classroom demonstration useful in teaching these designs to undergraduate psychology majors. Using a reversal design as a methodological frame, students collect repeated measures of their own behavior, and they graph and visually interpret the data.

Single-Subject Research Designs for Optometry

1985 ◽  
Vol 62 (8) ◽  
pp. 516-522 ◽  
Author(s):  
FRANK L. COLLINS ◽  
RUTH A. BAER ◽  
RONALD L. BLOUNT
Keyword(s):  
Single Subject ◽  
Single Subject Research ◽  
Research Designs

N = 1 Research Methodology in Counselling: Focusing upon the Individual Client

2003 ◽  
Vol 13 (2) ◽  
pp. 133-144 ◽  
Author(s):  
Christopher F. Sharpley
Keyword(s):  
Data Analysis ◽  
Large Scale ◽  
Single Subject ◽  
Health Practitioner ◽  
Design Data ◽  
Single Subject Research ◽  
Research Designs ◽  
Research Findings ◽  
Individual Client ◽  
The Individual

Although the last 20years have seen a focus upon evidence-based therapies, there are arguments that much of the so-called “evidence” produced is, in fact, irrelevant to the mental health practitioner in the field, principally because of the use of large-scale group designs in clinical controlled studies of the effectiveness of one therapy over another. By contrast, and with particular relevance to the practitioner who is both scientist and therapist, single subject research designs and methodologies for data analysis can be applied in ways that allow for generalisation to everyday practice. To inform the readership, the rationale underlying n = 1 studies is described, with some explanation of the major designs and their application to typical cases in guidance and counselling. Issues of inferential deductions from data, variations of design, data analysis via visual and statistical procedures, and replication are discussed. Finally, a case is argued for the introduction of n = 1 reports within the Australian Journal of Guidance and Counselling to better inform the readership about clinical research findings relevant to their practices.

Single-subject research designs for group counselors studying their own groups

1989 ◽  
Vol 14 (2) ◽  
pp. 93-97 ◽  
Author(s):  
Floyd F. Robison ◽  
D. Keith Morran ◽  
Diana Hulse-killacky
Keyword(s):  
Single Subject ◽  
Single Subject Research ◽  
Research Designs ◽  
Group Counselors

scholarly journals Investigating human skeletal muscle physiology with unilateral exercise models: when one limb is more powerful than two

2017 ◽  
Vol 42 (6) ◽  
pp. 563-570 ◽  
Author(s):  
Martin J. MacInnis ◽  
Chris McGlory ◽  
Martin J. Gibala ◽  
Stuart M. Phillips
Keyword(s):  
Skeletal Muscle ◽  
Repeated Measures ◽  
Statistical Power ◽  
Human Skeletal Muscle ◽  
Capillary Density ◽  
Muscle Physiology ◽  
Repeated Measures Design ◽  
Confounding Variables ◽  
Direct Sampling ◽  
Wide Range

Direct sampling of human skeletal muscle using the needle biopsy technique can facilitate insight into the biochemical and histological responses resulting from changes in exercise or feeding. However, the muscle biopsy procedure is invasive, and analyses are often expensive, which places pragmatic restraints on sample sizes. The unilateral exercise model can serve to increase statistical power and reduce the time and cost of a study. With this approach, 2 limbs of a participant are randomized to 1 of 2 treatments that can be applied almost concurrently or sequentially depending on the nature of the intervention. Similar to a typical repeated measures design, comparisons are made within participants, which increases statistical power by reducing the amount of between-person variability. A washout period is often unnecessary, reducing the time needed to complete the experiment and the influence of potential confounding variables such as habitual diet, activity, and sleep. Variations of the unilateral exercise model have been employed to investigate the influence of exercise, diet, and the interaction between the 2, on a wide range of variables including mitochondrial content, capillary density, and skeletal muscle hypertrophy. Like any model, unilateral exercise has some limitations: it cannot be used to study variables that potentially transfer across limbs, and it is generally limited to exercises that can be performed in pairs of treatments. Where appropriate, however, the unilateral exercise model can yield robust, well-controlled investigations of skeletal muscle responses to a wide range of interventions and conditions including exercise, dietary manipulation, and disuse or immobilization.

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