benefits of single subject research design

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The benefits of single-subject research designs and multi-methodological approaches for neuroscience research

April becker.

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Edited by: Erik Arntzen, Oslo Metropolitan University, Norway

Reviewed by: Christoffer Eilifsen, Oslo Metropolitan University, Norway; A. Imam, John Carroll University, United States

*Correspondence: April Becker [email protected]

Received 2023 Mar 20; Accepted 2023 Sep 27; Collection date 2023.

Keywords: single-subject, experimental design, neuroscience, within-subject, validity

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

1. Introduction

The scientific method is neither singular nor fixed; it is an evolving, plural set of processes. It develops and improves through time as methodology rises to meet new challenges (Lakatos, 1978 ; Hull, 1988 ; Kuhn and Hacking, 2012 ). “It would be wrong to assume that one must stay with a research programme until it has exhausted all its heuristic power, that one must not introduce a rival programme before everybody agrees that the point of degeneration has probably been reached” (Lakatos, 1978 ). These insights apply not least to experimental design approaches.

For better and for worse, no experimental design comes without limitation. We must accept that the realities of the world cannot be simplistically verified against universal standard procedures; we are free instead to explore how the progressive evolution of experimental design enables new advancement. This paper proposes support for a shift of focus in the methodology of experimental research in neuroscience toward an increased utilization of single-subject experimental designs. I will highlight several supports for this suggestion. Most importantly, single-subject methods can complement group methodologies in two ways: by addressing important points of internal validity and by enabling the inductive process characteristic of quality early research. The power of these approaches has already been somewhat established by key historical neuroscience experiments. Additionally, the individuated nature of subject matter in behavioral neuroscience makes the single-subject approach particularly powerful, and single-subject phases in a research program can decrease time and resource costs in relation to scientific gains.

2. Complimentary research designs

Though the completely randomized group design is considered by many to be the gold standard of evidence (Meldrum, 2000 ), its limitations as well as ethical and logistical execution difficulties have been noted: e.g., blindness to group heterogeneity, problematic application to individual cases, and experimental weakness in the context of other often-neglected aspects of study design such as group size, randomization, and bias (Kravitz et al., 2004 ; Grossman and Mackenzie, 2005 ; Williams, 2010 ; Button et al., 2013 ). Thus, the concept of a “gold standard” results not from the uniform superiority of a method, but from an implicit valuing of its relative strengths compared to other designs, all things being equal (even though such things as context, randomization, group size, bias, heterogeneity, etc. are rarely equal). There is an alternative to this approach. Utilizing a wider array of methods across studies can help compensate for the limitations of each and provide flexibility in the face of unequal contexts. In a multi-methodological approach, different experimental designs can be evaluated in terms of complementarity rather than absolute strength. If one experimental design is limited in a particular way, adding another approach that is stronger in that aspect (but perhaps limited in another) can provide a more complete picture. This tactic also implicitly acknowledges that scientific rigor does not proceed only from the single study; replication, systematic replication, and convergent evidence may proceed from a progression of methods.

I suggest adding greater utilization of single-subject design to the already traditionally utilized between-subject and within-subject group designs in neuroscience to achieve this complementarity. The advantages and limitations of these designs are somewhat symmetrical. Overall, single-subject experiments carry with them more finely-focused internal validity because the same subject (together with their array of individual characteristics) serves in both the experimental and control conditions. Unlike in typical within-subject group comparisons, the repetition of comparisons in single-subject designs control for other confounding variables, rendering n = 1 into a true experiment. While an unreplicated single-subject experiment by itself cannot establish external validity, systematic replication of single-subject experiments over the relevant range of individual differences can. On the other hand, group designs cannot demonstrate an effect on an individual level, but within-individual group studies can characterize the generality of effects across large populations in a single properly sampled study, and may be particularly suited to analyzing combined effects of multiple variables (Kazdin, 1981 ). Single subject and group approaches can also be hybridized to fit a study's goals (Kazdin, 2011 ). In the following sections, I will describe aspects of each approach that illustrate how the addition of single-subject methodology to neuroscience could be of use. I do not mean to exhaustively describe either methodology, which would be outside the scope of this paper.

2.1. Group designs

Group experimental designs 1 interrogate the effect of an independent variable (IV) by applying that variable to a group of people, other organisms, or other biological units (e.g., neurons) and usually—but not always—comparing an aggregated population measure to that of one or more control groups. These designs require data from multiple individuals (people, animals, cells, etc.). Group experiments with between-group comparisons often assign these individuals to conditions (experimental or control) randomly. Other group experiments (such as a randomized block design) assign individuals to conditions systematically to explicitly balance the groups according to particular pre-considered individual factors. In both cases, the assumption is that if alternative variables influence the dependent variable (DV), they are unlikely to do so differentially across groups. Group experiments with within-subject comparisons expose each individual to both experimental and control conditions at different times and compare the grouped measures between conditions; this approach assures that the groups are truly identical since the same individuals are included in both conditions.

Because they involve multiple individuals, some group designs can provide important information about the generality of an effect across the included population, especially in the case of within-subject group designs. Unfortunately, some often-misused aspects of group designs tend to temper this advantage. For example, restricted inclusion criteria are often necessary to produce clear results. When desired generality involves only such a restricted population (e.g., only acute stroke patients, or only layer IV glutamatergic cortical neurons), this practice carries no disadvantage. However, if the study aims to identify more widely applicable processes, stringent inclusion criteria can produce cleaner but overly conditional results, limiting external validity (Henrich et al., 2010 ). Further, the analysis approach taken in many group designs that narrowly examines changes in central tendency (such as the mean) of groups can limit the assessment of generality within the sampled population since averaging will wash out heterogeneity of effects. Other aspects of rigor in group designs can also affect external validity (e.g., Kravitz et al., 2004 ; Grossman and Mackenzie, 2005 ; Williams, 2010 ; Button et al., 2013 ).

Another limitation of group design logic is the practical difficulty of balancing individual differences between groups. In the case of between-group comparisons, these difficulties arise from selection bias, mortality, etc. Even well controlled studies can still produce probabilistically imbalanced groups, especially in the small sample sizes often used in neuroscience research (Button et al., 2013 ). Deliberately balanced groups or post-hoc statistical control may help, but the former introduces a potential problem with true randomization, and the latter is weaker than true experimental control. Within-subject group comparisons implement both experimental and control conditions for each individual in a group and therefore better control for individual differences, however these designs still do not experimentally establish effects within the individual since single manipulations of experimental conditions can be confounded with other changes on an individual level.

The typical focus on parameters such as the mean in the analysis of group designs can also threaten internal as well as external validity, particularly if the experimental question concerns biological or behavioral variables that are highly individually contextualized or developmentally variant. 2 This problem extends from the fact that aggregate measures across populations do not necessarily reflect any of the underlying individuals (e.g., Williams, 2010 ); for example, average brain functional mapping tends not to apply to individual brains (Brett et al., 2002 ; Dworetsky et al., 2021 ; Fedorenko, 2021 ; Hanson, 2022 ). This kind of problem is particularly amplified in the study of human behavior and brain sciences, which both tend to be highly idiosyncratic. In these cases, aggregated measures can mask key heterogeneity including contradictory effects of IVs. This can complicate the application of results to individuals: an issue especially relevant in clinical research (Sidman, 1960 ; Williams, 2010 ). Relatedly, the estimation of population-based effect size provides scant information with which to estimate effects and relevance for an individual. Post-hoc statistical analysis may help to tease out these issues, but verification still requires new experimentation. True generality of a scientific insight requires not only that effects occur with reasonable replicability across individuals, but that a reasonable range of conditions that would alter the effect can be predicted: a difficult point to discern in group studies. Thus, while group designs carry advantages insofar as they can be used to characterize effects across a whole population in a single experiment, those advantages can be and often are subverted. Perhaps counter-intuitively, single-subject approaches can be ideal for methodically discovering the common processes that underlie diversity within a population, which have made it particularly powerful in producing generalizable results (see next section).

2.2. Single-subject designs

Single-subject designs compare experimental to control conditions repeatedly over time within the same individual. Like group designs with within-subject comparisons, single-subject designs can control for individual differences, which remain constant. However, single-subject designs take individual control to a new level. Since other confounding changes may coincide with a single change in the IV, single-subject designs also require multiple implementations of the same manipulation so that the comparison can be repeated within the individual, controlling for the coincidental confounds of a single condition change. Additionally, single-subject designs measure multiple data points through time within each condition before any experimental change occurs to assess pre-existing variation and trends in comparisons with the subsequent condition. Of course, a single-subject experiment without inter-individual replication has no generality—systematic replications across relevant individual characteristics and contexts are generally required to establish external validity. However, the typical group design also often requires similar replication to establish the same validity, and unlike group designs single-subject studies are also capable of rigorously interrogating even the rarest of effects.

Because single-subject experiments deal well with individual effects, they are often used in clinical and closely applied disciplines, e.g., education (Alnahdi, 2015 ), rehabilitation and therapy (Tankersley et al., 2006 ), speech and language (Byiers et al., 2012 ), implementation science (Miller et al., 2020 ), neuropsychology (Perdices and Tate, 2009 ), biomedicine (Janosky et al., 2009 ), and behavior analysis (Perone, 1991 ). However, the single-subject design is not limited to clinical applications or to the study of rare effects; it can also be used for the study of generalizable individual processes via systematic replication. Serial replications often enable detailed distillation of both common and uncommon relevant factors across individuals, making the approach particularly powerful for identifying generalizable processes that account for within-population diversity (although this process can be challenging even on the single-subject level; see Kazdin, 1981 ). Single-subject methodology has historically established some of the most generalizable findings in psychology including the principles of Pavlovian and operant conditioning (Iversen, 2013 ). Establishing this generalizability requires a research program rather than a single study, however since each replication (and comparisons between them) can potentially add information about important contextual variables, systematic progression toward generality can be more efficient than in one-shot group studies.

Single-subject designs are sometimes confused with within-subject group comparisons or n-of-1 case studies, neither of which usually include multiple implementations of each condition for any one individual. N-of-1 case studies sometimes make no manipulation at all or may make a single comparison (as with an embedded AB design or pre-post observation), which can at best serve as a quasi-experiment (Kazdin and Tuma, 1982 ). A single subject design, in contrast, will include many repeated condition changes and collect multiple data points inside each condition (as in the ABABABAB design as well as many others, see Perone, 1991 ). As is the case for group designs, the quality of evidence in a single-subject experiment increases with the number of instances in which the experimental condition is compared to a control condition; the more comparisons occur, the less likely it is that an alternative explanation will have tracked with the manipulation. A strong single-subject design will require a minimum of three IV implementations for the same individual (i.e., ABABAB, with multiple data points for each A and each B), and a robust effect will require many more.

Because single-subject designs implement conditions across time, they are susceptible to some important limitations including sequence, maturation, and exposure effects. The need to consider within-condition stability, serial dependence in data sets, reversibility, carryover effects, and long experimental time courses can also complicate these designs. Still, manipulations common in neuroscience research is often amenable to these challenges (Soto, 2020 ). Single-subject designs for phenomena that are not reversable (such as skill acquisition) can also be studied using approaches such as the within-subject multiple baseline. Multiple baselines experiments across behaviors, across cell populations, or across homotopic brain regions may be reasonable if independence can be established (Soto, 2020 ). A variety of single-subject methods are available that can help to address the unique strengths and limitations in single-subject methodology; the reader is encouraged to explore the variety of designs that cannot be enumerated in the scope of the current paper (Horner and Baer, 1978 ; Hains and Baer, 1989 ; Perone, 1991 ; Holcombe et al., 1994 ; Edgington, 1996 ; Kratochwill et al., 2010 ; Ward-Horner and Sturmey, 2010 ).

2.3. A note about statistical methods

Issues relating to statistical analysis are commonly erroneously conflated with group experimental design per se . Problems with the frequentist statistical approach commonly used in group designs has greatly impacted its efficacy; frequentist statistical methods carry limitations that have been treated thoroughly elsewhere [e.g., the generic problems with null-hypothesis statistical testing NHST (Branch, 2014 ), the inappropriate use of frequentist statistics contrary to their best use and design (Moen et al., 2016 ; Wasserstein and Lazar, 2016 ), and the inappropriate reliance on p -values (Wasserstein and Lazar, 2016 )]. I do not expand on these issues in my summary of group design because such critiques need not apply to all between-group comparisons. The use and applicability of analysis techniques are separable from the experimental utility of group designs in general, which are not limited to inferential statistics. Group experiments can also be analyzed using alternative, less problematic statistical approaches such as the probability of replication statistic or P-rep (Killeen, 2015 ) and Bayesian approaches (Berry and Stangl, 2018 ). Well-considered statistical best practices for various forms of group analysis (e.g., Moen et al., 2016 ) can help a researcher to address limitations.

The conflation of statistical methods with group designs has also led to the misconception that single-subject designs cannot be analyzed statistically. Most scientists have less familiarity with statistical analyses appropriate for use in single-subject designs and the serially-dependent data sets that they produce. While pronounced effects uncovered in single-subject experiments can often be clearly detected using appropriate visual analysis, rigorous statistical methods applicable to single-subject designs are also available (e.g., Parker and Brossart, 2003 ; Scruggs and Mastropieri, 2013 ).

3. Single-subject design and the inductive process

The advantages highlighted above suggest not only compatibility between single-subject and group approaches, but a potential advantage conferred by an order of operations between methods. Early in the research process, inductive inference based on single-subject manipulations are ideal to generate likely and testable abstractions (Russell, 1962 ). Using single-subject approaches for this inductive phase requires fewer resources compared to fully powered group approaches and can be more rigorous than small-n group pilots. An effect can be isolated in one individual, then systematically replicated across relevant differences and contexts until it fails to replicate, at which time explanatory variables can be adjusted until replicated results are produced. The altered experiment can then be analyzed in comparison to previous experiments to form a more general understanding that can be tested in a new series of experiments. After sufficient systematic replication, hybrid and group designs can assess the extent to which inductively and contextually informed abstractions generalize across the widest relevant populations.

4. Precedent of within-subject methods

Although within-subject group experiments are common in human neuroscience and psychology, e.g., Greenwald ( 1976 ) and Crockett and Fehr ( 2014 ), full-fledged single-subject designs are virtually unknown in many subfields. Still, high-impact neuroscience experiments have occasionally either implicitly or deliberately implemented within-subject reversals, demonstrating the power of these approaches to advance the science. To name just a few high-impact examples, Hodgkin et al. ( 1952 ) classic work on voltage clamping utilizing the giant squid neuron involved multiple parametric IV implementations on single neurons. The discovery of circadian rhythms in humans also involved systematic single-subject experiments comparing circadian patterns at various light intensities, light-dark schedules, and control contexts, which allowed investigators to establish that outside entrainment overrode the cycle-altering effects of different light intensities (Aschoff, 1965 ). This fruitful precedent of single-subject-like experiments at the very foundation of historical neuroscience together with the well-established efficacy of single-subject design in other fields imply that the wider adoption of the full methodology can succeed.

5. Single-subject design and individuality in neuroscience

As suggested earlier in this paper, individual variation dominates the scene in behavioral and brain sciences and constitutes a basic part of the evolutionary selection processes that shaped them. In human neuroscience, individual developmental and experience-dependent variation are of particular importance. Human brains are so individuated that functional units across individuals cannot be discerned via typical anatomical landmarks, and even between-group designs often need to utilize individuated or normalized measures (Brett et al., 2002 ; Dworetsky et al., 2021 ; Fedorenko, 2021 ; Hanson, 2022 ). A shift toward including rigorous single-subject research therefore holds particular promise for the field. For example, systematically replicated individual analyses of functional brain networks and their dynamics may more easily lead to generalizable ideas about how they develop and change, and these purportedly general processes could in turn be tested across individual contexts.

6. Time and resource logistics

Group methodology often requires great time and resources in order to produce properly powered experiments. This can lead to problems with rigor, particularly in contexts of limited funding and publish-or-perish job demands (Bernard, 2016 ; Button, 2016 ). Especially in early stages of research, single-subject methodology enables experimenters to investigate effects more critically and rigorously for each subject, to more quickly answer and refine questions in individuals first before systematically exploring the generality of findings or the importance of context, and to do so in a cost-effective way. Thus, both cost and rigor could be served by conscientiously adding single-subject methodology to the neuroscience toolbelt.

7. Suggestions for neuroscience subfields that could benefit

Cognitive, behavioral, social, and developmental neuroscience each deal with individual variation in which later stages are often dependent on earlier stages and seek to identify generalizable processes that produce variant outcomes: a task for which the single-subject and multi-method approach is ideal. Neurology and clinical neuroscience also stand to benefit from a more rigorous tool for investigating clinical cases or rare phenomena. While I do not mean to suggest that the method's utility should be limited to these subfields, the potential benefit seems particularly pronounced.

8. Discussion

In summary, greater utilization of single-subject research in human neuroscience can complement current methods by balancing the progression toward internal and then external validity and enabling a low-cost and flexible inductive process that can strengthen subsequent between-group studies. These methods have already been incidentally utilized in important neuroscience research, and they could be an even more powerful, thorough, cost-efficient, rigorous, and deliberate ingredient of an ideal approach to studying the generalizable processes that account for the highly individuated human brain and the behavior that it enables.

Author contributions

AB conceived of and wrote this manuscript.

Acknowledgments

The author would like to thank Daniele Ortu, Ph.D. for helpful comments.

Funding Statement

AB was funded by the Beatrice H. Barrett endowment for research on neuro-operant relations.

1 This discussion intentionally excludes assignment to groups based on non-manipulable variables because of the qualitative difference between correlational approaches and true experimental approaches that manipulates the IV. The former carries a very different set of considerations outside the scope of this paper.

2 If the biological process under investigation actually occurs at the population level (e.g. natural selection), the population parameter precisely applies to the question at hand. However, group comparisons are more often used to study processes that function on the individual level.

Conflict of interest

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Alnahdi G. H. (2015). Single-subject designs in special education: advantages and limitations. J. Res. Special Educ. Needs 15, 257–265. 10.1111/1471-3802.12039 [ DOI ] [ Google Scholar ]
Aschoff J. (1965). Circadian rhythms in man: a self-sustained oscillator with an inherent frequency underlies human 24-hour periodicity. Science 148, 1427–1432. 10.1126/science.148.3676.1427 [ DOI ] [ PubMed ] [ Google Scholar ]
Bernard C. (2016). Scientific rigor or rigor mortis? Eneuro 3, 5–48. 10.1523/ENEURO.0176-16.2016 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
Berry D. A., Stangl D. (2018). Bayesian Biostatistics. Boca Raton, FL: CRC Press. [ Google Scholar ]
Branch M. (2014). Malignant side effects of null-hypothesis significance testing. Theory Psychol. 24, 256–277. 10.1177/0959354314525282 [ DOI ] [ Google Scholar ]
Brett M., Johnsrude I. S., Owen A. M. (2002). The problem of functional localization in the human brain. Nat. Rev. Neurosci. 3, 243–249. 10.1038/nrn756 [ DOI ] [ PubMed ] [ Google Scholar ]
Button K. S. (2016). Statistical rigor and the perils of chance. Eneuro 3, e30. 10.1523/ENEURO.0030-16.2016 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
Button K. S., Ioannidis J. P., Mokrysz C., Nosek B. A., Flint J., Robinson E. S., et al. (2013). Power failure: why small sample size undermines the reliability of neuroscience. Nat. Rev. Neurosci. 14, 365–376. 10.1038/nrn3475 [ DOI ] [ PubMed ] [ Google Scholar ]
Byiers B. J., Reichle J., Symons F. J. (2012). Single-subject experimental design for evidence-based practice. Am. J. Speech Lang. Pathol. 21, 397–414. 10.1044/1058-0360(2012/11-0036) [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
Crockett M. J., Fehr E. (2014). Social brains on drugs: tools for neuromodulation in social neuroscience. Soc. Cogn. Affect. Neurosci. 9, 250–254. 10.1093/scan/nst113 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
Dworetsky A., Seitzman B. A., Adeyemo B., Nielsen A. N., Hatoum A. S., Smith D. M., et al. (2021). Two common and distinct forms of variation in human functional brain networks. bioRxiv 2021.2009. 2017.460799. 10.1101/2021.09.17.460799 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
Edgington E. S. (1996). Randomized single-subject experimental designs. Behav. Res. Ther. 34, 567–574. 10.1016/0005-7967(96)00012-5 [ DOI ] [ PubMed ] [ Google Scholar ]
Fedorenko E. (2021). The early origins and the growing popularity of the individual-subject analytic approach in human neuroscience. Curr. Opin. Behav. Sci. 40, 105–112. 10.1016/j.cobeha.2021.02.023 [ DOI ] [ Google Scholar ]
Greenwald A. G. (1976). Within-subjects designs: To use or not to use? Psychol. Bull. 83, 314. 10.1037/0033-2909.83.2.314 [ DOI ] [ Google Scholar ]
Grossman J., Mackenzie F. J. (2005). The randomized controlled trial: gold standard, or merely standard? Perspect. Biol. Med. 48, 516–534. 10.1353/pbm.2005.0092 [ DOI ] [ PubMed ] [ Google Scholar ]
Hains A. H., Baer D. M. (1989). Interaction effects in multielement designs: Inevitable, desirable, and ignorable. J. Appl. Behav. Analy. 22, 57–69. 10.1901/jaba.1989.22-57 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
Hanson S. J. (2022). The failure of blobology: FMRI misinterpretation, maleficience and muddle. Front. Hum. Neurosci. 16, 205. 10.3389/fnhum.2022.870091 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
Henrich J., Heine S. J., Norenzayan A. (2010). The weirdest people in the world? Behav. Brain Sci. 33, 61–83. 10.1017/S0140525X0999152X [ DOI ] [ PubMed ] [ Google Scholar ]
Hodgkin A. L., Huxley A. F., Katz B. (1952). Measurement of current-voltage relations in the membrane of the giant axon of Loligo. J. Physiol. 116, 424–448. 10.1113/jphysiol.1952.sp004716 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
Holcombe A., Wolery M., Gast D. L. (1994). Comparative single-subject research: Description of designs and discussion of problems. Topics Early Childh. Special Educ. 14, 119–145. 10.1177/027112149401400111 [ DOI ] [ Google Scholar ]
Horner R. D., Baer D. M. (1978). Multiple-probe technique: a variation of the multiple baseline 1. J. Appl. Behav. Analy. 11, 189–196. 10.1901/jaba.1978.11-189 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
Hull D. L. (1988). Science as a Process: An Evolutionary Account of the Social and Conceptual Development of Science. Chicago: University of Chicago Press. 10.7208/chicago/9780226360492.001.0001 [ DOI ] [ Google Scholar ]
Iversen I. H. (2013). “Single-case research methods: an overview,” in APA handbook of behavior analysis: Methods and principles, ed. G. J. Madden (New York, NY: American Psychological Association; ). 10.1037/13937-001 [ DOI ] [ Google Scholar ]
Janosky J. E., Leininger S. L., Hoerger M. P., Libkuman T. M. (2009). Single Subject Designs in Biomedicine. Cham: Springer Science and Business Media. 10.1007/978-90-481-2444-2 [ DOI ] [ Google Scholar ]
Kazdin A. E. (1981). External validity and single-case experimentation: Issues and limitations (a response to JS Birnbrauer). Analy. Interv. Dev. Disab. 1, 133–143. 10.1016/0270-4684(81)90027-6 [ DOI ] [ Google Scholar ]
Kazdin A. E. (2011). “Additional design options,” in Single-Case Research Designs, ed. A. E. Kazdin (New York: Oxford Press; ), 227–256. [ Google Scholar ]
Kazdin A. E., Tuma A. H. (1982). Single-Case Research Designs. San Francisco: Jossey Bass. [ Google Scholar ]
Killeen P. R. (2015). P rep, the probability of replicating an effect. Encyclop. Clin. Psychol. 4, 2201–2208. 10.1002/9781118625392.wbecp030 [ DOI ] [ Google Scholar ]
Kratochwill T. R., Hitchcock J., Horner R. H., Levin J. R., Odom S., Rindskopf D., et al. (2010). Single-case designs technical documentation. What works clearinghouse. Technical paper. [ Google Scholar ]
Kravitz R. L., Duan N., Braslow J. (2004). Evidence-based medicine, heterogeneity of treatment effects, and the trouble with averages. Milbank Q. 82, 661–687. 10.1111/j.0887-378X.2004.00327.x [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
Kuhn T. S., Hacking I. (2012). The Structure of Scientific Revolutions. Chicago; London: The University of Chicago Press. 10.7208/chicago/9780226458144.001.0001 [ DOI ] [ Google Scholar ]
Lakatos I. (1978). The Methodology of Scientific Research Programmes. Cambridge; New York: Cambridge University Press. 10.1017/CBO9780511621123 [ DOI ] [ Google Scholar ]
Meldrum M. L. (2000). A brief history of the randomized controlled trial: From oranges and lemons to the gold standard. Hematol. Oncol. Clin. North Am. 14, 745–760. 10.1016/S0889-8588(05)70309-9 [ DOI ] [ PubMed ] [ Google Scholar ]
Miller C. J., Smith S. N., Pugatch M. (2020). Experimental and quasi-experimental designs in implementation research. Psychiat. Res. 283, 112452. 10.1016/j.psychres.2019.06.027 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
Moen E. L., Fricano-Kugler C. J., Luikart B. W., O'Malley A. J. (2016). Analyzing clustered data: why and how to account for multiple observations nested within a study participant? PLoS ONE 11, e0146721. 10.1371/journal.pone.0146721 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
Parker R. I., Brossart D. F. (2003). Evaluating single-case research data: A comparison of seven statistical methods. Behav. Ther. 34, 189–211. 10.1016/S0005-7894(03)80013-8 [ DOI ] [ Google Scholar ]
Perdices M., Tate R. L. (2009). Single-subject designs as a tool for evidence-based clinical practice: Are they unrecognised and undervalued? Neuropsychol. Rehabilit. 19, 904–927. 10.1080/09602010903040691 [ DOI ] [ PubMed ] [ Google Scholar ]
Perone M. (1991). Experimental design in the analysis of free-operant behavior. Techn. Behav. Neur. Sci. 6, 135–171. [ Google Scholar ]
Russell B. (1962). The Scientific Outlook. New York, NY: Norton. [ Google Scholar ]
Scruggs T. E., Mastropieri M. A. (2013). PND at 25: Past, present, and future trends in summarizing single-subject research. Remed. Special Educ. 34, 9–19. 10.1177/0741932512440730 [ DOI ] [ Google Scholar ]
Sidman M. (1960). Tactics of Scientific Research. New York: Basic Books. [ Google Scholar ]
Soto P. L. (2020). Single-case experimental designs for behavioral neuroscience. J. Exper. Analy. Behav. 114, 447–467. 10.1002/jeab.633 [ DOI ] [ PubMed ] [ Google Scholar ]
Tankersley M., McGoey K. E., Dalton D., Rumrill Jr P. D., Balan C. M. (2006). Single subject research methods in rehabilitation. Work 26, 85–92. [ PubMed ] [ Google Scholar ]
Ward-Horner J., Sturmey P. (2010). Component analyses using single-subject experimental designs: A review. J. Appl. Behav. Analy. 43, 685–704. 10.1901/jaba.2010.43-685 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
Wasserstein R. L., Lazar N. A. (2016). The ASA statement on p-values: context, process, and purpose. Am. Statist. 70, 129–133. 10.1080/00031305.2016.1154108 [ DOI ] [ Google Scholar ]
Williams B. A. (2010). Perils of evidence-based medicine. Perspect. Biol. Med. 53, 106–120. 10.1353/pbm.0.0132 [ DOI ] [ PubMed ] [ Google Scholar ]
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Chapter 10: Single-Subject Research

Single-Subject Research Designs

Learning Objectives

Describe the basic elements of a single-subject research design.
Design simple single-subject studies using reversal and multiple-baseline designs.
Explain how single-subject research designs address the issue of internal validity.
Interpret the results of simple single-subject studies based on the visual inspection of graphed data.

General Features of Single-Subject Designs

Before looking at any specific single-subject research designs, it will be helpful to consider some features that are common to most of them. Many of these features are illustrated in Figure 10.2, which shows the results of a generic single-subject study. First, the dependent variable (represented on the y -axis of the graph) is measured repeatedly over time (represented by the x -axis) at regular intervals. Second, the study is divided into distinct phases, and the participant is tested under one condition per phase. The conditions are often designated by capital letters: A, B, C, and so on. Thus Figure 10.2 represents a design in which the participant was tested first in one condition (A), then tested in another condition (B), and finally retested in the original condition (A). (This is called a reversal design and will be discussed in more detail shortly.)

A subject was tested under condition A, then condition B, then under condition A again.

Another important aspect of single-subject research is that the change from one condition to the next does not usually occur after a fixed amount of time or number of observations. Instead, it depends on the participant’s behaviour. Specifically, the researcher waits until the participant’s behaviour in one condition becomes fairly consistent from observation to observation before changing conditions. This is sometimes referred to as the steady state strategy (Sidman, 1960) [1] . The idea is that when the dependent variable has reached a steady state, then any change across conditions will be relatively easy to detect. Recall that we encountered this same principle when discussing experimental research more generally. The effect of an independent variable is easier to detect when the “noise” in the data is minimized.

Reversal Designs

The most basic single-subject research design is the reversal design , also called the ABA design . During the first phase, A, a baseline is established for the dependent variable. This is the level of responding before any treatment is introduced, and therefore the baseline phase is a kind of control condition. When steady state responding is reached, phase B begins as the researcher introduces the treatment. There may be a period of adjustment to the treatment during which the behaviour of interest becomes more variable and begins to increase or decrease. Again, the researcher waits until that dependent variable reaches a steady state so that it is clear whether and how much it has changed. Finally, the researcher removes the treatment and again waits until the dependent variable reaches a steady state. This basic reversal design can also be extended with the reintroduction of the treatment (ABAB), another return to baseline (ABABA), and so on.

The study by Hall and his colleagues was an ABAB reversal design. Figure 10.3 approximates the data for Robbie. The percentage of time he spent studying (the dependent variable) was low during the first baseline phase, increased during the first treatment phase until it leveled off, decreased during the second baseline phase, and again increased during the second treatment phase.

A graph showing the results of a study with an ABAB reversal design. Long description available.

Why is the reversal—the removal of the treatment—considered to be necessary in this type of design? Why use an ABA design, for example, rather than a simpler AB design? Notice that an AB design is essentially an interrupted time-series design applied to an individual participant. Recall that one problem with that design is that if the dependent variable changes after the treatment is introduced, it is not always clear that the treatment was responsible for the change. It is possible that something else changed at around the same time and that this extraneous variable is responsible for the change in the dependent variable. But if the dependent variable changes with the introduction of the treatment and then changes back with the removal of the treatment (assuming that the treatment does not create a permanent effect), it is much clearer that the treatment (and removal of the treatment) is the cause. In other words, the reversal greatly increases the internal validity of the study.

There are close relatives of the basic reversal design that allow for the evaluation of more than one treatment. In a multiple-treatment reversal design , a baseline phase is followed by separate phases in which different treatments are introduced. For example, a researcher might establish a baseline of studying behaviour for a disruptive student (A), then introduce a treatment involving positive attention from the teacher (B), and then switch to a treatment involving mild punishment for not studying (C). The participant could then be returned to a baseline phase before reintroducing each treatment—perhaps in the reverse order as a way of controlling for carryover effects. This particular multiple-treatment reversal design could also be referred to as an ABCACB design.

In an alternating treatments design , two or more treatments are alternated relatively quickly on a regular schedule. For example, positive attention for studying could be used one day and mild punishment for not studying the next, and so on. Or one treatment could be implemented in the morning and another in the afternoon. The alternating treatments design can be a quick and effective way of comparing treatments, but only when the treatments are fast acting.

Multiple-Baseline Designs

There are two potential problems with the reversal design—both of which have to do with the removal of the treatment. One is that if a treatment is working, it may be unethical to remove it. For example, if a treatment seemed to reduce the incidence of self-injury in a developmentally disabled child, it would be unethical to remove that treatment just to show that the incidence of self-injury increases. The second problem is that the dependent variable may not return to baseline when the treatment is removed. For example, when positive attention for studying is removed, a student might continue to study at an increased rate. This could mean that the positive attention had a lasting effect on the student’s studying, which of course would be good. But it could also mean that the positive attention was not really the cause of the increased studying in the first place. Perhaps something else happened at about the same time as the treatment—for example, the student’s parents might have started rewarding him for good grades.

One solution to these problems is to use a multiple-baseline design , which is represented in Figure 10.4. In one version of the design, a baseline is established for each of several participants, and the treatment is then introduced for each one. In essence, each participant is tested in an AB design. The key to this design is that the treatment is introduced at a different time for each participant. The idea is that if the dependent variable changes when the treatment is introduced for one participant, it might be a coincidence. But if the dependent variable changes when the treatment is introduced for multiple participants—especially when the treatment is introduced at different times for the different participants—then it is extremely unlikely to be a coincidence.

Three graphs depicting the results of a multiple-baseline study. Long description available.

As an example, consider a study by Scott Ross and Robert Horner (Ross & Horner, 2009) [2] . They were interested in how a school-wide bullying prevention program affected the bullying behaviour of particular problem students. At each of three different schools, the researchers studied two students who had regularly engaged in bullying. During the baseline phase, they observed the students for 10-minute periods each day during lunch recess and counted the number of aggressive behaviours they exhibited toward their peers. (The researchers used handheld computers to help record the data.) After 2 weeks, they implemented the program at one school. After 2 more weeks, they implemented it at the second school. And after 2 more weeks, they implemented it at the third school. They found that the number of aggressive behaviours exhibited by each student dropped shortly after the program was implemented at his or her school. Notice that if the researchers had only studied one school or if they had introduced the treatment at the same time at all three schools, then it would be unclear whether the reduction in aggressive behaviours was due to the bullying program or something else that happened at about the same time it was introduced (e.g., a holiday, a television program, a change in the weather). But with their multiple-baseline design, this kind of coincidence would have to happen three separate times—a very unlikely occurrence—to explain their results.

In another version of the multiple-baseline design, multiple baselines are established for the same participant but for different dependent variables, and the treatment is introduced at a different time for each dependent variable. Imagine, for example, a study on the effect of setting clear goals on the productivity of an office worker who has two primary tasks: making sales calls and writing reports. Baselines for both tasks could be established. For example, the researcher could measure the number of sales calls made and reports written by the worker each week for several weeks. Then the goal-setting treatment could be introduced for one of these tasks, and at a later time the same treatment could be introduced for the other task. The logic is the same as before. If productivity increases on one task after the treatment is introduced, it is unclear whether the treatment caused the increase. But if productivity increases on both tasks after the treatment is introduced—especially when the treatment is introduced at two different times—then it seems much clearer that the treatment was responsible.

In yet a third version of the multiple-baseline design, multiple baselines are established for the same participant but in different settings. For example, a baseline might be established for the amount of time a child spends reading during his free time at school and during his free time at home. Then a treatment such as positive attention might be introduced first at school and later at home. Again, if the dependent variable changes after the treatment is introduced in each setting, then this gives the researcher confidence that the treatment is, in fact, responsible for the change.

Data Analysis in Single-Subject Research

In addition to its focus on individual participants, single-subject research differs from group research in the way the data are typically analyzed. As we have seen throughout the book, group research involves combining data across participants. Group data are described using statistics such as means, standard deviations, Pearson’s r , and so on to detect general patterns. Finally, inferential statistics are used to help decide whether the result for the sample is likely to generalize to the population. Single-subject research, by contrast, relies heavily on a very different approach called visual inspection . This means plotting individual participants’ data as shown throughout this chapter, looking carefully at those data, and making judgments about whether and to what extent the independent variable had an effect on the dependent variable. Inferential statistics are typically not used.

In visually inspecting their data, single-subject researchers take several factors into account. One of them is changes in the level of the dependent variable from condition to condition. If the dependent variable is much higher or much lower in one condition than another, this suggests that the treatment had an effect. A second factor is trend , which refers to gradual increases or decreases in the dependent variable across observations. If the dependent variable begins increasing or decreasing with a change in conditions, then again this suggests that the treatment had an effect. It can be especially telling when a trend changes directions—for example, when an unwanted behaviour is increasing during baseline but then begins to decrease with the introduction of the treatment. A third factor is latency , which is the time it takes for the dependent variable to begin changing after a change in conditions. In general, if a change in the dependent variable begins shortly after a change in conditions, this suggests that the treatment was responsible.

In the top panel of Figure 10.5, there are fairly obvious changes in the level and trend of the dependent variable from condition to condition. Furthermore, the latencies of these changes are short; the change happens immediately. This pattern of results strongly suggests that the treatment was responsible for the changes in the dependent variable. In the bottom panel of Figure 10.5, however, the changes in level are fairly small. And although there appears to be an increasing trend in the treatment condition, it looks as though it might be a continuation of a trend that had already begun during baseline. This pattern of results strongly suggests that the treatment was not responsible for any changes in the dependent variable—at least not to the extent that single-subject researchers typically hope to see.

Results of a single-subject study showing level, trend and latency. Long description available.

The results of single-subject research can also be analyzed using statistical procedures—and this is becoming more common. There are many different approaches, and single-subject researchers continue to debate which are the most useful. One approach parallels what is typically done in group research. The mean and standard deviation of each participant’s responses under each condition are computed and compared, and inferential statistical tests such as the t test or analysis of variance are applied (Fisch, 2001) [3] . (Note that averaging across participants is less common.) Another approach is to compute the percentage of nonoverlapping data (PND) for each participant (Scruggs & Mastropieri, 2001) [4] . This is the percentage of responses in the treatment condition that are more extreme than the most extreme response in a relevant control condition. In the study of Hall and his colleagues, for example, all measures of Robbie’s study time in the first treatment condition were greater than the highest measure in the first baseline, for a PND of 100%. The greater the percentage of nonoverlapping data, the stronger the treatment effect. Still, formal statistical approaches to data analysis in single-subject research are generally considered a supplement to visual inspection, not a replacement for it.

Key Takeaways

Single-subject research designs typically involve measuring the dependent variable repeatedly over time and changing conditions (e.g., from baseline to treatment) when the dependent variable has reached a steady state. This approach allows the researcher to see whether changes in the independent variable are causing changes in the dependent variable.
In a reversal design, the participant is tested in a baseline condition, then tested in a treatment condition, and then returned to baseline. If the dependent variable changes with the introduction of the treatment and then changes back with the return to baseline, this provides strong evidence of a treatment effect.
In a multiple-baseline design, baselines are established for different participants, different dependent variables, or different settings—and the treatment is introduced at a different time on each baseline. If the introduction of the treatment is followed by a change in the dependent variable on each baseline, this provides strong evidence of a treatment effect.
Single-subject researchers typically analyze their data by graphing them and making judgments about whether the independent variable is affecting the dependent variable based on level, trend, and latency.
Does positive attention from a parent increase a child’s toothbrushing behaviour?
Does self-testing while studying improve a student’s performance on weekly spelling tests?
Does regular exercise help relieve depression?
Practice: Create a graph that displays the hypothetical results for the study you designed in Exercise 1. Write a paragraph in which you describe what the results show. Be sure to comment on level, trend, and latency.

Long Descriptions

Figure 10.3 long description: Line graph showing the results of a study with an ABAB reversal design. The dependent variable was low during first baseline phase; increased during the first treatment; decreased during the second baseline, but was still higher than during the first baseline; and was highest during the second treatment phase. [Return to Figure 10.3]

Figure 10.4 long description: Three line graphs showing the results of a generic multiple-baseline study, in which different baselines are established and treatment is introduced to participants at different times.

For Baseline 1, treatment is introduced one-quarter of the way into the study. The dependent variable ranges between 12 and 16 units during the baseline, but drops down to 10 units with treatment and mostly decreases until the end of the study, ranging between 4 and 10 units.

For Baseline 2, treatment is introduced halfway through the study. The dependent variable ranges between 10 and 15 units during the baseline, then has a sharp decrease to 7 units when treatment is introduced. However, the dependent variable increases to 12 units soon after the drop and ranges between 8 and 10 units until the end of the study.

For Baseline 3, treatment is introduced three-quarters of the way into the study. The dependent variable ranges between 12 and 16 units for the most part during the baseline, with one drop down to 10 units. When treatment is introduced, the dependent variable drops down to 10 units and then ranges between 8 and 9 units until the end of the study. [Return to Figure 10.4]

Figure 10.5 long description: Two graphs showing the results of a generic single-subject study with an ABA design. In the first graph, under condition A, level is high and the trend is increasing. Under condition B, level is much lower than under condition A and the trend is decreasing. Under condition A again, level is about as high as the first time and the trend is increasing. For each change, latency is short, suggesting that the treatment is the reason for the change.

In the second graph, under condition A, level is relatively low and the trend is increasing. Under condition B, level is a little higher than during condition A and the trend is increasing slightly. Under condition A again, level is a little lower than during condition B and the trend is decreasing slightly. It is difficult to determine the latency of these changes, since each change is rather minute, which suggests that the treatment is ineffective. [Return to Figure 10.5]

Sidman, M. (1960). Tactics of scientific research: Evaluating experimental data in psychology . Boston, MA: Authors Cooperative. ↵
Ross, S. W., & Horner, R. H. (2009). Bully prevention in positive behaviour support. Journal of Applied Behaviour Analysis, 42 , 747–759. ↵
Fisch, G. S. (2001). Evaluating data from behavioural analysis: Visual inspection or statistical models. Behavioural Processes, 54 , 137–154. ↵
Scruggs, T. E., & Mastropieri, M. A. (2001). How to summarize single-participant research: Ideas and applications. Exceptionality, 9 , 227–244. ↵

The researcher waits until the participant’s behaviour in one condition becomes fairly consistent from observation to observation before changing conditions. This way, any change across conditions will be easy to detect.

A study method in which the researcher gathers data on a baseline state, introduces the treatment and continues observation until a steady state is reached, and finally removes the treatment and observes the participant until they return to a steady state.

The level of responding before any treatment is introduced and therefore acts as a kind of control condition.

A baseline phase is followed by separate phases in which different treatments are introduced.

Two or more treatments are alternated relatively quickly on a regular schedule.

A baseline is established for several participants and the treatment is then introduced to each participant at a different time.

The plotting of individual participants’ data, examining the data, and making judgements about whether and to what extent the independent variable had an effect on the dependent variable.

Whether the data is higher or lower based on a visual inspection of the data; a change in the level implies the treatment introduced had an effect.

The gradual increases or decreases in the dependent variable across observations.

The time it takes for the dependent variable to begin changing after a change in conditions.

The percentage of responses in the treatment condition that are more extreme than the most extreme response in a relevant control condition.

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Section A: Philosophical Underpinnings
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D-4: Describe the advantages of single subject experimental designs compared to group design ©

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Target terms (or phrases, in this case): single subject experimental designs, groups designs

Research is about asking and answering questions. It’s really important that the way we find an answer matches the question that was asked! That’s basically what research methods are all about. It’s probably not helpful or productive to think of group designs and single subject designs as diametrically opposed in any philosophical way. They are two different ways of answering different kinds of research questions. Some people do feel that there are important and foundational divisions between the underlying assumptions of group designs and those of single subject designs. (This can be complex! Focus on learning the basics first.) It’s important to note that “groups design” does not inherently refer to very large numbers of participants, nor does “single subject design” refer to studies that necessarily contain a single participant. The names refer to the level at which the analysis is conducted, not the absolute number of participants involved in the study. That being said, most single case studies do involve fewer participants than group designs.

Groups designs

Definition: Most people are familiar with research that involves two big groups of people. Researchers do something to group 1, and not group 2, and then they compare how both groups are doing (often by looking at an average of both groups) to see if the treatment made a difference. This is between subject research because the comparison is done (you guessed it) between different research subjects! (There is much more to between groups designs, of course. They are not simple. This is just a very, very basic overview of the main idea.)

Example in clinical context : Leonardo is in charge of assembling a team to evaluate the effectiveness of different reading programs on students who attend special education programs in major U.S. cities. The goal is to find out which program is most effective, so that the Department of Education can provide more targeted support to special education programs in urban areas. This research question is not about a particular individual. It is about which program is the best, on average, for urban special education students as a whole. This is a question that should be answered by assembling several very large groups of students, assigning them randomly to different programs, then comparing the dependent variables (in this case, reading scores) for each group. This design acknowledges that the results may not capture each individual’s experience. It is about what has the greatest impact on the dependent variable overall.

Why it matters : Between groups designs are not “bad,” nor do they yield inferior results when compared to within subjects designs. They simply provide answers to different questions, and those answers should be applied in different ways . It is important for behavior analysts to understand the basics of groups designs for several reasons, including the following: (1) most other fields, professionals, and stakeholders are more familiar with groups designs than with single subject methodology, and it is important to speak the same language when collaborating, (2) much of the broader conversation about “evidence based practices” in the helping professions are informed by groups research, and (3) a behavior analyst will probably encounter questions in their career that are best answered by consulting the between groups design literature.

Single subject experimental designs

Definition: Single subject experimental designs are different in several fundamental ways. The most obvious way is that comparisons are made within each subject. This kind of research design is therefore called within subjects research . The most obvious (and most experimentally weak) way to use an individual as their own control is to take data at baseline, then introduce an intervention and take data, and then compare the two sets of data to see if there is a difference. (See D-5 to explore more sophisticated ways to do this!)

Example in clinical context: DeShawn is in charge of selecting reading programming for special education students within an urban public school in the U.S. He knows that his students have different learning profiles and different learning contexts. DeShawn evaluates the relative benefit of multiple reading interventions at the individual level by conducting a multi-element design, and makes his decision based on how individual students respond to each intervention.

Why it matters: It is a foundational responsibility of behavior analysts to make decisions based on data and for the benefit of each individual client. Using single case design methodology helps us demonstrate a functional relationship between our intervention and the behavior change the client needs. At this point in human history, nothing can answer client specific questions regarding environmental interventions better than single subject methodologies. Pretty cool, right?

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Use of the single subject design for practice based primary care research

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The use of a single subject research design is proposed for practice based primary care research. An overview of the rationale of the design, an introduction to the methodology, strengths, limitations, a sample of recent literature citations, a working example, and possible clinical applications are presented.

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Selected References

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Balestra D. J., Balestra S. T., Wasson J. H. Ulcerative colitis and steroid-responsive, diffuse interstitial lung disease. A trial of N = 1. JAMA. 1988 Jul 1;260(1):62–64. [ PubMed ] [ Google Scholar ]
Guyatt G. H., Heyting A., Jaeschke R., Keller J., Adachi J. D., Roberts R. S. N of 1 randomized trials for investigating new drugs. Control Clin Trials. 1990 Apr;11(2):88–100. doi: 10.1016/0197-2456(90)90003-k. [ DOI ] [ PubMed ] [ Google Scholar ]
Guyatt G. H., Keller J. L., Jaeschke R., Rosenbloom D., Adachi J. D., Newhouse M. T. The n-of-1 randomized controlled trial: clinical usefulness. Our three-year experience. Ann Intern Med. 1990 Feb 15;112(4):293–299. doi: 10.7326/0003-4819-112-4-293. [ DOI ] [ PubMed ] [ Google Scholar ]
Guyatt G., Sackett D., Taylor D. W., Chong J., Roberts R., Pugsley S. Determining optimal therapy--randomized trials in individual patients. N Engl J Med. 1986 Apr 3;314(14):889–892. doi: 10.1056/NEJM198604033141406. [ DOI ] [ PubMed ] [ Google Scholar ]
Jaeschke R., Cook D., Sackett D. L. The potential role of single-patient randomized controlled trials (N-of-1 RCTs) in clinical practice. J Am Board Fam Pract. 1992 Mar-Apr;5(2):227–229. [ PubMed ] [ Google Scholar ]
Kearns K. P. Flexibility of single-subject experimental designs. Part II: Design selection and arrangement of experimental phases. J Speech Hear Disord. 1986 Aug;51(3):204–214. doi: 10.1044/jshd.5103.204. [ DOI ] [ PubMed ] [ Google Scholar ]
Kratochwill T., Alden K., Demuth D., Dawson D., Panicucci C., Arntson P., McMurray N., Hempstead J., Levin J. A further consideration in the application of an analysis-of-variance model for the intrasubject replication design. J Appl Behav Anal. 1974 Winter;7(4):629–633. doi: 10.1901/jaba.1974.7-629. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
Langer J. C., Winthrop A. L., Issenman R. M. The single-subject randomized trial. A useful clinical tool for assessing therapeutic efficacy in pediatric practice. Clin Pediatr (Phila) 1993 Nov;32(11):654–657. doi: 10.1177/000992289303201103. [ DOI ] [ PubMed ] [ Google Scholar ]
Larson E. B., Ellsworth A. J., Oas J. Randomized clinical trials in single patients during a 2-year period. JAMA. 1993 Dec 8;270(22):2708–2712. [ PubMed ] [ Google Scholar ]
Larson E. B. N-of-1 clinical trials. A technique for improving medical therapeutics. West J Med. 1990 Jan;152(1):52–56. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Lashner B. A., Hanauer S. B., Silverstein M. D. Testing nicotine gum for ulcerative colitis patients. Experience with single-patient trials. Dig Dis Sci. 1990 Jul;35(7):827–832. doi: 10.1007/BF01536795. [ DOI ] [ PubMed ] [ Google Scholar ]
McReynolds L. V., Thompson C. K. Flexibility of single-subject experimental designs. Part I: Review of the basics of single-subject designs. J Speech Hear Disord. 1986 Aug;51(3):194–203. doi: 10.1044/jshd.5103.194. [ DOI ] [ PubMed ] [ Google Scholar ]
Robin E. D., Burke C. M. Single-patient randomized clinical trial. Opiates for intractable dyspnea. Chest. 1986 Dec;90(6):888–892. doi: 10.1378/chest.90.6.888. [ DOI ] [ PubMed ] [ Google Scholar ]
Wagner J. L., Winett R. A. Prompting one low-fat, high-fiber selection in a fast-food restaurant. J Appl Behav Anal. 1988 Summer;21(2):179–185. doi: 10.1901/jaba.1988.21-179. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
Woolf G. M., Townsend M., Guyatt G. Treatment of cryptosporidiosis with spiramycin in AIDS. An "N of 1" trial. J Clin Gastroenterol. 1987 Dec;9(6):632–634. doi: 10.1097/00004836-198712000-00005. [ DOI ] [ PubMed ] [ Google Scholar ]
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Single Subject Research Design

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Gina Coffee Ph.D. 3

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Single-case research design ; Single-participant experimental design ; Time-series design

Single subject research design refers to a unique type of research methodology that facilitates intervention evaluation through an individual case.

Description

Single subject research design is a type of research methodology characterized by repeated assessment of a particular phenomenon (often a behavior) over time and is generally used to evaluate interventions [ 2 ]. Repeated measurement across time differentiates single subject research design from case studies and group designs, as it facilitates the examination of client change in response to an intervention. Although the use of single subject research design has generally been limited to research, it is also appropriate and useful in applied practice.

Single subject research designs differ in structure and purpose and typically fall into one of three categories: within-series designs, between-series designs and...

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Hayes, S. C., Barlow, D. H., & Nelson-Gray, R. O. (1999). The scientist practitioner: Research and accountability in the age of managed care (2nd ed.). Boston, MA: Allyn & Bacon.

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Herrera, G. C., & Kratochwill, T. R. (2005). Single-case experimental design. In S. W. Lee (Ed.), Encyclopedia of School Psychology (pp. 501–504). Thousand Oaks, CA: Sage Publications.

Kazdin, A. E. (1982). Single-case research designs: Methods for clinical and applied settings . New York, NY: Oxford Press University.

Kratochwill, T. R., & Levin, J. R. (1992). Single-case research design and analysis: New directions for psychology and education . Hillsdale, NJ: Lawrence Erlbaum Associates.

Kratochwill, T. R., Mott, S. E., & Dodson, C. L. (1984). Case study and single case research in clinical and applied psychology. In A. S. Bellack & M. Hersen (Eds.), Research methods in clinical psychology (pp. 55–99). New York, NY: Pergamon Press.

Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi- experimental designs for generalized causal inference . Boston, MA: Houghton Mifflin Company.

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Coffee, G. (2011). Single Subject Research Design. In: Goldstein, S., Naglieri, J.A. (eds) Encyclopedia of Child Behavior and Development. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-79061-9_2654

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10.1 Overview of Single-Subject Research

Learning objectives.

Explain what single-subject research is, including how it differs from other types of psychological research.
Explain what case studies are, including some of their strengths and weaknesses.
Explain who uses single-subject research and why.

What Is Single-Subject Research?

Single-subject research is a type of quantitative research that involves studying in detail the behavior of each of a small number of participants. Note that the term single-subject does not mean that only one participant is studied; it is more typical for there to be somewhere between two and 10 participants. (This is why single-subject research designs are sometimes called small- n designs, where n is the statistical symbol for the sample size.) Single-subject research can be contrasted with group research , which typically involves studying large numbers of participants and examining their behavior primarily in terms of group means, standard deviations, and so on. The majority of this book is devoted to understanding group research, which is the most common approach in psychology. But single-subject research is an important alternative, and it is the primary approach in some areas of psychology.

Before continuing, it is important to distinguish single-subject research from two other approaches, both of which involve studying in detail a small number of participants. One is qualitative research, which focuses on understanding people’s subjective experience by collecting relatively unstructured data (e.g., detailed interviews) and analyzing those data using narrative rather than quantitative techniques. Single-subject research, in contrast, focuses on understanding objective behavior through experimental manipulation and control, collecting highly structured data, and analyzing those data quantitatively.

It is also important to distinguish single-subject research from case studies. A case study is a detailed description of an individual, which can include both qualitative and quantitative analyses. (Case studies that include only qualitative analyses can be considered a type of qualitative research.) The history of psychology is filled with influential cases studies, such as Sigmund Freud’s description of “Anna O.” (see Note 10.5 “The Case of “Anna O.”” ) and John Watson and Rosalie Rayner’s description of Little Albert (Watson & Rayner, 1920), who learned to fear a white rat—along with other furry objects—when the researchers made a loud noise while he was playing with the rat. Case studies can be useful for suggesting new research questions and for illustrating general principles. They can also help researchers understand rare phenomena, such as the effects of damage to a specific part of the human brain. As a general rule, however, case studies cannot substitute for carefully designed group or single-subject research studies. One reason is that case studies usually do not allow researchers to determine whether specific events are causally related, or even related at all. For example, if a patient is described in a case study as having been sexually abused as a child and then as having developed an eating disorder as a teenager, there is no way to determine whether these two events had anything to do with each other. A second reason is that an individual case can always be unusual in some way and therefore be unrepresentative of people more generally. Thus case studies have serious problems with both internal and external validity.

The Case of “Anna O.”

Sigmund Freud used the case of a young woman he called “Anna O.” to illustrate many principles of his theory of psychoanalysis (Freud, 1961). (Her real name was Bertha Pappenheim, and she was an early feminist who went on to make important contributions to the field of social work.) Anna had come to Freud’s colleague Josef Breuer around 1880 with a variety of odd physical and psychological symptoms. One of them was that for several weeks she was unable to drink any fluids. According to Freud,

She would take up the glass of water that she longed for, but as soon as it touched her lips she would push it away like someone suffering from hydrophobia.…She lived only on fruit, such as melons, etc., so as to lessen her tormenting thirst (p. 9).

But according to Freud, a breakthrough came one day while Anna was under hypnosis.

[S]he grumbled about her English “lady-companion,” whom she did not care for, and went on to describe, with every sign of disgust, how she had once gone into this lady’s room and how her little dog—horrid creature!—had drunk out of a glass there. The patient had said nothing, as she had wanted to be polite. After giving further energetic expression to the anger she had held back, she asked for something to drink, drank a large quantity of water without any difficulty, and awoke from her hypnosis with the glass at her lips; and thereupon the disturbance vanished, never to return.

Freud’s interpretation was that Anna had repressed the memory of this incident along with the emotion that it triggered and that this was what had caused her inability to drink. Furthermore, her recollection of the incident, along with her expression of the emotion she had repressed, caused the symptom to go away.

As an illustration of Freud’s theory, the case study of Anna O. is quite effective. As evidence for the theory, however, it is essentially worthless. The description provides no way of knowing whether Anna had really repressed the memory of the dog drinking from the glass, whether this repression had caused her inability to drink, or whether recalling this “trauma” relieved the symptom. It is also unclear from this case study how typical or atypical Anna’s experience was.

Figure 10.2

“Anna O.” was the subject of a famous case study used by Freud to illustrate the principles of psychoanalysis.

Wikimedia Commons – public domain.

Assumptions of Single-Subject Research

Again, single-subject research involves studying a small number of participants and focusing intensively on the behavior of each one. But why take this approach instead of the group approach? There are several important assumptions underlying single-subject research, and it will help to consider them now.

First and foremost is the assumption that it is important to focus intensively on the behavior of individual participants. One reason for this is that group research can hide individual differences and generate results that do not represent the behavior of any individual. For example, a treatment that has a positive effect for half the people exposed to it but a negative effect for the other half would, on average, appear to have no effect at all. Single-subject research, however, would likely reveal these individual differences. A second reason to focus intensively on individuals is that sometimes it is the behavior of a particular individual that is primarily of interest. A school psychologist, for example, might be interested in changing the behavior of a particular disruptive student. Although previous published research (both single-subject and group research) is likely to provide some guidance on how to do this, conducting a study on this student would be more direct and probably more effective.

A second assumption of single-subject research is that it is important to discover causal relationships through the manipulation of an independent variable, the careful measurement of a dependent variable, and the control of extraneous variables. For this reason, single-subject research is often considered a type of experimental research with good internal validity. Recall, for example, that Hall and his colleagues measured their dependent variable (studying) many times—first under a no-treatment control condition, then under a treatment condition (positive teacher attention), and then again under the control condition. Because there was a clear increase in studying when the treatment was introduced, a decrease when it was removed, and an increase when it was reintroduced, there is little doubt that the treatment was the cause of the improvement.

A third assumption of single-subject research is that it is important to study strong and consistent effects that have biological or social importance. Applied researchers, in particular, are interested in treatments that have substantial effects on important behaviors and that can be implemented reliably in the real-world contexts in which they occur. This is sometimes referred to as social validity (Wolf, 1976). The study by Hall and his colleagues, for example, had good social validity because it showed strong and consistent effects of positive teacher attention on a behavior that is of obvious importance to teachers, parents, and students. Furthermore, the teachers found the treatment easy to implement, even in their often chaotic elementary school classrooms.

Who Uses Single-Subject Research?

Single-subject research has been around as long as the field of psychology itself. In the late 1800s, one of psychology’s founders, Wilhelm Wundt, studied sensation and consciousness by focusing intensively on each of a small number of research participants. Herman Ebbinghaus’s research on memory and Ivan Pavlov’s research on classical conditioning are other early examples, both of which are still described in almost every introductory psychology textbook.

In the middle of the 20th century, B. F. Skinner clarified many of the assumptions underlying single-subject research and refined many of its techniques (Skinner, 1938). He and other researchers then used it to describe how rewards, punishments, and other external factors affect behavior over time. This work was carried out primarily using nonhuman subjects—mostly rats and pigeons. This approach, which Skinner called the experimental analysis of behavior —remains an important subfield of psychology and continues to rely almost exclusively on single-subject research. For excellent examples of this work, look at any issue of the Journal of the Experimental Analysis of Behavior . By the 1960s, many researchers were interested in using this approach to conduct applied research primarily with humans—a subfield now called applied behavior analysis (Baer, Wolf, & Risley, 1968). Applied behavior analysis plays an especially important role in contemporary research on developmental disabilities, education, organizational behavior, and health, among many other areas. Excellent examples of this work (including the study by Hall and his colleagues) can be found in the Journal of Applied Behavior Analysis .

Although most contemporary single-subject research is conducted from the behavioral perspective, it can in principle be used to address questions framed in terms of any theoretical perspective. For example, a studying technique based on cognitive principles of learning and memory could be evaluated by testing it on individual high school students using the single-subject approach. The single-subject approach can also be used by clinicians who take any theoretical perspective—behavioral, cognitive, psychodynamic, or humanistic—to study processes of therapeutic change with individual clients and to document their clients’ improvement (Kazdin, 1982).

Key Takeaways

Single-subject research—which involves testing a small number of participants and focusing intensively on the behavior of each individual—is an important alternative to group research in psychology.
Single-subject studies must be distinguished from case studies, in which an individual case is described in detail. Case studies can be useful for generating new research questions, for studying rare phenomena, and for illustrating general principles. However, they cannot substitute for carefully controlled experimental or correlational studies because they are low in internal and external validity.
Single-subject research has been around since the beginning of the field of psychology. Today it is most strongly associated with the behavioral theoretical perspective, but it can in principle be used to study behavior from any perspective.
Practice: Find and read a published article in psychology that reports new single-subject research. (A good source of articles published in the Journal of Applied Behavior Analysis can be found at http://seab.envmed.rochester.edu/jaba/jabaMostPop-2011.html .) Write a short summary of the study.

Practice: Find and read a published case study in psychology. (Use case study as a key term in a PsycINFO search.) Then do the following:

Describe one problem related to internal validity.
Describe one problem related to external validity.
Generate one hypothesis suggested by the case study that might be interesting to test in a systematic single-subject or group study.

Baer, D. M., Wolf, M. M., & Risley, T. R. (1968). Some current dimensions of applied behavior analysis. Journal of Applied Behavior Analysis , 1 , 91–97.

Freud, S. (1961). Five lectures on psycho-analysis . New York, NY: Norton.

Kazdin, A. E. (1982). Single-case research designs: Methods for clinical and applied settings . New York, NY: Oxford University Press.

Skinner, B. F. (1938). The behavior of organisms: An experimental analysis . New York, NY: Appleton-Century-Crofts.

Watson, J. B., & Rayner, R. (1920). Conditioned emotional reactions. Journal of Experimental Psychology , 3 , 1–14.

Wolf, M. (1976). Social validity: The case for subjective measurement or how applied behavior analysis is finding its heart. Journal of Applied Behavior Analysis, 11 , 203–214.

Research Methods in Psychology Copyright © 2016 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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The benefits of single-subject research designs and multi-methodological approaches for neuroscience research

1. Introduction

2. Complimentary research designs

2.1. Group designs

2.2. Single-subject designs

2.3. A note about statistical methods

3. Single-subject design and the inductive process

4. Precedent of within-subject methods

5. Single-subject design and individuality in neuroscience

6. Time and resource logistics

7. Suggestions for neuroscience subfields that could benefit

8. Discussion

Author contributions

Acknowledgments

Funding Statement

Conflict of interest

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10.1 Overview of Single-Subject Research

What Is Single-Subject Research?

The Case of “Anna O.”

Who Uses Single-Subject Research?

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