Ch. 10 Flashcards
Single-subject research
A type of quantitative research that involves studying in detail the behavior of each of a small number of participants.
focuses on understanding objective behavior through experimental manipulation and control, collecting highly structured data, and analyzing those data quantitatively.
group research
Research that involves studying large numbers of participants and examining their behavior primarily in terms of group means, standard deviations, and so on.
Assumptions of Single-Subject Research
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.
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 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.
A third assumption of single-subject research is that it is important to study strong and consistent effects that have biological or social importance.
social validity
Referred to as treatments that have substantial effects on important behaviors and that can be implemented reliably in the real-world contexts in which they occur.
experimental analysis of behavior
A subfield of psychology (behaviorism) that focuses exclusively on the effects of rewards, punishments, and other external factors on behavior.
applied behavior analysis
An application of the principles of experimental analysis of behavior that plays an important role in contemporary research on developmental disabilities, education, organizational behavior, and health, among many other applied areas.
General Features of Single-Subject Designs
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.
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 behavior.
Specifically, the researcher waits until the participant’s behavior in one condition becomes fairly consistent from observation to observation before changing conditions.
This is sometimes referred to as the steady state strategy
steady state strategy
When the researcher waits until the participant’s behavior in one condition becomes fairly consistent from observation to observation before changing conditions.
Reversal Design also called the ABA design.
The most basic single-subject research design in which the researcher measures the dependent variable in three phases: Baseline, before a treatment is introduced (A); after the treatment is introduced (B); and then a return to baseline after removing the treatment (A). It is often called an ABA design.
This basic reversal design can also be extended with the reintroduction of the treatment (ABAB), another return to baseline (ABABA), and so on.
baseline
The beginning phase of an ABA design which acts as a kind of control condition in which the level of responding before any treatment is introduced.
multiple-treatment reversal design
In this design the baseline phase is followed by separate phases in which different treatments are introduced.
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.
The second problem is that the dependent variable may not return to baseline when the treatment is removed.
One solution to these problems is to use a multiple-baseline design
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.
multiple-baseline design
In this design, multiple baselines are either established for one participant or one baseline is established for many participants.
There are three different types
Multiple-Baseline Design Across Participants
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 unlikely to be a coincidence.
Multiple-Baseline Design Across Behaviors
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.
Multiple-Baseline Design Across Settings
In yet a third version of the multiple-baseline design, multiple baselines are established for the same participant but in different settings.
Data Analysis in Single-Subject Research compared to group and inferential
group research involves combining data across participants. Group data are described using statistics such as means, standard deviations, correlation coefficients, 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.
visual inspection
This means plotting individual participants’ data, looking carefully at those plots, 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.
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.
percentage of non-overlapping data (PND)
This is the percentage of responses in the treatment condition that are more extreme than the most extreme response in a relevant control condition.
The greater the percentage of non-overlapping 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.
results of single-subject research can also be analyzed using statistical procedures 😟😟
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
Single-subject research is similar to group research—especially experimental group research
both quantitative approaches that try to establish causal relationships by manipulating an independent variable, measuring a dependent variable, and controlling extraneous variables.
But there are important differences between these approaches too.
It is worth addressing the most common points of disagreement between single-subject researchers and group researchers and how these disagreements can be resolved.
Data Analysis
disagreements revolves around the issue of data analysis for both group and single subject
One specific concern is that visual inspection is not sensitive enough to detect weak effects.
A second is that visual inspection can be unreliable, with different researchers reaching different conclusions about the same set of data.
A third is that the results of visual inspection—an overall judgment of whether or not a treatment was effective—cannot be clearly and efficiently summarized or compared across studies (unlike the measures of relationship strength typically used in group research).
Data analysis
Additional disagreements revolving around the issue of data analysis for single subject
argue that their use of the steady state strategy, combined with their focus on strong and consistent effects, minimizes most of them.
If the effect of a treatment is difficult to detect by visual inspection because the effect is weak or the data are noisy, then single-subject researchers look for ways to increase the strength of the effect or reduce the noise in the data by controlling extraneous variables.
If the effect is still difficult to detect, then they are likely to consider it neither strong enough nor consistent enough to be of further interest.
Many single-subject researchers also point out that statistical analysis is becoming increasingly common and that many of them are using this as a supplement to visual inspection—especially for the purpose of comparing results across studies
Data analysis
some advocates of single-subject research worry about the way that group researchers analyze their data.
Specifically, they point out that focusing on group means can be highly misleading.
Again, imagine that a treatment has a strong positive effect on half the people exposed to it and an equally strong negative effect on the other half.
In a traditional between-subjects experiment, the positive effect on half the participants in the treatment condition would be statistically cancelled out by the negative effect on the other half.
The mean for the treatment group would then be the same as the mean for the control group, making it seem as though the treatment had no effect when in fact it had a strong effect on every single participant!
External Validity
Group research researchers concerns about external validity of single-subject design
External validity—the ability to generalize the results of a study beyond the people and specific situation actually studied.
In particular, advocates of group research point out the difficulty in knowing whether results for just a few participants are likely to generalize to others in the population.
Single-subject researchers share this concern.
In response, they note that the strong and consistent effects they are typically interested in—even when observed in small samples—are likely to generalize to others in the population.
Single-subject researchers also note that they place a strong emphasis on replicating their research results.
When they observe an effect with a small sample of participants, they typically try to replicate it with another small sample—perhaps with a slightly different type of participant or under slightly different conditions.
Each time they observe similar results, they rightfully become more confident in the generality of those results.
External Validity
single-subject researchers concerns about external validity of Group research design
One extremely important point they make is that studying large groups of participants does not entirely solve the problem of generalizing to other individuals.
Another point that single-subject researchers make is that group researchers also face a similar problem when they study a single situation and then generalize their results to other situations.
Single-Subject and Group Research as Complementary Methods
Single-subject research is particularly good for testing the effectiveness of treatments on individuals when the focus is on strong, consistent, and biologically or socially important effects.
It is also especially useful when the behavior of particular individuals is of interest.
Clinicians who work with only one individual at a time may find that it is their only option for doing systematic quantitative research.
Group research, on the other hand, is ideal for testing the effectiveness of treatments at the group level.
Among the advantages of this approach is that it allows researchers to detect weak effects, which can be of interest for many reasons.
Group research is also good for studying interactions between treatments and participant characteristics.
Finally, it is important to understand that the single-subject and group approaches represent different research traditions.
This factor is probably the most important one affecting which approach a researcher uses.
Researchers in the experimental analysis of behavior and applied behavior analysis learn to conceptualize their research questions in ways that are amenable to the single-subject approach.
Researchers in most other areas of psychology learn to conceptualize their research questions in ways that are amenable to the group approach.
At the same time, there are many topics in psychology in which research from the two traditions have informed each other and been successfully integrated.
The Principle of Converging Evidence
An idea that tells us to examine the pattern of flaws running through the research literature because the nature of this pattern can either support or undermine the conclusions we wish to draw.
Converging evidence example explanation
Suppose the findings from a number of different studies were largely consistent in supporting a particular conclusion.
If all of the studies were flawed in a similar way, for example, if all of the studies were correlational and contained the third variable problem and the directionality problem, this would undermine confidence in the conclusions drawn because the consistency of the outcome may simply have resulted from a particular flaw that all of the studies shared.
On the other hand, if all of the studies were flawed in different ways and the weakness of some of the studies were the strength of others (the low external validity of a true experiment was balanced by the high external validity of a correlational study), then we could be more confident in our conclusions.
