Meta-Analysis of Video Prompting to Teach Daily Living Skills to Individuals With Autism Spectrum Disorder

Abstract

We conducted a meta-analysis of the single-case research design data on the effects of video prompting (VP) on the acquisition of daily living skills (DLS) among individuals with autism spectrum disorder (ASD). An analysis of potential moderators was conducted, and these included VP implemented alone versus VP with additional response prompting or error correction procedures, the effects of VP across participants’ age range, and the effects of VP among participants with ASD versus those with ASD and intellectual disability. There were 54 participants across 17 studies meeting our inclusion criteria. The results from the included studies demonstrated a moderate effect size for VP on the acquisition of DLS among the targeted population. The analysis of potential moderators showed no significant differences. These results and implications for research and practice are discussed.

Keywords

autism exceptionality assistive technology technology perspectives video prompting

Individuals with autism spectrum disorder (ASD) experience significant deficits in the domains of social communication and repetitive and restricted behaviors and interests (American Psychiatric Association, 2013). Features of the disorder such as difficulty with attending behaviors; deficits in imitation skills; issues with expressive, receptive, and pragmatic language; and the presence of stimulus overselectivity can result in complications that individuals with ASD experience while learning skills (Gonzalez, Cassel, Durocher, & Lee, 2017). Among the skills that seem to be affected are those related to functional living and vocational, and a lack of such skills can negatively impact independence and quality of life (Carothers & Taylor, 2008).

Daily living skills (DLS) refer to behaviors that allow individuals to function as independently as possible in everyday activities such as hygiene, domestic, community, employment, and leisure (Bennett & Dukes, 2014). Indeed, Domire and Wolfe (2014) contended that such skills are prerequisites needed to enhance job opportunities and independent living for individuals with ASD. Fortunately, there has been a recent increase in research activity to identify evidence-based practices for teaching DLS to individuals with ASD (Bennett & Dukes, 2014).

One evidence-based practice that has gained attention in recent years for teaching DLS is video-based instruction (VBI; Banda, Dogoe, & Matuszny, 2011). This methodology includes several variations, with the most prominent being video modeling (VM) and video prompting (VP). During VM, a practitioner plays a video clip of an entire task being performed from beginning to end before the student has an opportunity to perform the skill. When using VP, however, the practitioner plays a video clip of one task step being completed before the student attempts that skill, and this sequence repeats until all task steps have been attempted or completed (Sigafoos et al., 2007).

Researchers have demonstrated the effectiveness of VP when teaching DLS to individuals with ASD. To date, three systematic reviews on VP have been conducted (Banda et al., 2011; Domire & Wolfe, 2014; Gardner & Wolfe, 2013). Banda, Dogoe, and Matuszny (2011) were among the first researchers to examine the literature on the effects of VP with individuals with ASD learning skills. Their review of the literature assessed the effects of VP across 18 studies that were inclusive of 68 participants with developmental disabilities. The majority of participants in the studies were adolescents or adults, and many individuals had a dual diagnosis of ASD and intellectual disability (ID). A significant finding from the Banda et al. study was that VP has frequently been included with different intervention components, such as antecedent-based response prompting and consequence-based error correction procedures. These strategies were used as adjunct interventions when VP, by itself, was not effective in improving participants’ skills. Nevertheless, Banda et al. reported that VP with and without response prompting or error correction improved the DLS of the majority of the participants reviewed. In 12 studies, Banda et al. reported that VP led to participants’ maintaining and generalizing the skills, two issues known to be problematic among learners with ASD (Gonzalez et al., 2017).

Following the review by Banda et al. (2011), Gardner and Wolfe (2013) and Domire and Wolfe (2014) conducted their systematic reviews of the VP literature and focused on participants with ASD learning DLS. Gardner and Wolfe examined the effects of VP among 38 participants with ASD across 13 studies, while Domire and Wolfe evaluated the effects of VP among 38 participants with ASD in 12 studies. These research teams also reported the inclusion of participants with comorbid diagnoses of ASD and ID. Additionally, Gardner and Wolfe and Domire and Wolfe reported that response prompting or error correction was part of the VP intervention package in some studies. Notwithstanding the inclusion of additional strategies, both research teams reported that VP demonstrated positive outcomes when teaching DLS to individuals with ASD. Moreover, both research teams indicated that VP helped participants maintain the skills over time as well as generalize the skills to other environments, and this finding is similar to Banda et al.

One feature of the Domire and Wolfe (2014) study, which differed from the other reviews, was their use of percent of nonoverlapping data (PND) to summarize the effects observed in each participant’s graph among the included studies. They found that seven studies had 100% PND for each participant indicating strong intervention effects. The remainder of the studies reported varying levels of PND that ranged from low to high overlap among the participants.

In a fourth study, Hong et al. (2016) conducted a meta-analysis of VM (that included VP studies) on teaching DLS to individuals with ASD, the majority of whom had a secondary diagnosis of ID. There were 66 participants with ASD across 23 studies meeting Hong et al.’s inclusion criteria. By using the Tau-U effect size (ES) nonoverlap index and the Kruskal–Wallis test to analyze 119 A–B phase contrasts statistically, Hong et al. found that the overall ES of VM (inclusive of VP) interventions was 0.83, 95% CI [0.79, 0.87]. This finding represents a moderate ES (Parker, Vannest, & Davis, 2011; Parker, Vannest, Davis, & Sauber, 2011). Additionally, Hong et al. did not find significant differences between potential moderators that included age, diagnosis, independent variables (i.e., formats of VM inclusive of VP), and dependent variables (i.e., various DLS).

These published reviews provided valuable insight regarding the effects of VP interventions but with some limitations. First, the reviews of Banda et al. (2011) and Gardner and Wolfe (2013) did not use quantitative techniques to examine the magnitude of change in DLS in response to VP intervention packages. Second, although Domire and Wolfe (2014) used PND to measure nonoverlap, they did not measure the overall ES of VP across all included studies. Moreover, some researchers have questioned the utility of using PND as an ES measure when analyzing studies (Parker, Vannest, & Davis, 2011). Third, upon close inspection of the Hong et al. (2016) data, it is clear that VM and VP studies were combined in the analysis. Indeed, there were nine such studies whereby VP was used rather than VM. Although VM and VP are both VBI strategies that share commonalities, there are essential differences in their application (Banda et al., 2011). Unlike using VM to teach individuals behavior chains, VP requires a short time period for attention and retention given that one step is viewed and completed before advancing to additional steps in the behavior chain. This feature seems to be appropriate for learners with ASD who could have attention difficulties (Travers, Klinger, & Klinger, 2011). Furthermore, Banda et al. indicated that VP could be more effective than VM for individuals with moderate to severe disabilities, and they also suggested that VP seemed to be more effective than VM when teaching lengthy behavior chains. Thus, there appear to be essential distinctions between VP and VM that researchers and practitioners should consider when deciding on specific VBI tactics when the goal is to teach skills comprised of behavior chains to individuals with ASD.

Given the distinction between VM and VP, the purpose of this meta-analysis was to use the Tau-U ES index to determine the magnitude of change of DLS in response to VP among individuals with ASD. Moreover, Banda et al. (2011), Gardner and Wolfe (2013), and Domire and Wolfe (2014) reported that VP had been implemented in isolation and in combination with other interventions. The data from these reviews also demonstrated that VP has frequently been used with adolescents and adults; younger participants have been less commonly included in VP studies. Finally, Banda et al. suggested that VP might be more appropriate for individuals with moderate to severe learning needs. Therefore, an additional purpose of this study was to compare the magnitude of change of DLS across potential moderators including VP with and without additional response prompting and error correction strategies, participants’ ages, and participants’ disabilities.

Method

Definitions

As part of this study, we compared the effects of VP alone to VP with additional response prompting or error correction procedures. VP alone was defined as VP where voice-over narration was either included or not included as part of the intervention package. This decision was made due to two reasons: (a) Researchers frequently include voice-over narration as part of VBI interventions (Mechling & Collins, 2012) and (b) recent studies that demonstrated VP with or without voice-over narration resulted in marginal differences on participants’ ability to learn and perform the skills being taught (e.g., Bennett, Gutierrez, & Honsberger, 2013; Gutierrez, Bennett, McDowell, Cramer, & Crocco, 2016). Moreover, VP alone could include reinforcement as part of the intervention package since reinforcement is needed for learning to occur (Cooper, Heron, & Heward, 2007).

VP with additional responses prompting or error correction included those studies whereby researchers added response prompts to increase the likelihood of participants responding correctly. These tactics included prompting and fading systems (e.g., least-to-most prompting, most-to-least prompting, graduated guidance, and time delay progressions) and isolated prompting strategies (e.g., physical, live modeling, gestural, and additional verbal prompts beyond any initial voice-over narration provided on the video recording) combined with the VP procedure. The prompts could be delivered as an antecedent condition or a consequence condition.

DLS were defined as domestic and personal care skills (e.g., laundry skills), shopping skills (e.g., purchasing items), money skills (e.g., using an ATM), community skills (e.g., using public transportation), vocational skills (e.g., cleaning tables), functional academic skills (e.g., filling in job applications), and leisure/play skills, where the focus was learning to engage in the activity (e.g., playing card games) and unrelated to communication or social skills.

Inclusion and Exclusion Criteria

The inclusion criteria of this meta-analysis included: (a) The study was published in a peer-previewed journal in English between 1991 and 2017; (b) the study included at least one participant diagnosed with ASD; (c) the study used VP alone or VP plus response prompting or error correction as independent variables; (d) the study targeted DLS as dependent variables; (e) the study used a single case research design (SCRD); and (f) studies had to meet the Institute of Education Sciences (IES), What Works Clearinghouse (WWC), SCRD standards with or without reservations. Literature reviews and qualitative case studies were excluded from the current study. Moreover, group designs were excluded from the present study as ESs of SCRD should not be combined with those from group design research for analysis (Beretvas & Chung, 2008).

Literature Search

We conducted four systematic searches of the literature that included an examination of databases, conducting journal hand searches, reviewing previous systematic reviews on the topic, and conducting an ancestral search of articles meeting our inclusion criteria. The database search included (a) Education Resources Information Center, (b) EBSCOhost, (c) PsycINFO, and (d) PsycARTICLES. The following search terms were entered into each database: video prompt* or video model* or video instruction or video intervention or video-based instruction and autis* or ASD or autism spectrum disorder or developmental disability. Next, we conducted a journal hand search for articles published between August 2016 and August 2017 to identify possible studies not yet prorogated on the databases. The journals searched included (a) Journal of Autism and Developmental Disorders, (b) Education and Training in Autism and Developmental Disabilities, (c) Focus on Autism and Other Developmental Disabilities, (d) Journal of Special Education Technology, and (e) Research in Autism Spectrum Disorder. Then, we examined the reference lists of previous systematic reviews of the literature that were conducted by Banda et al. (2011), Gardner and Wolfe (2013), and Domire and Wolfe (2014) as these were recent reviews on the topic. Finally, the reference sections of each study that met the inclusion criteria were searched for additional studies.

Screening and Reliability of Inclusion Criteria (a)–(e)

This initial search yielded 955 articles. To screen for potential inclusion in this meta-analysis, both authors independently read the titles and abstracts of the articles from the database and hand searches as well as the titles from the reference sections of the systematic reviews and the articles meeting the inclusion criteria. Studies that indicated the use of any VBI application (e.g., VM, VP, and video self-modeling) in either the title or abstract were subjected to a full review. During the full review, both authors independently read 245 articles and applied the inclusion criteria (a)–(e). The full review resulted in 32 articles meeting the initial five inclusion criteria. Point-by-point interobserver agreement (IOA) was calculated on inclusion criteria (a)–(e) across all studies subjected to the full review. The IOA formula used was dividing the number of agreements by the number of agreements plus disagreements and multiplying by 100 (Cooper et al., 2007). Our initial IOA equaled 96.7% (range = 60–100%). We reached consensus to include or exclude articles following the initial IOA.

Evaluation of Research Designs and Reliability of Inclusion Criterion (f)

The authors independently examined the methodology of the remaining 32 studies using the WWC SCRD standards (Version 3.0), inclusive of the additional criteria recommended for analyzing studies that used the multiple probe design (MPD; IES, 2014). When applying the WWC SCRD standards, studies can either meet the design standards without reservations, meet the design standards with reservations, or not meet the design standards (IES, 2014). Those studies meeting the design standards with or without reservation were included in the study, while those not meeting the standards were excluded from the study. Note that in the current meta-analysis, we did not examine the strength of evidence of intervention effects to include or exclude studies as recommended by the WWC SCRD standards. Using such a standard for a meta-analysis could lead to a sampling bias. That is, excluding studies with no evidence of effects may lead to targeting only positive outcomes while ignoring less effective outcomes of the VP intervention, which may affect the overall ES estimation (Ledford, Wolery, & Gast, 2014; Mason, Davis, Boles, & Goodwyn, 2013).

First, we coded whether the appropriate SCRD was selected given the parameters of the study as recommended by the WWC (IES, 2014). Second, we applied the following standards when evaluating each study: (a) The intervention was systematically manipulated by the research team, (b) more than one observer collected data, (c) IOA data were collected for a minimum of 20% across conditions/phases of the study and equaled at least 80%, (d) there were an appropriate number of demonstrations of effect given the SCRD selected, and (e) there were an appropriate number of data points per condition/phase. Moreover, we applied the additional design standards when evaluating the MPD, including: (a) The initial baseline data must overlap across tiers of the design and (b) probes must be conducted immediately before introducing the independent variable (IES, 2014). Finally, we applied a modified standard to studies whereby researchers used the adapted alternating treatments design (AATD). The IES (2014) discusses “alternating treatments” generically although there are specific comparison designs that have slight differences among the guidelines for implementation (see Wolery, Gast, & Ledford, 2014). According to Wolery, Gast, and Ledford (2014), although a baseline condition can enhance the traditional alternating treatments design, it is not required. However, the AATD requires a precomparison baseline condition since the design allows researchers to examine differing treatment effects on nonreversible behaviors targeted for acquisition among participants (Wolery et al., 2014). Moreover, Wolery et al. stated that a minimum of three stable baseline data points were needed when researchers used the AATD to evaluate interventions. Thus, we required studies where the AATD was used to have a minimum of three baseline data points.

Point-by-point IOA was calculated for the application of the WWC SCRD standards across all analyzed studies. The IOA was calculated by dividing the number of agreements by the number of agreements plus disagreements and multiplying by 100 (Cooper et al., 2007). The IOA equaled 98.7% (range = 85.7–100%). Moreover, an additional IOA procedure was calculated on the decision to include or exclude an article in the final analysis using the same formula and that IOA equaled 90.6%. There were three disagreements and these were reconciled by consensus. After all inclusion criteria (a)–(f) were applied, 17 studies were accepted for analysis into the current investigation.

Extractions of Qualitative Data and Reliability

Six qualitative data variables were independently extracted by the two authors from the included studies. These variables included (a) participant demographics (i.e., number of participants with ASD, gender, age, and disability), (b) the setting(s) in which the studies occurred, (c) the VP intervention package details (i.e., use of VP alone or VP plus response prompting or error correction), (d) the DLS targeted, (e) the SCRD used, and (f) the number of extracted A–B phase contrasts. Six of the seventeen studies (35.3%) were coded for IOA. Point-by-point IOA was calculated by dividing agreements by agreements plus disagreements and multiplying by 100 (Cooper et al., 2007). The IOA equaled 100% for the qualitative data extraction.

Extraction of Quantitative Data and Reliability

Data from adjacent A–B phase contrasts were extracted and evaluated (Parker & Vannest, 2012) of those participants with ASD across the included studies. Targeting a specific population, such as individuals with ASD and omitting others in meta-analytic reviews, has been reported in the literature (i.e., Hong et al., 2016; Ninci et al., 2015). Note that a control behavior condition before an intervention condition in a comparative SCRD was considered an adjacent A and B phases contrast (control condition/probes to the best treatment condition), and thus, these data were extracted and analyzed. The extraction of data points was omitted in the following situations: (a) any reversal contrasts (B1 vs. A2; Parker & Vannest, 2012), (b) phases of participants without ASD, (c) intervention phases that did not implement VP alone or VP plus response prompting or error correction, and (d) generalization and maintenance phases/conditions as these data were infrequently reported in our sample. In total, 115 A–B phase contrasts were extracted from all studies.

The data points for each A–B phase contrast were extracted by applying the rank-order method (Ninci et al., 2015; Parker et al., 2011). Using this method, the data points in a graph are ranked based on their relative order across adjacent conditions/phases. The lowest data point across two adjacent conditions/phases was ranked number one, the second lowest data point was ranked number two, and so on, until the entire data series was ordered in relative rank. When two or more data points were at the same level on the graph, the same ranking was assigned.

Fifty of the 115 A–B phase contrasts (43.5%) were coded for IOA. Point-by-point IOA was calculated by dividing the number of agreements by the number of agreements plus disagreements and multiplying by 100 (Cooper et al., 2007). For the ranked ordered data extraction, IOA equaled 98.1% (range = 50–100%). Note that the one A–B phase contrast where IOA equaled 50% was a function of one disagreement early in the data series that resulted in disagreements in all subsequent data points for that A–B phase contrast only. Disagreements were resolved by the authors building consensus.

Potential Moderators

In addition to examining the overall effect of VP on teaching DLS to individuals with ASD, each extracted A–B phase contrast was classified based on three potential moderators: VP intervention type, participants’ ages, and participants’ disabilities. The VP intervention type was classified into two groups: (a) VP alone (i.e., using VP without additional intervention practices other than voice-over narration and reinforcement) and (b) VP plus response prompting or error correction. Participants’ ages were classified into four groups: (a) early childhood (aged 1–5 years), (b) elementary (aged 6–12 years), (c) secondary (aged 13–17 years), and (d) adult (aged 18 years and older). Participants’ disabilities were classified into two groups: (a) ASD and (b) ASD plus ID.

Tau-U ES Calculation and Analysis

We used the Tau-U nonoverlap index to evaluate the omnibus ES of VP to teaching DLS to individuals with ASD and to compare the ESs of the potential moderators of VP intervention type, age, and disability (Parker, Vannest, Davis, et al., 2011). Tau-U is a nonparametric statistical index that measures nonoverlap among data points between A and B phases with the possibility to control for baseline phase trends (Parker, Vannest, Davis, et al., 2011). Tau-U with baseline trend control was selected because it demonstrates strong statistical power compared to other nonoverlap indexes and provides a conservative analysis by measuring A–B phase nonoverlap data with the option of controlling for positive baseline trends in any pattern (Parker, Vannest, Davis, et al., 2011).

Ranked order data points for each adjacent A–B phase contrast (n = 115) were entered into the Web-based Tau-U calculator resulting in Tau-U, SE _Tau , and confidence interval (CI) values for each contrast (Vannest, Parker, Gonen, & Adiguzel, 2016). When the A phase showed a positive trend (i.e., Tau-U was greater than or equal to 0.1), the A–B phase contrast was corrected to control for this trend (Camargo et al., 2016).

Next, we entered the Tau-U and SE _Tau values of each phase contrast into the WinPEPI (Version 11.6) meta-analysis software (Abramson, 2011) to obtain an omnibus ES for using VP to teach DLS to individuals with ASD. A fixed effect model was used to weight all Tau-U and SE _Tau values of A–B phase contrasts to estimate the omnibus ES, SE, and CIs, which was done automatically by the software (Bowman-Perrott, Burke, Zaini, Zhang, & Vannest, 2016; Parker et al., 2011). For analyzing the omnibus ES statistically, we selected a CI of 95%. A value within the range of 1–0.93 indicated a large ES, 0.92–0.63 indicated a moderate ES, and 0.62–0 indicated a small ES (Parker et al., 2011; Parker, Vannest, Davis, et al., 2011).

In addition, we grouped all A–B phase contrasts based on potential moderators and calculated the ES for each group. For example, A–B phases coded as VP alone were grouped together and A–B phases coded as VP plus response prompting or error correction were grouped together. The Tau-U and SE _Tau values of each A–B phase in each group were entered into WinPEPI as two independent samples to calculate an ES and SE for each group. This procedure was completed for each potential moderator comparison.

Significance Test of Potential Moderators

The potential moderators of VP intervention type (i.e., VP alone vs. VP plus response prompting or error correction), participants’ ages (i.e., aged 1–5 years, 6–12 years, 13–17 years, and 18 years and older), and disability category (i.e., ASD vs. ASD plus ID) were analyzed for statistical significance using CI hypothesis testing (Payton, Greenstone, & Schenker, 2003). For these potential moderator analyses, a CI of 83.4% was used as this gives an approximate α value at p = .05 (Payton et al., 2003). Payton, Greenstone, and Schenker (2003) argued against using higher CI (e.g., 90% or 95%) for such significance tests as these provide restricted analyses that could miss subtle but significant differences among variables being examined. If the lower limit and upper limit of 83.4% CIs of compared ES indexes in potential moderator groups do not overlap, the groups are significantly different at the level of p = .05 (Payton et al., 2003). Overlapping CI testing in meta-analytic reviews within SCRD has been documented in the peer-reviewed literature (Bowman-Perrott et al., 2016; Camargo et al., 2016).

In addition to analyzing the overlap of CIs, the Kruskal–Wallis test was used to examine differences among the potential moderators. The Kruskal–Wallis test is a nonparametric statistical procedure that compares at least two independent samples (Kruskal & Wallis, 1952), which was appropriate for synthesized data based on the nonparametric Tau-U ES. Significance testing between moderator groups was determined at the level of p = .05. This statistical procedure has also been used in SCRD meta-analyses to test potential moderator variables (Hong et al., 2016; Ninci et al., 2015).

Results

We identified 17 studies meeting the inclusion criteria to be evaluated in this meta-analysis. Across these studies, there were 54 participants and 115 A–B phase contrasts. First, we provide a qualitative analysis, and this is followed by a quantitative analysis of the data.

Qualitative Data

We evaluated the qualitative data on participants’ demographics including gender, age, and disability (see Table 1). There was an equal amount of secondary-aged students and adults, which outnumbered elementary-aged students and students in the early childhood category. Additionally, the majority of the participants had ASD plus ID.

Table 1.

Participant Characteristics.

Variables	n	%
Gender
Male	46	85.2
Female	8	14.8
Age/grade
Early childhood	2	3.7
Elementary	10	18.5
Secondary	21	38.9
Adult	21	38.9
Disability
ASD without ID	21	38.9
ASD plus ID	33	61.1

Note. ASD = autism spectrum disorder; ID = intellectual disability.

We also examined the features of the studies, and these included the research sites, targeted DLS, VP intervention type, and the SCRDs used by researchers (see Table 2). Most of the research was conducted at school sites; this was followed by vocational centers and university settings. Specific areas within these general sites included classrooms, kitchens, simulated living rooms, workrooms, home economic rooms, dining rooms, lobbies, laundry rooms, cafeterias, therapy/vocational rooms, and an indoor swimming pool. Additionally, the DLS of washing and laundry was targeted the most by researchers with the remaining skills receiving nearly equal attention among the accepted studies. Furthermore, the majority of studies focused on VP alone versus VP with response prompting or error correction. Lastly, researchers used a variety of SCRD among the included studies; the AATD and the MPD accounted for the majority of the designs.

Table 2.

Features of the Studies.

Variables	n	%
Research sites
Schools	11	64.7
Vocational centers	4	23.5
University	2	11.8
Daily living skills targeted
Washing/laundry	6	35.3
Cooking	3	17.6
Putting away items, cleaning, folding, setting table	3	17.6
Play/leisure	3	17.6
Office/vocational	2	11.8
Intervention type
Video prompting alone	12	70.6
Video prompting plus response prompting/error correction	5	29.4
Research designs
Adapted alternating treatments design	5	29.4
Multiple probe design	5	29.4
Multiple baseline design	3	17.6
Combination designs	4	23.5

Quantitative Data

We analyzed the omnibus ES of using VP to teach DLS to individuals with ASD. Next, we conducted analyses on the aggregate ESs based on VP intervention type, participants’ ages, and participants’ disabilities. Figure 1 contains a forest plot with the number of participants per potential moderator, the number of A–B phase contrasts analyzed per potential moderator, Tau-U values, SE, CI, and the Kruskal–Wallis test results (see Figure 1).

Figure 1.

Forest plot of omnibus Tau-U and potential moderator analysis. ASD = autism spectrum disorder; CI = confidence interval; closed circles = lower limit, upper limit; closed squares = Tau-U value; EC = error correction; ID = intellectual disability; RP = response prompting; SE = standard error; VP = video prompting. *Two participants used both VP alone and VP plus response prompting/error correction in different tasks. **The lower limit and upper limit values are based on 95% CI.

Omnibus ES

The omnibus Tau-U was 0.92 (SE = 0.03, 95% CI [0.86, 0.99]). This finding represents a moderate omnibus ES of using VP to teach DLS to individuals with ASD (see Figure 1). A total of 79.1% of A–B phase contrasts fell within the range of a large ES, 14.8% of A–B phase contrasts were within the range of a moderate ES, and 6.1% of A–B phase contrasts were in the small ES range.

VP intervention type

The Tau-U value of the VP alone group indicated a moderate ES (ES = 0.92, SE = 0.04, 83.4% CI [0.86, 0.97]), while the Tau-U value of the VP plus response prompting or error correction group showed a large ES (ES = 0.93, SE = 0.07, 83.4% CI [0.83, 1]). CI testing at 83.4% showed overlap between these variables; therefore, there were no significant differences between the groups (Payton et al., 2003). Additionally, the Kruskal–Wallis test showed no significant differences across the two groups (p = 0.724; see Figure 1).

Participants’ ages

The Tau-U value of the early childhood group was within the range of a moderate ES (ES = 0.85, SE = 0.19, 83.4% CI [0.59, 1]). For the elementary group, the Tau-U value fell within the range of a large ES (ES = 0.97, SE = 0.09, 83.4% CI [0.84, 1]). When looking at the secondary group, the Tau-U value was in the range of a moderate ES (ES = 0.91, SE = 0.05, 83.4% CI [0.84, 0.98]). Finally, the Tau-U value of the adult group was in the range of a large ES (ES = 0.93, SE = 0.06, 83.4% CI [0.85, 1]). An examination of CI at 83.4% for significance testing showed overlap across each age-group, and thus, there were no significant differences among these groups of participants (Payton et al., 2003). Moreover, the Kruskal–Wallis test revealed no significant differences across all age groups (p = 0.051; see Figure 1).

Participants' disabilities

The Tau-U value of the ASD group was within the range of a large ES (ES = 0.94, SE = 0.05, 83.4% CI [0.87, 1]), and the Tau-U value of the ASD plus ID group was within the range of a moderate ES (ES = 0.91, SE = 0.05, 83.4% CI [0.84, 0.98]). CI analysis at 83.4% revealed an overlap between the two groups indicating no significant differences (Payton et al., 2003). Additionally, the Kruskal–Wallis test revealed no significant differences between the groups (p = 0.056; see Figure 1).

Discussion

The purpose of this study was to conduct a meta-analysis of the SCRD data on using VP to teach DLS to individuals with ASD. An additional objective was to analyze the moderators of VP intervention type, participants’ ages, and participants’ disabilities. Our findings revealed a moderate omnibus Tau-U score in the upper range of that category, demonstrating that VP is an effective strategy for teaching DLS to students with ASD. This finding is supported by, and extends, the past systematic reviews of the literature conducted by Banda et al. (2011), Gardner and Wolfe (2013), and Domire and Wolfe (2014). An analysis of the potential moderators showed Tau-U values that ranged from a moderate to a large ES, with most scores falling within the large ES range. Moreover, there were no significant differences among the moderators within each category indicating that VP is an effective intervention across these variables.

The current results differ from those reported in a recent meta-analysis by Hong et al. (2016) on the effects of VM (inclusive of VP) on teaching DLS skills to individuals with ASD. Our overall Tau-U value was higher than that reported by Hong et al., and our moderator Tau-U values were higher within each age-group and among participants with ASD with and without a comorbid ID. In two instances, the early childhood and the secondary-aged group of participants, our Tau-U scores nearly doubled those reported in the Hong et al. meta-analysis. These differences suggest the possibility that their findings might have underestimated the effects of VP by combining this methodology with VM. Indeed, VM and VP are distinct strategies that should be differentially applied depending on the parameters in which the procedures are used (e.g., participant severity of disability and length of the behavior chain targeted; Banda et al., 2011; Cannella-Malone et al., 2011).

Implications

The findings from the current study suggest that VP is a viable strategy for teaching DLS to specific individuals with ASD. Our data indicated that VP is an effective intervention when used with secondary-aged and adult students. Thus, teachers and related service providers should use this strategy with confidence when teaching these skills to this population of students.

The data also demonstrated that the strategy was effective for elementary-aged students. However, there were few participants in this age-group. Consequently, we recommend that practitioners use caution when using VP to teach DLS to students of this age-group until more data are published in the peer-reviewed literature.

Unfortunately, there is not enough evidence at this time to suggest the use of VP to teach DLS to young children with ASD (aged 5 years and younger). There were only two participants in the studies meeting our inclusion criteria. Although the Tau-U score for this age-group was within the moderate ES range, one of these individual’s score fell within the small ES category. Clearly, more evidence is needed among the early childhood and elementary-aged populations before researchers can make a stronger recommendation related to the utility of VP when used to teach DLS, and this finding is supported by the systematic reviews conducted by Banda et al. (2011), Gardner and Wolfe (2013), and Domire and Wolfe (2014).

In addition to the effects of VP on teaching DLS to learners among various age categories, our data also showed that VP was effective when teaching these skills to individuals with ASD with and without a secondary diagnosis of ID. Nonetheless, our findings showed that students with a comorbid diagnosis of ASD and ID might need additional response prompting or error correction. In those studies where supplementary procedures were required, participants tended to be adolescents or adults with the majority of these individuals having ASD with ID (e.g., Cannella-Malone, Wheaton, Wu, Tullis, & Park, 2012; Gardner & Wolfe, 2015; Goodson, Sigafoos, O’Reilly, Cannella, & Lancioni, 2007). Thus, educators and similar professionals should consider the use of additional response prompts or error correction strategies to help the students. However, to achieve independence, practitioners and caregivers must be aware of these added procedures and plan to fade these tactics so that learners can (a) emit the DLS without response prompting or error correction or (b) access the VP system to support their learning needs without adult support (Banda et al., 2011).

Limitations

Several limitations should be considered when interpreting the results of this meta-analysis. First, as with any systematic review and meta-analysis, it is possible that additional studies could have been identified but were not given our inclusion criteria. Second, we examined the peer-reviewed SCRD data and excluded theses and dissertations. This exclusion could increase the threat of publication bias in the current study. Third, we excluded some studies from the present analysis due to our modification made to the WWC SCRD standards related to the AATD. Specifically, for those studies where the AATD was used, we required a sufficient number of baseline data points based on other WWC SCRD baseline standards given that Wolery et al. (2014) called for baseline data points when researchers use the AATD. This modification required us to exclude some studies using comparative SCRDs. Nevertheless, researchers should consider this modification given the differences between the alternating treatments design and AATD parameters used to assess the internal validity of studies. Fourth, there were very few participants in the early childhood category. Thus, conclusions regarding this age-group are limited. Fifth, generalization and maintenance data were infrequently reported among the included studies. Therefore, we excluded these conditions from our analysis, which could limit our understanding of the generalized and long-term effects of VP on DLS acquisition.

Future Research

The results of this meta-analysis contributed to the literature in several meaningful ways; the findings also highlighted areas in need of additional research. Overall, more research is needed on the effects of VP on DLS instruction considering that some studies did not meet specific WWC SCRD standards. We encourage future researchers to consider these standards when designing and conducting studies on this topic.

In addition to increasing the overall volume of studies meeting research design standards, specific research topics related to VP and DLS instruction are needed. First, we suggest that researchers study the effects of VP on DLS acquisition with younger children. Additional meta-analyses should be conducted once enough data have been generated by these individual studies to examine the omnibus ES and potential moderators among this age-group. Next, researchers should study the degree of maintenance and the generalized effects of the VP intervention across age groups and DLS. There was a paucity of data on these effects in the current study. Furthermore, additional research is needed exploring the effects of VP on play/leisure and vocational skills, given these behavior sets were less frequently examined when compared to domestic skills. We also recommend that researchers investigate the effects of VP on DLS in authentic environments since most studies that met our inclusion criteria were conducted in contrived settings. Finally, we suggest that researchers examine other potential moderators not studied in this meta-analysis as this information can inform research and practice.

Conclusion

In this study, we analyzed the SCRD data on using VP to teach DLS to individuals with ASD. Overall, the tactic was moderately to highly effective when applied to teach DLS to this population. There were no significant differences among potential moderators of VP with or without additional response prompting or error correction, participants’ ages, or participants’ disabilities. Although further research is needed on the effects of VP on DLS acquisition—particularly with younger populations as well as in authentic environments—the quantitative data from this analysis add to the previous qualitative systematic reviews on the effectiveness of VP for teaching DLS to individuals with ASD.

Footnotes

Acknowledgment

The authors would like to thank Dr. Haiying Long for her assistance with statistical analyses.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

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