Find an experimental research study on the topic chosen in Week One for your Final Research Proposal. You may choose to include an experimental study which was included in the literature review you us

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Find an experimental research study on the topic chosen in Week One for your Final Research Proposal. You may choose to include an experimental study which was included in the literature review you used in the Week One assignment by searching the reference list for experimental research studies on the topic. However, it is also acceptable to find and include an experimental research study on the topic that is not included in that literature review.

Identify the specific experimental research design used in the study. Summarize the main points of the experimental research study including information on the hypothesis, sampling strategy, research design, statistical analysis, results, and conclusion(s). Evaluate the published experimental research study focusing on and identifying the specific threats to validity that apply to the chosen study. Explain whether or not these threats were adequately addressed by the researchers. Describe how the researchers applied ethical principles in the research study.

The Research and Critique an Experimental Study

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  • Must be three to four double-spaced pages in length APA style
  • Separate title page with the following:

    • Title of paper
    • Student’s name
    • Course name and number
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    • Date submitted
  • Must use at least two peer-reviewed sources in addition to those required for this week.
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  • Must include a separate reference page that is formatted according to APA style

I included four references that I looked up additional to our topic included is Doc: 95,96,97,98 and out14. Also added on that was a resource this week for class which is ED99991 on experimental design. Additionally, I found the two articles you included in week one Doc 99, and 100. The one with stress index-short form I found is not the one you quoted but should work is Doc101.

Find an experimental research study on the topic chosen in Week One for your Final Research Proposal. You may choose to include an experimental study which was included in the literature review you us
BRIEF REPORT Psychometric Properties of the Parenting Stress Index-Short Form (PSI-SF) in a High-Risk Sample of Mothers and Their Infants Nicole E. Barroso, Gabriela M. Hungerford, Dainelys Garcia, Paulo A. Graziano, and Daniel M. Bagner Florida International University The goal of the present study was to evaluate the psychometric properties of the English and Spanish versions of the Parenting Stress Index-Short Form (PSI-SF) with mothers of 12- to 15-month-old infants with elevated levels of behavior problems and from predominately Hispanic, low-income backgrounds. Mothers of 58 infants were assessed as part of a larger study examining a brief home-based intervention for infants with elevated behavior problems. Internal consistency was good for all 3 subscales (i.e., Parental Distress, Parent-Child Dysfunctional Interaction, and Difficult Child) and the Total Stress scale. Convergent validity of subscales was supported by correlations with measures of theoretically related constructs, including maternal depressive symptoms, maternal parenting practices, and infant behavior. Furthermore, examination of the optimal clinical cutoff by examining sensitivity and specificity sug- gested that for this high-risk sample lower percentile scores (73rd–77th), relative to the published 85th percentile cutoff, were sufficient for identifying mothers with clinically elevated depressive symptoms and infants with clinically elevated behavioral and emotional difficulties. The current results provide psychometric support for the PSI-SF as an effective and appropriate measure for use with high-risk families that have been underrepresented in previous research, including mothers of very young children with behavior problems, Hispanic and Spanish-speaking populations, and low-income families. Keywords:assessment, parenting stress, at-risk, behavior problems, infancy Stress has been defined as coping with challenges (Lazarus, 2000) and shown to play a critical role in parenting, especially among clinical populations (Crnic, Gaze, & Hoffman, 2005). For example, research has demonstrated a transactional relation be- tween parenting stress and child behavior problems from 3 to 9 years (Neece, Green, & Baker, 2012). The transition from infancy to toddlerhood can be especially stressful as parents experience new and challenging child behaviors, and higher levels of parent- ing stress during infancy has been associated with a difficult infant temperament and higher levels of infant negative affectivity (Chang et al., 2004). In addition to the negative impact of early parenting stress on infant outcomes, levels of parenting stress are higher among fam- ilies from racial and ethnic minority backgrounds (Franco, Pottick, & Huang, 2010). Furthermore, higher levels of parenting stress among minority groups are largely because of higher poverty rates,especially for Hispanics who struggle with acculturation (Huston, McLoyd, & Coll, 1994). Given the higher levels of parenting stress among parents of children from minority and disadvantaged back- grounds and its negative impact in infancy, it is critical to have a reliable and valid measure of parenting stress with this population. One of the most common measures of parenting stress is the Parenting Stress Index (PSI), a 120-item parent-report question- naire. To reduce burden of the full-length PSI, a brief measure known as the PSI-Short Form (PSI-SF), which consists of 36 items from the full PSI, was developed. The newest full and short versions, the PSI-4 and PSI-SF-4, are highly correlated with the original PSI and PSI-SF (Abidin, 2012). The PSI-SF was devel- oped based on several exploratory factor analyses of the full PSI (Solis & Abidin, 1991) and is comprised of three subscales, each consisting of the items that best loaded together, which include the Parental Distress (PD), Parent–Child Dysfunctional Interaction (PCDI), and Difficult Child (DC) subscales, as well as a Total Stress scale. All three subscales and the Total Stress scale are highly correlated between the PSI-SF and PSI-SF-4 ranging from .97–.99 (Abidin, 2012). According to the manual, scores at or above the 85th percentile on the Total Stress scale are considered to be borderline clinically significant based on the norms of the full PSI (Abidin, 2012). However, the clinical utility of this cutoff has not been examined in the literature. Although the PSI-SF is com- monly used in a variety of settings with infants, such as hospital discharge (Thomas, Renaud, & Depaul, 2004), only five studies have examined the psychometric properties of the PSI-SF with mothers of infants and toddlers. Furthermore, no studies to our This article was published Online First November 23, 2015. Nicole E. Barroso, Gabriela M. Hungerford, Dainelys Garcia, Paulo A. Graziano, and Daniel M. Bagner, Department of Psychology, Florida International University. This work was supported by a career development award from the National Institute of Mental Health (K23MH085659) to Daniel M. Bagner. Correspondence concerning this article should be addressed to Nicole E. Barroso, Department of Psychology, Florida International University, 11200 SW 8th Street, DM Room 142A, Miami, FL 33199. E-mail: [email protected] This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. Psychological Assessment© 2015 American Psychological Association 2016, Vol. 28, No. 10, 1331–13351040-3590/16/$12.00http://dx.doi.org/10.1037/pas0000257 1331 knowledge have examined the psychometric properties of the PSI-SF in a high-risk sample of mothers of infants and toddlers with elevated levels of behavior problems. In addition to the original development of the PSI-SF, only three of the five studies with infants and toddlers demonstrated the reliability of the PSI-SF; two used the English version (Haskett, Ahern, Ward, & Allaire, 2006;Whiteside-Mansell et al., 2007) and one used the Spanish version (Díaz-Herrero, López-Pina, Pérez- López, de la Nuez, & Martínez-Fuentes, 2011). High internal consistency was demonstrated for the three subscales and the Total Stress scale for the Spanish version with a sample of Europeans (Díaz-Herrero et al., 2011). However, the Spanish version has not been examined with Hispanic parents, who report higher rates of child behavior problems (Holtrop, McNeil Smith, & Scott, 2015) and parenting stress (Franco, Pottick, & Huang, 2010). Studies on the English version in Head Start samples demonstrated high internal consistency estimates with mostly White children (Whiteside-Mansell et al., 2007) and high test–retest reliability estimates with mostly Black children (Haskett et al., 2006). In terms of validity, high correlations have been reported be- tween all PSI-SF subscales and child behavior problems (Haskett et al., 2006). Furthermore, research has documented positive cor- relations between the PD subscale and parenting behaviors, includ- ing negative parenting practices and emotional responsiveness (Haskett et al., 2006;Whiteside-Mansell et al., 2007), as well as maternal depression (Whiteside-Mansell et al., 2007). Research has also supported that mothers who report a higher external locus of control of their child’s behavior, report higher levels of parent- ing stress (Hassall, Rose, & McDonald, 2005). However, all pre- vious studies examining the validity of the PSI-SF used the Eng- lish version and included samples of predominantly White or Black families and not Hispanic families. Despite the demonstrated psychometric properties of the Eng- lish and Spanish versions of the PSI-SF with White and Black samples, no research study to our knowledge has examined the PSI-SF with a predominantly Hispanic sample. Research with Hispanic samples is particularly relevant in the United States, where the number of Hispanic families continues to rise (Johnson & Lichter, 2008). Given that Hispanic mothers report higher levels of parenting stress (Franco et al., 2010) and early childhood behavior problems (Theule, Wiener, Tannock, & Jenkins, 2013) compared with White mothers, it is important to ensure adequate measurement of parenting stress in infancy and with a predomi- nately Hispanic sample. Therefore, the purpose of the current study was to examine the reliability and validity of the PSI-SF with a high-risk sample of mothers and their infants. To examine reliability, we measured estimates of both internal consistency and test–retest reliability for the PSI-SF subscales and the Total Stress scale. To examine convergent validity, we measured the association between scores on each PSI-SF subscale with scores on measures of theoretically related constructs, including maternal depressive symptoms, ma- ternal parenting practices, and infant behavior. Finally, we exam- ined the optimal percentile cutoff for the PSI-SF Total Stress Score using the Receiver Operating Curve fitting (ROC) methodology, which examines the sensitivity and specificity of cutoff scores in predicting relevant outcomes. The criterion validity of the identi- fied cutoff score was subsequently assessed by comparing motherswho scored above and below clinical cutoffs on measures of maternal and infant outcomes. Method Participants were 58 mothers and their 12- to 15-month-old infant with elevated levels of behavior problems who participated in a larger study examining a brief home-based parenting inter- vention. Families were recruited at a large pediatric primary care clinic in a predominately Hispanic community. On average, moth- ers were 29.88-years-old (SD 5.28), and their infants (53% male) were 13.52-months-old (SD 1.30). Most mothers reported Hispanic ethnicity (91.4%) and an income below the poverty line (60.3%). Thirty-five mothers (60.3%) completed the assessment in Spanish (seeTable 1). To screen into the study, infants had to be rated by their mother to be above the clinical cutoff (i.e., 75th percentile) on theBrief Infant-Toddler Social and Emotional Assessment(BITSEA) Prob- lem scale (Briggs-Gowan, Carter, Irwin, Wachtel, & Cicchetti, 2004), a screening measure of social, emotional, and behavioral functioning. English-speaking mothers had to receive an estimated IQ score 70 on the two-subtest version of theWechsler Abbre- viated Scale of Intelligence(WASI;Wechsler, 1999), and Spanish- speaking mothers had to receive an average standard score 4on the two-subtest version of theEscala de Inteligencia Wechsler Para Adultos(EIWA;Pons et al., 2008). Exclusion criteria in- cluded any infants with major sensory impairments (e.g., deafness, blindness) or current child protection services involvement, al- though no families were excluded based on these criteria. During recruitment, we approached 315 families at the pediatric clinic and 146 primary caregivers (46.3%) agreed to participate. Primary caregivers were the infant’s mother in all cases. Sixty families screened into the intervention study and were randomized to an intervention (n 31) or standard care group (n 29). Two families screened in and were randomized to condition but did not complete the Time 1 (baseline) assessment and thus did not com- plete the PSI-SF yielding a total of 58 families for the current sample. Families in the intervention group received a brief home- based adaptation of Parent–Child Interaction Therapy (PCIT) for infants at-risk for behavior problems. Details of the intervention and main outcomes for the randomized controlled trial are pre- sented elsewhere (Bagner et al., 2015). Families in both groups participated in a Time 2 assessment an average of 71.87 (SD 16.42) days after the Time 1 assessment. Table 1 Sample Demographic Characteristics CharacteristicM(SD)N(%) Child age (months) 13.52 (1.30) Child sex (% male) 31 (53.4) Child ethnicity (% Hispanic) 55 (94.8) Child race (% minority) 57 (98.3) Mother age (years) 29.88 (5.28) Mother ethnicity (% Hispanic) 53 (91.4) Mother race (% minority) 55 (94.8) Mother marital status (% married) 40 (69.0) Mother education (% graduated high school or less) 40 (69.1) Poverty status (% below poverty) 35 (60.3) This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 1332 BARROSO, HUNGERFORD, GARCIA, GRAZIANO, AND BAGNER Measures Parenting Stress Index-Short Form (PSI-SF;Abidin, 2012). The PSI-SF is a 36-item self-report questionnaire of parenting stress with three subscales (PD, PCDI, and DC) and a Total Stress scale. Scores above the 85th percentile on the Total Stress scale are considered borderline clinically significant (Abidin, 2012), but this cutoff has not been empirically tested. As described previously, there is evidence to support reliability and validity, but there is limited support for these psychometric properties in a high-risk mother–infant sample. Center for Epidemiologic Studies Depression Scale (CES-D; Radloff & Locke, 1986).The CES-D is a widely used 20-item self-report questionnaire of depressive symptomatology. Internal consistency estimates have been found to be high in a variety of samples (Orme, Reis, & Herz, 1986;Thomas, Jones, Scarinci, Mehan, & Brantley, 2001), including the current sample ( .90). A cutoff score of 16 is commonly suggested for detecting depres- sion, and has been found to predict Major Depressive Disorder with a sensitivity of .95 and specificity of .70 (Thomas, Jones, Scarinci, Mehan, & Brantley, 2001). The CES-D was used to examine the convergent validity with the PSI-SF PD subscale. Parental Locus of Control-Short Form (PLOC-SF;Rayfield, Eyberg, Bogg, & Roberts, 1995).The PLOC-SF is a 25-item self-report questionnaire of parents’ beliefs regarding their ability to influence their child’s behavior. Higher scores indicate parents believe they have less control over their child’s behavior. Internal consistency rates have been acceptable ( .79;Rayfield et al., 1995), including in the current sample ( .71). The PLOC-SF was included in the current study to examine convergent validity with the PSI-SF PCDI subscale. Infant-Toddler Social and Emotional Assessment (ITSEA; Carter & Briggs-Gowan, 2006).The ITSEA is a parent-report questionnaire of social-emotional and behavioral problems in 12- to 36-month-olds and includes four broad domains (Externalizing, Internalizing, Dysregulation, and Competence). Strong reliability estimates of the ITSEA domains were demonstrated in a nationally representative sample (Carter & Briggs-Gowan, 2006), and inter- nal consistency estimates in the current sample were good for the Externalizing and Dysregulation domains ( .80 and .78, re- spectively) and adequate for the Internalizing domain ( .69). For these domains, aTscore 65 is consideredOf Concern, which is at or above the 90th percentile (Carter & Briggs-Gowan, 2006; Briggs-Gowan & Carter, 2007). The Externalizing, Internalizing, and Dysregulation domains were included in the current study to examine convergent validity with the PSI-SF DC subscale. Data Analysis Before analysis, the data were evaluated for multivariate outli- ers, and no outliers were detected. Values for missing data (17%) were imputed using multiple imputation in SPSS 20 (Chicago, IL). For internal consistency, Cronbach’s coefficients were computed for each of the three PSI-SF subscales and the PSI-SF Total Stress scale at Time 1. Internal consistency estimates were examined with the total sample and separately for mothers who completed the assessment in English and Spanish. Test–retest reliability between Time 1 and 2 for the subscales and Total Stress scale were evaluated using Intraclass Correlation Coefficients (ICC) using a two-way mixed, absolute consistency model (McGraw & Wong,1996). To control for group, linear regressions were also con- ducted to measure test–retest reliability between Time 1 and 2. To examine convergent validity, we examined correlations be- tween scores for each of the PSI-SF subscales with other measures of theoretically related constructs at Time 1. Specifically, we examined correlations between the PD subscale and CES-D; PCDI subscale and PLOC-SF; and DC subscale and ITSEA Externaliz- ing, Internalizing, and Dysregulation domains. To identify the optimal percentile cutoff for the PSI-SF Total Stress scale, we conducted ROC analyses in conjunction with Youden’sJindex. To demonstrate the utility of the identified cutoffs, criterion va- lidity was assessed. Results Internal consistency estimates were adequate for the PSI-SF PD subscale ( .75, .71, .79) in the full sample, as well as in the English and Spanish samples, respectively. Internal consistency estimates were good for the PSI-SF PCDI subscale ( .85, .87, .83) and DC subscale ( .82, .81, .84), and excellent for the Total Stress scale ( .91, .92, .90) in the full sample, as well as in the English and Spanish samples, respectively. Test–retest reli- ability estimates were good for the PSI-SF Total Stress scale (ICC .77, .78, .77) and the PSI-SF PD subscale ( .82, .80, .84) in the full sample, as well as in the English and Spanish samples, respectively. Test–retest reliability estimates were ade- quate for the PCDI subscale ( .61, .66, .58), and DC subscale ( .66, .60, .70) in the full sample, as well as in the English and Spanish samples, respectively. To control for group, PSI-SF sub- scales and the Total Stress scale at Time 2 were regressed on the PSI-SF subscales and the Total Stress scale score at Time 1. Mothers’ scores on the PSI-SF PD subscale at Time 1 significantly predicted scores at Time 2, B .77,t(45) 7.15,p .001, and explained a significant proportion of variance in Time 2 scores, R 2 .57,F(2, 45) 29.94,p .001. Mothers’ scores on the PCDI subscale at Time 1 significantly predicted scores at Time 2, B .38,t(45) 3.36,p .002, and explained a significant proportion of variance in Time 2 scores,R 2 .21,F(2, 45) 5.98, p .005.Mothers’ scores on the DC subscale at Time 1 significantly predicted scores at Time 2, B .46,t(45) 3.75, p .001, and explained a significant proportion of variance in Time 2 scores,R 2 .25,F(2, 45) 7.30,p .002. Finally, mother’s scores on the Total Stress scale at Time 1 significantly predicted scores at Time 2, B .57,t(45) 5.44,p .001, and explained a significant proportion of variance in Time 2 scores, R 2 .40,F(2, 45) 14.81,p .001. For convergent validity, mothers’ scores on the PSI-SF PD subscale were moderately correlated with mothers’ scores on the CES-D,r(58) .53,p .001. Similarly, mothers’ scores on the PCDI subscale were moderately correlated with mothers’ scores on the PLOC-SF,r(58) .44,p .001. Finally, mothers’ scores on the DC subscale were moderately correlated with infants’ scores on the ITSEA Externalizing,r(58) .50,p .01, Internalizing,r(58) .38,p .01, and Dysregulation,r(58) .44,p .01 domains. Sensitivity, specificity, and Youden’sJvalues were calculated for mother-reported Total Stress percentile scores at Time 1. Classification below or above the clinical cutoff on the CES-D (i.e., raw score 16) and ITSEA Externalizing, Internalizing, and This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 1333 PARENTING STRESS Dysregulation domains (i.e., Tscore 65) were used as the diagnostic outcome.Table 2illustrates the cutoff score that opti- mized both sensitivity and specificity as indexed by the best Youden’sJvalue, and the percentage of the sample that fell above the cutoff. Overall, the 72.5%ile best identified mothers with elevated levels of depressive symptoms and infant externalizing problems, and the 77.5%ile was identified as the optimal cutoff for infant internalizing and dysregulation problems. Relative to moth- ers below the PSI-SF cutoff for the CES-D (72.5%ile), mothers scoring at or above the cutoff were more likely to have increased depressive symptoms,t(56) 4.99,p .001. Relative to mothers below the PSI-SF cutoff for the ITSEA domain scores, mothers scoring at or above the 72.5%ile were more likely to have infants with increased externalizing,t(56) 2.25,p .05 and internal- izing,t(56) 4.24,p .001, and dysregulation,t(56) 3.42,p .001, for mothers scoring at or above the 77.5%ile. Discussion The purpose of the current study was to examine the psychometric properties of the PSI-SF in mothers of infants between 12- and 15-months-old with elevated behavior problems from predominately Hispanic and low-income families. Consistent with previousstudies of the PSI-SF in White and Black samples (Díaz-Herrero et al., 2011;McKelvey et al., 2009), internal consistency was good and test–retest reliability ranged from adequate to good in the English and Spanish versions. Support for convergent validity based on the relation between mothers’ scores on the PD subscale and the CESD suggests parental distress independent of the infant’s be- havior is related to maternal depressive symptoms, which is con- sistent with previous research documenting the association be- tween depression and life stress (Hammen, 2005). Additionally, support for convergent validity based on the relation between mothers’ scores on the PCDI subscale and the PLOC-SF indicates stress related to the parent– child relationship is related to the extent to which the mother perceives she has control over her infant’s behavior. Lastly, support for convergent validity based on the relation between mothers’ scores on the DC subscale and the ITSEA domains suggests stress related to the parent’s view of having a more difficult infant is related to infant behavior prob- lems and dysregulation. We also examined optimal cutoff scores for the PSI-SF Total Stress scale using ROC analyses in conjunction with Youden’sJto identify parent and child outcomes. For maternal outcomes, the 72.5%ile on the PSI-SF Total Stress scale demonstrated the best sensitivity and specificity for predicting maternal depressive symptoms. For child outcomes, the 72.5%ile and the 77.5%iledemonstrated the best sensitivity and specificity for the external- izing and the internalizing and dysregulation domains, respec- tively. The lower Youden’sJfor the Externalizing domain may be because of the high number of infants in the sample (n 42; 72%) with clinically elevated levels (Tscore 65) of externalizing behaviors, thereby limiting variability. Furthermore, we demon- strated these cutoffs adequately predicted both maternal and child outcomes. These findings suggest the 85th percentile cutoff may exclude mothers who are experiencing difficulties and may need services, especially among those from high-risk families. Although the cutoff scores are helpful in referring families for additional services, it is important to acknowledge that cutoff scores can be arbitrary (Blanton & Jaccard, 2006) and additional factors should be considered when referring families for services. The current study has some limitations, and it is important to interpret the current results in light of these limitations. First, the sample size used in the current analyses was relatively small in relation to other studies examining the psychometric properties of the PSI-SF, thereby limiting power. Therefore, the data presented should be interpreted as preliminary but should encourage future research on the psychometric properties of the PSI-SF in larger samples with similar demographic compositions. Additionally, the sample exam- ined was specific to infants 12 to 15 months of age with elevated behavior problems and may limit generalizability to infants from a wider age range, infants without behavior problems, or infants at high risk because of other problems (e.g., medical conditions). Thus, future research should examine the psychometric properties of the PSI-SF in these other infant populations. Another limitation is that data were collected only from mothers in the current study, which did not allow for examination of interrater reliability. Lastly, the current timeline for the test–retest interval was in the moderate range and may have been confounded by the fact that half of the group received an intervention. Although we controlled for intervention status in our regression analyses, future studies should examine test–retest reliability of the PSI-SF over different periods of time in a sample that did not receive an intervention to ensure stability across the scales. Despite these limitations, the current study was the first to examine the psychometric properties of the PSI-SF in a sample of mothers of infants with behavior problems from predominately Hispanic and low-income backgrounds. Examining the reliability and validity of the PSI-SF is especially important given the in- creased levels of parenting stress reported by mothers of young children with behavior problems and from Hispanic backgrounds and the need for instruments that accurately assess their difficulties related to parenting. Overall, these findings provide initial support for the psychometric properties of the English and Spanish ver- Table 2 Summary of Cutoff Thresholds Using Total Stress Percentile Scores (n 58) OutcomeOptimal percentile cutoffPercentage above cutoff Sensitivity Specificity Youden’sJ CES-D 72.5 41.1 .86 .58 .45 ITSEA externalizing 72.5 41.4 .68 .54 .23 ITSEA internalizing 77.5 50 .90 .58 .48 ITSEA dysregulation 77.5 50 .78 .63 .41 Note.ITSEA Infant Toddler Social Emotional Assessment; CES-D Center for Epidemiological Studies Depression Scale. This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 1334 BARROSO, HUNGERFORD, GARCIA, GRAZIANO, AND BAGNER sions of the PSI-SF in a high-risk sample. 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Parenting stress of low-income parents of toddlers and preschoolers: Psychometric properties of a short form of the Parenting Stress Index.Parenting, 7,26 –56.http://dx.doi.org/10.1080/ 15295190709336775 Received June 1, 2015 Revision received October 8, 2015 Accepted October 20, 2015 This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 1335 PARENTING STRESS
Find an experimental research study on the topic chosen in Week One for your Final Research Proposal. You may choose to include an experimental study which was included in the literature review you us
Experimental Design 1 Running Head: EXPERIMENTAL DESIGN Experimental Design and Some Threats to Experimental Validity: A Primer Susan Skidmore Texas A&M University Paper presented at the annual meeting of the Southwest Educational Research Association, New Orleans, Louisiana, February 6, 2008 . Experimental Design 2 Abstract Experimental designs are distinguished as the best method to respond to questions involving causality . The purpose of th e present paper is to explicate the logic of experimental design and why it is so vital to questions that demand causal conclusions. In addition, types of internal and external validity threats are discussed. To emphasize the current interest in exper imental desig ns, Evidence – Based Practices (EBP) in medicine, psychology and education are highlighted. Finally, cautionary statements regarding experimental designs are elucidated with examples from the literature. Experimental Design 3 The No Child Left Behind Act (NCLB ) deman ds “scientifically based research” as the basis for awarding many grants in education (2001). Specifically, the 107th Congress (2001) delineated scientifically -based research as that which “is evaluated using experimental or quasi -experimental designs”. Recognizing the increased interest and demand for scientifically -based research in education policy and practice, the National Research Council released the publication, Scientific Research in Education (Shavelson & Towne, 2002) a year after the implementat ion of NCLB . Almost $5 billion have been channeled to programs that provide scientifically -based evidence of effective instruction , such as the Reading First Program (U. S. Department of Education, 2007). With multiple methods available to education resea rchers, why does the U. S. government show partiality to one particular method? The purpose of the present paper is to explicate the logic of experimental design and why it is so vital to questions that demand causal conclusions. In addition, types of in ternal and external validity threats are discussed. To emphasize the current interest in experimental designs, Evidence -Based Practices (EBP) in medicine, psychology and education are highlighted. Finally, cautionary statements regarding experimental desig ns are elucidated with examples from the literature. Experimental Design An experiment is “that portion of research in which variables are manipulated and their effects upon other variables observed” (Campbell & Stanley, 1963, p. 171). Or stated another way, experiments are concerned with an independent variable (IV) that causes or predicts the outcome of the Experimental Design 4 dependent variable (DV). Ideally, all other variables are eliminated, controlled or distributed in such a way that a conclusion that the IV caused the DV is validly justified. Figure 1. Diagram of an experiment. In Figure 1 above you can see that there are two groups. One group receives some sort of manipulation that is thought (theoretically or from previous research) to have an impact on the DV. This is known as the experimental group because participants in thi s group receive some type of treatment that is presumed to impact the DV. The other group, which does not receive a treatment or instead receives some type of alternative treatment, prov ides the result of what would have happened without experimental inter vention (manipulation of the IV). So how do you determine whether participants will be in the control group or the experimental group? The answer to this question is one of the characteristics that underlie the strength of true experimental designs. True experiments must have three essential characteristics: random assignment to Outcome measured as DV No manipulation or alternate manipulation of IV (treatment or intervention) Control Group Manipulation of IV (treatment or intervention) Experimental Group Experimental Design 5 groups , an intervention given to at least one group and an alternate or no intervention for at least one other group, and a comparison of group performances on some post -interventio n measurement (Gall, Gall, & Borg, 2005). Participants in a true experimental design are randomly allocated to either the control group or the experimental group. A caution is necessary here. Random assignment is not equivalent to random sampling. Random sampling determines who will be in the study, while random assignment determines in which groups participants will be. Random assignment makes “samples randomly similar to each other , whereas random sampling makes a sample similar to a population ” (Shadish , Cook, & Campbell, 2002, p. 248, emphasis in original). Nonetheless, random assignment is extremely important. By randomly assigning participants (or groups of participants) to either the experimental or control group, each participant (or groups of parti cipants) is as likely to be assigned to one group as to the other (Gall et al., 2005). In other words, by giving each participant an equal probability of being a member of each group, random assignment equates the groups on all other factors, except for th e intervention that is being implemented, thereby ensuring that the experiment will produce “unbiased estimates of the average treatment effect” (Rosenbaum, 1995, p. 37). To be clear, the term “unbiased estimates” describes the fact that any observed effe ct differences between the study results and the “true” population are due to chance (Shadish et al., 2002). Experimental Design 6 This equality of groups assertion is based on the construction of infinite number of random assignments of participants (or groups of participants ) to treatment groups in the study and not to the single random assignment in the particular study (Shadish et al., 2002). Thankfully, researchers do not have to conduct an infinite number of random assignments in an infinite number of studies for this as sumption to hold. The equality of groups‟ assumption is supported in studies with large sample sizes, but not in studies with very small sample sizes. This is true due to the law of large numbers . As Boger (2005) explained, “If larger and larger samples are successively drawn from a population and a running average calculated after each sample has been drawn, the sequence of averages will converge to the mean, µ, of the population” (p. 175) . If the reader is interested in ex ploring this concept further, the reader is directed to George Boger‟s article that details how to create a spreadsheet simulation of the law of large numbers . In addition, a medical example of this is found in Observational Studies (Rosenbaum, 1995, pp. 1 3-15). To consider the case of small sample size, let us suppose that I have a sample of 10 graduate students that I am going to randomly assign to one of two treatment groups. The experimental group will have regularly scheduled graduate advisor meetings to monitor students‟ educational progress. The control group will not have regularly scheduled graduate advisor meetings. Just to see what happens, I choose to do several iterations of this random assignment process. Of course, I discover that the identit y of the members in the groups across iterations is wildly different. Experimental Design 7 Recognizing that most people are outliers on at least some variables (Thompson, 2006), there may be some observed differences that are due simply to the variable characteristics of the members of the treatment groups. For example, let‟s say that six of the ten graduate students are chronic procrastinators, and might benefit greatly from regular scheduled visits with a graduate advisor, while four of the ten graduate students are intrinsi cally motivated and tend to experience increased anxiety with frequent graduate advisor inquiries. If the random assignment process distributes these six procrastinator graduate students equally among the two groups, a bias due to this characteristic will not evidence itself in the results. If instead, due to chance all four intrinsically motivated students end up in the experimental group, the results of the study may not be the same had the groups been more evenly distributed. Ridiculously small sample s izes, therefore would result in more pronounced differences between the groups that are not due to treatment effects , but instead are due to the variable characteristics of the members in the groups. If instead I have a sample of 10,000 graduate students that that I am going to randomly assign to one of two treatment groups, the law of large numbers works for me. As explained by Thompson et al. (2005), “The beauty of true experiments is that the law of large numbers creates preintervention group equivalen cies on all variables, even variables that we do not realize are essential to control” (p. 183) . While there is still not id entical membership across treatment groups, and I still expect that the observed differences between the control group and the exper imental group are going to be due to any possible treatment effects Experimental Design 8 and to the error associated with the random assignment process, the expectation of equality of groups is nevertheless reasonably approximated . In other words, I expect the ratio of procras tinators to intrinsically motivated students to be approximately the same across the two treatment groups. In fact, I expect proportions of variables I am not even aware of to be the same, on average, across treatment groups! The larger sample size has gre atly decreased the error due to chance associated with the random assignment process. As you can see in Figure 2, even if both of the sample studies produce identical treatment effects, the results are not equally valid. The majority of the effect observ ed in the small sample size study is actually due to error associated with the random assignment process and not a result of the treatment. This effect due to error is greatly reduced in the large sample size study. Figure 2. Observed treatment e ffects in two studies with different sample sizes. The white area represents the amount of the observed effect due to the error associated with the random assignment process. The grey area represents the “true” treatment effect. Three Experimental Designs When well -conducted, a randomized experiment is considered the “ gold standard” in causal research (Campbell, 1957; Campbell & Stanley, 1963; “True” treatment effects n=10 error “True” treatment effects n=10,000 e r r o r Experimental Design 9 Sackett, Strauss, Richardson, Rosenberg, & Haynes, 2000; Thompson, 2006). In fact, “No other type of quantitative research (descriptive, correlational, or causal – comparative) is a s powerful in demonstrating the existence of cause -and -effect relationships among variables as experimental research” (Gall et al., 2005, p. 249). There are three designs that meet the characteristics of true experimental designs, first described by Campbe ll (1957) and revisited in several research design texts. While other designs have the potential to produce causal effects (see Odom et al., 2005; Rosenbaum, 1995; Thompson et al., 2005) only the three classic true experimental designs are discussed in th e present paper. For a more extensive description of other experimental designs, the reader is directed to research design works such as Campbell (1957); Campbell and Stanley (1963); Creswell (2003); Gall et al. (2005); Shadish et al. (2002); and Thompson (2006). The first true experimental design is known as the Pretest -Posttest Control -Group Design. This research design meets the characteristics of a true experiment because participants are randomly assigned (denoted by an R) to either the experimental or control group. There is an intervention or treatment (denoted by an X) given to one group, the experimental group, and no intervention (or alternate intervention) given to the other group, the control group . Finally, there is some form of post -interventio n measurement (denoted by an O ). This is also known as a post test, because this measurement occurs after the intervention. In addition, in this particular design, there is also a pre test, denoted by an O prior to the intervention. The pre test allows the re searcher to test for Experimental Design 10 equality of groups on the variable of interest prior to the intervention. These designs are “read” left to right to correspond to the passage of time (i.e., what happens first, second). Experimental Group R O X O Control Group R O O The second true experiment is the Posttest -Only Control Group Design. This design varies from the first in that it controls for possible confounding effects of a pretest because it does not use a pre -intervention measurement. All three characteristic s of a true experimental design are present as in the previous design: random assignment, intervention implemented with experimental group only, and post -intervention measurement. Experimental Group R X O Control Group R O The third and final design is the Solomon Four -Group Design. This design is the strongest of the three. It not only corrects for the possible confounding effects of a pretest, but allows you to compare these results, to an experimental and contr ol group that did receive a pre test. The major drawback to this design compared to the others is the obvious increase in sample size needed to meet the needs of four treatment groups as opposed to two treatment groups. Experimental Group (with pre -test) R O X O Control Group (with pre -test) R O O Experimental Group (without pre -test) R X O Control Group (without pre -test) R O In addition to detailing thes e designs in their seminal work , Campbell and Stanley (1963) firmly established their explicit commitment to experiments “as Experimental Design 11 the only means for settling disputes regarding educational practice, as the only way of verifying educational improvements, and as the only way of establishing a cumulative tradition in which improvements can be introduced without the danger of a fa ddish discard of old wisdom in favor of inferior novelties”(Campbell & Stanley, 1963, p. 172). Validity Threats Even when these designs are used, there are differences in how rigidly they are followed as well as to what extent the researcher addr esses the multiple threats to validity (see Figure 3 below). Threats to validity are important not only to research designer but also to consumers of research. An informed consumer of research want s to rule out all competing hypothesis and be firmly conv inced that the evidence supports the claim that the IV caused the DV. To merit this conclusion, an evaluation of the study is necessary to determine whether threats to experimental validity were recognized and mitigated. Figure 3. Example of a research experiment and the questions you should ask yourself about internal and external validity. Adapted from (Sani & Todman, 2006). hypothesized effects internal validity Are we really observing these effects or the effects of other variables on the DV (procrastination vs. intrinsically motivated)? external validity Are these effects to be found in other contexts and people, or are they specific t o our experimental setting and participants? Independent Variable Graduate Advisor Meetings Dependent Variable Procrastination/ Motivation scale Experimental Design 12 Internal Validity Creswell defines internal validity threats as those “experimental procedures, treatments, or experiences of the participants that threaten the researchers‟ ability to draw correct inferences from the data in an experiment” (2003, p. 171) . In their classic text, Campbell and Stanley (1963) identified eight threats to internal validity. In a mor e recent text, Shadish, Cook and Campbell (2002) address ed nine threats to validity which are described below. For an extensive list of threats to internal and external validity , the reader is directed to Onwuegbuzie‟s work that cogently expresses the need to evaluate “ all quantitative research studies” (2000, p. 7), not just experimental design studies , for threats to internal and external validity. 1. Ambiguous temporal precedence: uncertainty about which occurred first (IV or DV) which would lead to qu estions about which variable is the cause and which is the effect. 2. Selection bias: a systematic bias resulting in non -random selection of participants to groups. By definition random assignment prevents selection bias , if and only if the law of large n umbers can be invoked. 3. History: an event that may occur between measurements that is not part of the intervention that could impact the posttest measurement. For example, let us return to the ten fictional graduate students described previously in the study . Let‟s say they were all living in the same dorm and the fire alarm kept going off the night before they were to take the motivation/ procrastination measurement instrument. Due to lack of sleep, participants may perform differently on the Experimental Design 13 motivation / procrastination scale than they would have had they gotten enough sleep . 4. Maturation: an observed change that is naturally occurring (such as aging, fatigue, hair length, number of graduate hours completed) that may be confused with the intervention ef fects but is really a function of the passage of time. 5. Statistical regression: the phenomenon that occurs when participant selection is based on extreme scores whereby the scores become less extreme, which may appear to be the intervention effect. If i n our study of graduate students we purposively select students based on pretest score s of extreme procrastination , the extreme procras tinator graduate students will on the posttest not be as extreme in their procrastination tendencies. Regression toward the mean was first documented by Sir Francis Galton in the late 1800s. Galton (1886) measured the heights of fathers and sons at a World Exposition. Galton found that very tall fathers tended to have sons who were not quite as tall, and that very short fat hers tended to have sons who were not quite as short. Clearly, this phenomenon is not a function of the exercise of will (i.e., fathers did not say to their wives, “Let‟s make a shorter son” or “Let‟s make a taller son”)! 6. Experimental mortality or attri tion: a concern about a differential loss of participants , or of different types of participants from the experimental or control group that may produce an effect that appears to be due to the intervention. For example, if half of the students in the exper imental group drop out of the study, but none of the control group member s drop, we would likely question the results. Experimental Design 14 Were those students that left somehow different from the ones that remained? If so, would that difference have produced differential resu lts than the ones we observed with the remaining participants? 7. Testing: the concern that a testing event will impact scores of a s ubsequent testing event. For example, if we give the graduate students the procrastination/ motivation scale prior to any graduate advisor meetings (the intervention), and then after the intervention we give them the procrastination/ motivation scale again, we may observe difference in the pre – and post test that are due part ly to familiarity with the test or the influence of the testing itself. 8. Instrumentation: th e change in either the measurement instrument itself or the manner in which the instrument is implem ented or scored that may cause changes that appear to be due to the intervention , or the failure to detect changes that actually did occur . For example, if between the first and second time that the procrastination/motivation test is given, the developers of the exam decide to remove ten of the questions, we do not know if the exclusion of those questions is responsible for differential scores or if the differences are due to treatment effects. 9. Additive and interactive effect of threats to internal vali dity: the concern that the impact of the threats may be additive or that presence of one threat may impact another. A selection -history additive effect occurs when nonequivalent groups are selected . F or example, groups may be selected from t wo different locations, such as, rural and urban areas. The participants in the groups are nonequivalent by selection and they also have unique local histories. The Experimental Design 15 resulting net bias is dependent on both the direction and magnitude of each individual bias and how the bi ases combine. Selection -maturation, and selection – instrumentation are other versions of this type of effect. External Validity External validity threats are threats of “incorrect inferences from the sample data to other persons, other settings, and past o r future situations” (Creswell, 2003, p. 171). Researchers must always remember the context from which their sample comes from, and take caution not to over generalize beyond that. Campbell and Stanley (1963) included four threats to external validity. Sh adish (2002) listed five external validity threats , as detailed below. 1. Interaction of the causal relationship with participants: an effect with certain kinds of participants that may not be present (or present to the same extent) with other kinds of par ticipants. For example, reduction of salt intake in hypertensive patients is more beneficial to certain populations than others (American Heart Association Nutrition Committee, 2006). 2. Interaction of the causal relations hip over treatment variations: the permanence of the causal relationship is dependent on fidelity to the specific treatment, thus possibly producing differential effects when treatments are varied. If a particular instructional intervention includes 5 components, the causal relationship m ay not hold if only 2 or 3 of the components are utilized. 3. Interaction of the causal relationships with outcomes: an effect that is present with one type of outcome measurement that may not be present (or present to Experimental Design 16 the same extent) if other outcome me asurements were used. For example, if a person scores highest on a test for physical strength they may not necessarily score highest on a flexibility test. 4. Interactions of the causal relationship with settings: an effect that is present in a particular setting may not be present (or present to the same extent) in a different setting. For example, a particular after school character development program involving community project work may not work equally well in rural versus urban areas. 5. Context -dep endent mediation: an explanatory mediator of a causal relationship in one context may not have the same impact in another context. For example, a study might find that a reduction in federal funding has no impact on student achievement because schools were able to turn to education foundation grants to provide them with additional resources. In another school district where schools did not have access to education foundation resources, the same causal mechanism may not be available. In addition to internal and external validity threats, there are other threats that we need to be aware of in the design and evaluation of studies. Interested readers may refer to such texts as Experimental and Quasi -Experimental Designs (Shadish et al., 2002) or Research Desig n: Qualitative, Quantitative, and Mixed Methods Approaches (Creswell, 2003) for information about statistical conclusion validity and construct validity concerns. Experimental Design 17 EBP in Medicine, Psychology and Education While the origins of EBP may date back to the origin of scientific reasoning, the Evidence -Based Medicine Working Group (EBMWG) brought the discussion of EBP to the forefront of medicine (1992). In 1996, Evidence -Based Medicine (EBM) was defined as “the c onscientious, explicit, and judicious use of current best evidence in making decisions about the care of individual patients. The practice of evidence based medicine means integrating individual clinical expertise with the best available external clinical evidence from systematic research” (Sackett, Rosenberg, Gray, Haynes, & Richardson, 1996, p. 71) . While EBP has many supporters in medicine, EBP has caused some concerns among practitioners. Researchers have addressed concerns regarding the perception of EBM as a top down approach that results in ivory tower researchers dictating how practitioners should practice (Sackett et al., 19 96) or similarly that evidence from randomized controlled trials may be valued more highly than practitioner expertise (Kübler, 2000). Yet, it is difficult to deny that there is great support for EBP considering the number of periodicals that have emerged since the years after EBMWG convened. A keyword search for “evidence -based” returns 100 serials on WorldCAT . A keyword search for “evidence -based” returns 96 serials in Ulrich’s Periodical Directory . At least 32 active periodicals, either in print form, electronic form, or both contain “evidence -based” within the title of the periodical. At least 26 of these periodicals are available electronically. See Table 1. Experimental Design 18 From the titles you can see that the majority of these periodicals are from a health -related field. It is important to note that while EBP do not only include randomized, experimental trials, the purpose of the table is to demonstrate the popularity of EBP that began in the mid 1990s and continues today. Ta ble 1 “Evidence -Based” periodicals St art Year Title of Periodical 1994 Bandolier: Evidence -Based Healthcare 1995 Evidence -Based Medicine 1996 Focus on Alternative and Compl ementary Therapies: An Evidence – Based Approach 1997 Evidence -Based Cardiovascular Medicine 1997 Evidence -Based Medicine in Practice 1997 (1998) Evidence -Based Mental Health 1997 (1998) Evidence -Based Nursing 1997 Evidence -Based Obstetrics and Gynecology 1998 EBN Online 1998 Evidence -Based Dentistry 1998 Evidence -Based Practice 1998 Evidence -Based Practice: Patient Oriented Evidence That Matters 1999 Evidence -Based Dental Practice 1999 (2002) Trends in Evidence -Based Neuropsychiatry: T.E.N. Experimental Design 19 Table 1 (continued). Start Year Title of Periodical 2000 Evidence -Based Gastroenterology 2000 Evidence -Based Oncology 2000 Trauma Reports: Evidence -Based Medicine for the ED 2001 Journal of Evidence -Based Dental Practice 2003 Evidence -Based Integrative Medicine 2003 Evidence -Based Midwifery 2003 Evidence -Based Preventive Medicine 2003 Evidence -Based Surgery 2003 (2005) International Journal of Evidence -Based Healthcare 2004 Evidence -Based Complementary and Alternative Medicine: eCAM 2004 Journal of Evidence -Based Social Work 2004 Worldviews on Evidence -Based Nursing 2005 Advances in Psychotherapy: Evidence -Based Practice 2005 Evidence -Based Ophthalmology 2005 Journal of Evidence -Based Practices for Schools 2006 Evidence -Based Child Health 2006 Evidence -Based Library and Information Practice 2007 Evidence -Based Communication Assessment and Intervention Periodicals available electronically are shown in bold. Parenthetical dates indicate different start year date in WorldCAT . Experimental Design 20 The popularity of EBP is evident in psychology as well. The American Psychological Association‟s Presidential Task Force on Evidence -Based Practice specifically defined Evidence -Based Practice in Psychology (EBPP) as “the integration of the best a vailable research with clinical expertise in the context of patient characteristics, culture, and preferences” (2006, p. 273). In addition to advocating evidence -based practices, this task f orce also established the two necessary components for evaluation of psychological interventions: treatment efficacy and clinical utility. Treatment efficacy specifically addresses questions such as how well a particular treatment works. This type of question lend s itself to experimental investigation to draw valid causal conclusions about the effect of a particular intervention (or lack thereof) on a particular disorder (American Psychological Association, 2002). Chambless and Hollon (1998), in their review of ps ychological treatment literature, provide a description of variables of interest when evaluating treatment efficacy in research studies. The Task Force acknowledged that while there are other methods that may lead to causal conclusions “randomized control led experiments represent a more stringent way to evaluate treatment efficacy because they are the most effective way to rule out threats to internal validity in a single experiment” (American Psychological Association, 2002, p. 1054). The appeals for evi dence continue also in the field of education. Grover J. (Russ) Whitehurst, who directs the Education Department’s Institute of Education Sciences, defined Evidence -Based Education (EBE) as “the integration of professional wisdom with the best available em pirical evidence in making Experimental Design 21 decisions about how to deliver instruction” (in Towne, 2005, p. 41). Whitehurst (2002 b) explained that without empirical evidence education is at the mercy of the latest educational craze. In addition, asserted that cumulative kn owledge cannot be generated without empirical evidence. To assist education practitioners in the identification of EBP, a practical guide has been provided (see Coalition for Evidence -Based Policy, 2003). Table 2 Definitions of EBP in medicine, psycholog y and education Field Definition Evidence -based medicine (EBM) “the conscientious, explicit, and judicious use of current best evidence in making decisions about the care of individual patients. The practice of evidence based medicine means integrating individual clinical expertise with the best available external clinical evidence from systematic research” (Sackett et al., p. 71). Evidence -based practices in psychology (EBPP) “the integration of the best available research with clinical expertise in the context of patient characteristics, culture, and preferences” (American Psychological Association, 2006, p. 273). Evidence -based education (EBE) “the integration of professional wisdom with the best available empirical evidence in making decisions about how to deliver instruction” (Whitehurst, 2002 b, Slide 3). Medicine, psychology, and education all have seemed to have jumped on the evidence wagon. Their definitions share the common themes of integration of Experimental Design 22 expertise with the best a vailable evidence (see Table 2 above ). We cannot ignore this need to balance practitioner expertise with empirical evidence whether in the field of medicine, psychology or education. As Kübler (2000) cautions: Undoubtedly evidence based medicine is the gold standar d for modern medicine. The results, however, should be applied in patient care with careful reflection. Otherwise evidence based medicine may acquire the same status for the doctor as a lamp post for a drunk: it gives more su pport than enlightenment . (p. 1 35) Frequency of Experiments in Different Disciplines One final caution is offered. It is imperative that consumers and producers of research critically evaluate research. In addition to threats to validity, we must keep in mind that experiments are conduc ted by people. People are fallible. We are prone to make mistakes, both consciously and unconsciously. An example of this is a graph that appears to be from the same data, yet describes different results . What is critical about these graphs is that depend ing on which one you look at, education ranks third, fourth, or first in cumulative total number of reports of trials identified from the Campbell Collaboration Social, Psychological, Educational and Criminological Trials Register (C2 -SPECTR) (Petrosino, Boruch, Rounding, McDonald, & Chalmers, 2000). One graph depicts education behind cri minology and psychology, but ahead of social policy (Boruch, Moya, & Snyder, 2002, p. 63). The authors describe the graph as follows: Experimental Design 23 Figure 3 -4 shows the increase in the number of articles on randomized and possibly randomized experiments that have appeared in about 100 peer -reviewed journals and in other places since 1950. The figure is based on the Campbell Collaboration Social, Psychological, Educational, and Criminolo gical Trials Registry (C2 -SPECTR) that is being developed in a continuing effort to identify all RFTs. (p. 62). The authors correctly cite Petrosino et al. (2000) as the source of the graph. In another graph, which cites Boruch et al. (2002) , education is now in last place behind criminology, psychology and social policy respectively (Whitehurst, 2002 b). The foll owing description was offered in Whitehurst‟s (2002 b) presentation: This chart indicates the total number of articles about randomi zed field trials in other areas of social science research (criminology, social policy and psychology) has steadily grown over the last 40 years; however, the number related to educat ion research has trailed behind. (Table Description, Slide 22) In a very similar presentation by Whitehurst (2002 a), a more extensive description of the same graph is provided: While the total number of articles about randomized field trials in other areas of social science research has steadily grown, the number in education research has trailed behind. The graph on this slide measures the growth of randomized field trials from 1950 to the present in the areas of criminology, social policy, psychology, and education. It shows that the Experimental Design 24 most rapid growth has been in criminology, followed by comparable rates of growth in social policy and psychology, with education having the least amount of growth. Source for the graph: Robert Boruch, Dorothy de Moya, and Brooke Snyder, 2001. (slide 21) The correct year for the citation is actual ly 2002. Finally, in still another version of the graph, education is leading the pack followed by psychology, social and criminology (Petrosino, Boruch, Soydan, Duggan, & Sanchez -Meca, 2001). The following description is offered: To facilitate the work of reviewers, the Campbell Collaboration Social, Psychological, Educational and Criminological Trials Register (C2 – SPECTR ) is in development. As Figure 2 shows, preliminary work toward C2 -SPECTR has already identified more than 10000 citations to randomize d or possibly randomized trials . (p. 28) Petrosino et al. (2001) cite Petrosino et al. ( 2000) , the same reference cited in Boruch et al . (2002 ). The only difference is that incorrect page numbers are given here. Instead of correctly identifying the pages a s 206 -219, Petrosino et al. (2001) identify pages 293 -307. Aside from the citations errors, one would hope that c larity about the results of the graph would be found in the original citation . Is education fourth, third or first in cumulative number of reports of randomized trials? The original citation , Petrosino et al. (2000) does ma tch the results of the graph in Petrosino et al. ( 2001), but not the results of the graphs in Whitehurst (2002 b) or Boruch et al. (2002).The original source offers the foll owing description for the chart: Experimental Design 25 C2 -SPECTR thus currently contains a total of 10,449 records. Figure 1 shows cumulative totals of reports of trials published between 1950 and 1998, subdivided on the basis of the „high level‟ codes which were assigned to in dicate the sphere(s) of intervention. (p. 211) See Figure 4 for a visual explanation. Figure 4. Diagram of citation errors. Deviations from original source are shown in bold. Examining the graphs, it is easy to see how these changes could have been made inadvertently. Nonetheless, one has to consider the impact that these errors may have had. Whitehurst‟s presentation was disseminated in “a series of four regional meetings as part of its work to ensure the effective implementation of the No Child Left Behind (NCLB) Act” (U. S. Department of Education, 2002) . In addition, the Web of Science shows that this presentation was cited at least 6 cite cite cites Boruch et al. (2002) 1. Criminology 2. Psychology 3. Education 4. Social  (p. 206 -219) Petrosino et al. (2001) 1. Education 2. Psychology 3. Social 4. Criminology  (p. 293 -307) Original Source Petrosino et al. (2000, p. 206 -219) 1. Education 2. Psychology 3. Social 4. Criminology Whitehurst (2002) 1. Criminology 2. Psychology 3. Social Policy 4. Education  No page number given  (2001) Experimental Design 26 times, Evidence Matters (Mosteller & Boruch, 2002) was cited at least 35 times, and the original source ( Petrosino et al., 2000) was cited 12 times with the correct page number and 6 times with the incorrect page number which is given in Petrosino et al. ( 2001). Whitehurst‟s (2002 b) presentation was described in a report by WestEd titled Scientific Research and Evidence -Based Practice (Hood, 2003). Hood gives the following description of the graph in Whitehurst‟s presentation: 22. Edu cation Lags Behind Chart Description: This chart indicates the total number of articles about randomized field trials in other areas of social science research (criminology, social policy and psychology) has steadily grown over the last 40 years; however, the number related to education research has trailed behind. [By approximately 1996, the cumulative number of articles about definite and possible randomized field trials in criminology is approaching 6,000; the numbers in social policy and psychology exce ed 2,000; while the number for education is less than 1,000.] (p.22) In addition, Whitehurst‟s presentation is identified as one of the Editor‟s Picks under Proven Methods : Doing What Works within the NCLB page on the U.S. Department of Education‟s Website (see http://www.ed.gov/nclb/methods/whatworks/edpicks.jhtml ). Colorado‟s Department of Education has apparently incorporated Whitehurst‟s graph into their Fast Facts: Evidence -Based Practice (2005) . Perhaps because of Whitehurst‟s position as the Director of the Institute of Education Sciences, or Experimental Design 27 perhaps because of the wide dissemination of this presentation, citations alone are not enough to measure the impact that his presentation has had. Errors in scholarly reports are not new. Thompson (1988, 1994) examined methodological mistakes in dissertations. Doctoral students and the prevalence of documentation errors are discussed in a recent article where the authors give several source s that address documentation errors in the literature such as “citation errors (for example, non -compliance to the prescribed editorial style), reference omissions, reference falsification, inconsistent references, inaccurate quotations, misspelled names, incorrect page numbers, and even fraudulent research” (Waytowich, Onwuegbuzie, & Jiao, 2006, p. 196) . Mistakes will always be present; it is up to the research community, and informed consumers to make wise decisions regarding the worth of studies. There i s no substitute for good judgment. Summary While true experiments do have the potential to provide the best possible causal evidence, it is imperative to keep in mind the threats that may undermine confidence in the findings, from internal and external va lidity threats, to simple human errors. In the wise words of Sackett and colleagues, the purpose of this type of research is to inform , but not to replace individual practitioner‟s knowledge (Sackett et al., 1996). This implies judgment on the part of the reader. Experimental Design 28 References American Heart Association Nutrition Committee. (2006). AHA Scientific statement: Diet and lifestyle recommendations revision 2006. Circulation, 114 , 82 -96. American Psychological Association. (2002). Criteria for evaluating treatment guidelines. American Psychologist, 57 , 1052 -1059. American Psychological Association. (2006). Evidence -based practice in psychology. American Psychologist, 61 , 271 -285. Boger, G. (2005). Spreadsheet simulation of the law of large numbers. Mathematics an d Computer Education, 39 , 175 -182. Boruch, R., Moya, D. D., & Snyder, B. (2002). The importance of randomized field trials in education and related areas. In F. Mosteller & R. 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Research design: Qualitative, quantitative, and mixed methods approaches (2n d ed.). Thousand Oaks, CA: Sage Publications, Inc. Evidence -Based Medicine Working Group. (1992). Evidence -based medicine: A new approach to teaching the practice of medicine. Journal of the American Medical Association, 268 , 2420 -2425. Gall, J. P., Gall , M. D., & Borg, W. R. (2005). Applying educational research: A practical guide (5th ed.). Boston: Pearson Education, Inc. Experimental Design 29 Galton, F. (1886). Regression towards mediocrity in hereditary stature. Journal of the Anthropological Institute, 15 , 246 -263. Hood, P. D. (2003). Scientific research and evidence -based practice. Retrieved January 2, 2008, from http://www.wested.org/online_pubs/scientrific.research.pdf Kübler, W. (2000). T reatment of cardiac diseases: Evidence based or experience based medicine? 84 , 134 -136. Mosteller, F., & Boruch, R. (Eds.). (2002). Evidence matters: Randomized trials in education research . Washington, DC: Brookings Institution Press. Odom, S. L., Brant linger, E., Gersten, R., Horner, R. H., Thompson, B., & Harris, K. R. (2005). Research in special education: Scientific methods and evidenced -based practices. Exceptional Children, 71 , 137 -148. Onwuegbuzie, A. J. (2000, November). Expanding the framework of internal and external validity in quantitative research. Paper presented at the annual meeting of the Association for the Advancement of Educational Research, Ponte Vedra, FL. (ERIC Document Reproduction Service No. ED 448 205). Petrosino, A., Boruch , R. F., Soydan, H., Duggan, L., & Sanchez -Meca, J. (2001). Meeting the challenges of evidence -based policy: The campbell collaboration The ANNALS of the American Academy of Political and Social Science, 578 , 14 -34. Petrosino, A. J., Boruch, R. F., Roundi ng, C., McDonald, S., & Chalmers, I. (2000). The campbell collaboration social, psychological, educational and criminological t rials register (C -2 SPECTR) to f acilitate the preparation and maintenance of systematic reviews of social and educational interve ntions. Evaluation and Research in Education, 14 , 206 -219. Rosenbaum, P. R. (1995). Randomized experiments . New York: Springer -Verlag. Sackett, D. L., Rosenberg, W. M. C., Gray, J. A. M., Haynes, R. B., & Richardson, W. S. (1996). Evidence based medicine : What it is and what it isn’t. British Medical Journal, 312 (7023), 71 -72. Sackett, D. L., Strauss, S. E., Richardson, W. S., Rosenberg, W., & Haynes, R. B. (Eds.). (2000). Evidence -based medicine: How to practice and teach EBM . New York: Churchill Living stone. Sani, F., & Todman, J. (2006). Experimental design and statistics for psychology: A first course . Malden, MA: Blackwell Publishing. Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi – experimental designs for generalized causal inference . Boston: Houghton Mifflin. Experimental Design 30 Shavelson, R. J., & Towne, L. (Eds.). (2002). Scientific research in education . Washington, DC: National Academy Press. Thompson, B. (1988, November). Common methodology mistakes in dissertations: Improving dissertation quality. Paper presented at the Annual Meeting of the Mid -South Educational Research Association, Louisville, KY. (ERIC Document Reproduction Service No. ED 301 595). Thompson, B. (1994, April). Common methodology mistakes in dissertations , revisited. Paper presented at the Annual Meeting of the American Educational Research Association New Orleans, LA. (ERIC Document Reproduction Service No. ED 368 771). Thompson, B. (2006). Foundations of behavioral statistics: An insight -based approa ch . New York: Guilford. Thompson, B., Diamond, K. E., McWilliam, R., Snyder, P., & Snyder, S. W. (2005). Evaluating the quality of evidence from correlational research for evidence -based practice. Exceptional Children, 71 , 181 -194. Towne, L. (2005). Scie ntific evidence and inference in educational policy and practice: Defining and implementing “Scientifically based research”. In C. A. Dwyer (Ed.), Measurement and Research in the Accountability Era (pp. 41 -58). Mahwah, NJ: Routledge. U.S. Congress. (2001) . No Child Left Behind Act of 2001, Public Law No. 107 -110 . Washington, DC. U. S. Department of Education. (2002). Lead & manage my school: Student achievement and school accountability conference. Retrieved December 27, 2007, from http://www.ed.gov/admins/lead/account/sasaconference02.html Waytowich, V. L., Onwuegbuzie, A. J., & Jiao, Q. G. (2006). Characteristics of doctoral students who commit citation errors. Library Review, 5 5, 195 -208. Whitehurst, G. J. (2002a). Archived evidence -based education (EBE). Retrieved December 20, 2007, from http://www.ed.gov/offices/OERI/presentations/evidencebase.html Whitehurst, G. J. (2002 b). Evidence -based education (EBE). On Student Achievement and School Accountability Conference . Retrieved December 20, 2007, from http://www.ed.gov/nclb/m ethods/whatworks/eb/edlite -index.html
Find an experimental research study on the topic chosen in Week One for your Final Research Proposal. You may choose to include an experimental study which was included in the literature review you us
Multidisciplinary Parent Education for Caregivers of Children with Autism Spectrum Disorders Binbin Ji a,b , Mei Sun b, Rongfang Yi c, Siyuan Tang b,⁎ aSchool of Nursing, Hunan University of Chinese Medicine, Changsha, ChinabSchool of Nursing, Central South University, Changsha, ChinacThe second Xiangya hospital of Central South University, Changsha, China abstract This quasi-experimental study aimed to determine the effectiveness of a multidisciplinary parent education program focused on improving health-related quality of life (HRQOL) for caregivers of children with autism spectrum disorders (ASD). This study included 42 participants (22 intervention, 20 wait-list control) who were the main caregivers of children with ASD. Data were collected at baseline and post-intervention. At the end of the multidisciplinary parent education program, significant improvements were observed in the mental HRQOL, family functioning, self-efficacy and positive coping style. The results indicate that a multidisciplinary parent education program, designed for caregivers of children with ASD, may have positive effects on caregivers’ mental health-related quality of life, while having little effect on their physical health- related quality of life. © 2014 Elsevier Inc. All rights reserved. The identified prevalence of autism spectrum disorder (ASD) has increased significantly around the world over the last few decades (Australian Bureau of Statistics, 2011; Centers for Disease Control and Prevention, 2012; Kim et al., 2011). About 1 in 88 children has been identified with an ASD according to estimates from the CDC’sAutism and Developmental Disabilities Monitoring Network (2012). In China, Professor Taofirst reported four cases of children with ASD in 1982 and subsequently, about 1.8 million children with ASD have been reported across the country (Autism-World, 2008). Raising a child with ASD can be a challengeable experience for caregivers. Previous studies show that caregivers of children with ASD have lower health-related quality of life (HRQOL) as compared to caregivers of typically-developing children (Allik, Larsson, & Smedje, 2006) and caregivers of children with other development disabilities or physical impairments (Abbedutao et al., 2004). In addition, caregivers’ HRQOL impacts on the quality of care provided to the child (Beach et al., 2005). Consequently, improving HRQOL in caregivers of children with ASD is overdue. Previous studies demonstrate that HRQOL of caregivers of children with ASD is affected by several variables, including demographics of the caregivers and care recipients (Lee et al., 2009), family functioning (Johnson, Frenn, Feetham, & Simpson, 2011; Khanna et al., 2011), social support (Khanna et al., 2011; Zablotsky, Bradshaw, & Stuart, 2013),caregiver burden (Khanna et al., 2011; Zablotsky et al., 2013), and maladaptive coping (Khanna et al., 2011; Zablotsky et al., 2013). Our previous research also found that besides the socio-demographic characteristics of children with ASD and their caregivers, family functioning, caregiver burden, coping style and social support are the main predictors of HRQOL of caregivers of children with ASD (Ji et al., 2014). Therefore, in order to improve caregivers’ HRQOL, comprehensive interventions that incorporate these components are recommended. Parent education is defined as an educational effort that attempts to enhance or facilitate parent behaviors that will, in turn, influence positive developmental outcomes in the participants and their children (Smith, Perou, & Lesesne, 2002). Previous studies have confirmed that parent education programs that include counseling components or focus on counseling aspects (managing emotions, cognitions and behavior) are useful for parents of children with ASD (Bitsika & Sharpley, 1999, 2000). A review of parent education programs for parents of children with ASD (Schultz, Schmidt, & Sticher, 2013), showed that parent education can increase knowledge and skills in caring for the children with ASD, with two potential benefits: reducing parent stress and increasing parents’ sense of competence. Currently, most parent education programs target on improving the child’s behavior and communicative capacity. However, limited research has studied the impact of parent education on HRQOL of caregivers of children with ASD. In one such study,Christodulu, Rinaldi, Knapp-Ines, Hiruma, and Costanzo (2012)found that families of children recently diagnosed with ASD benefited greatly from participating in a multidisciplinary parent education program. Parents who participated reported improvements in overall quality of life following the program, and Archives of Psychiatric Nursing 28 (2014) 319–326 ⁎Corresponding Author: Siyuan Tang, PhD, Professor, Head of School, School of Nursing, Central South University, Changsha, China. E-mail addresses:[email protected](B. Ji),[email protected](M. Sun), [email protected](R. Yi),[email protected],[email protected](S. Tang). http://dx.doi.org/10.1016/j.apnu.2014.06.003 0883-9417/© 2014 Elsevier Inc. All rights reserved. Contents lists available atScienceDirect Archives of Psychiatric Nursing journal homepage:www.elsevier.com/locate/apnu also had greater knowledge of ASD (Christodulu et al., 2012). Although the study byChristodulu et al. (2012)shed some light on the effectiveness of multidisciplinary parent education on quality of life among caregivers of children with ASD, there were a few limitations that restricted its generalizability. First, the study had a small sample size (only 14 parents participated in the program). Second, the main aim of the study was to identify the impact of a multidisciplinary parent education program on stress, knowledge and quality of life. The impacts of a multidisciplinary parent education program on other predictors of quality of life, such as caregiver burden, family functioning, social support and coping style, were not studied. In the current study, we focused on a multidisciplinary parent education that incorporated four subjects (community nursing, psychiatry, psychology and special education), improving caregivers’ HRQOL, through teaching ASD related knowledge and skills, and stress and mood self-management, and how to deal with family problems. THEORETICAL FRAMEWORK The theoretical foundation ofthe multidisciplinary parent education intervention was based onKhanna et al.’s (2011) conceptual model of factors influencing HRQOL among caregivers of children with ASD. This conceptual model was also confirmed in our previous study (Ji et al., 2014). Consistent with this conceptual model, Psychologist Albert Bandura (Luszczynska & Schwarzer, 2005) defined self-efficacy as one’s belief in one’s ability to succeed in specific situations. This sense of self-efficacy can play a major role in how people approach goals, tasks, and challenges. It is proposed that caregivers of children with ASD would benefit from learning basic information about ASD; skills for taking care of children with ASD, strengthening positive coping style and mood self-management capacity were also included in this intervention. Enhancing family functioning and utilizing social support were also included to improve health-related quality of life of caregivers of children with ASD. Perceived social support, coping style and family functioning were conceived as proximal outcomes, and caregiver burden, self-efficacy and health-related quality of life as distal outcomes (seeFig. 1). PURPOSE OF THE STUDY The main aim of the current studywas to assess the effectiveness of a multidisciplinary parent education program designed to improve health-related quality of life in caregivers of children with ASD.METHODS Research Design This research was conducted as a quasi-experimental study to assess the effectiveness of a multidisciplinary parent education program, prepared to improve health-related quality of life for caregivers of children with ASD. Sample This is a quasi-experimental study, conducted in two autism rehabilitation centers in Hunan Province of China from June to July 2013. The study sample consisted of 50 caregivers of children with ASD in two autism rehabilitation centers (Ai-meng rehabilitation center and Xing-yuan rehabilitation center) in Hunan Province, China. To prevent interaction and potential cross-contamination between the intervention group and the wait-list group, a cluster randomized trial was used. Caregivers in Ai-meng rehabilitation center were randomly assigned to the intervention group (27 caregivers), while caregivers in Xing-yuan rehabilitation center were randomly assigned to the wait-list control group (23 caregivers). However, 5 caregivers from the intervention group and 3 caregivers from the control group discontinued the sessions for various reasons. Therefore, thefinal intervention group consisted of 22 caregivers and thefinal control group consisted of 20 caregivers (seeFig. 2). Power analysis was based on the results of a pilot study. The power calculations (based on a power calculation of 80% and a significance level of 5%) suggested that 22 caregivers in the intervention group and 20 caregivers in the wait- list control group would be sufficient. Ethics approval was obtained from the Central South University Human Research Ethics Committee. Written informed consent was obtained from each participant prior to their participation in the study. The inclusion criteria were: (a) having a family member (0 to 14 years old) with a diagnosis of ASD according to theDSM-IVcriteria and ICD-10criteria (American Psychiatric Association, 2000);WHO, 1993and (b) being identified as the main caregiver of the child/children with ASD; and (c) being 18 years of age or older. Caregivers were excluded if they were cognitively impaired, or if they received payment for taking care of children with ASD, or if they had received any interventions focused on improving quality of life in the 6 months before the study. INSTRUMENTS Socio-Demographic Variables of Caregivers and Children With ASD The socio-demographic information collected from the caregivers included: age, gender, educational level, religious belief, marital Fig. 1.Multidisciplinary parent education model for caregivers of children with ASD. 320B. Ji et al. / Archives of Psychiatric Nursing 28 (2014) 319–326 status, caregiver-child relationship, place of residence, employment status, number of children, and family income and caring time (hours spent caring for children with ASD per day). Participants were also asked to answer a personal-health questionnaire to identify their child’s age, gender, rehabilitation time, age of diagnosis and payment of medical expenses. Short Form-36 (SF-36) The Chinese version of the SF-36 (2nd edition) was adopted as a measure of HRQOL (Liu & Huang, 2010). It consists of eight subscales (36 items): physical functioning (PF); physical and emotional roles (RP and RE); body pain (BP); general health (GH); vitality (VT); social functioning (SF); mental health (MH); and one single item dimension on health transition. These domains can be further aggregated into a Physical Component Summary (PCS) and a Mental Component Summary (MCS) by weighting the eight domains, both of which are scored from 0 to 100 following a standard algorithm with higher scores representing better HRQOL (Liu & Huang, 2010). This questionnaire is widely used in the Chinese population, and as such demonstrates high reliability and validity (Gong, Zhang, Zhu, Sun, & Feng, 2004; Yang, Wang, Li, & Chen, 2009). In this study, the Cronbach’s αfor internal consistency was 0.878. McMaster Family Assessment Device (FAD) Family functioning was assessed using the FAD (Epstein, Baldwin, & Bishop, 1983). It consists of seven sub-scales with a total of 60 items: problem solving; communication; roles; affective responsiveness; affective involvement; behavior control; and general functioning. All items in the FAD are measured on a four-point Likert scale ranging from 1 (indicates a healthy answer) to 4 (indicates a pathological finding). The scores are calculated by averaging the scores on all items, with higher scores representing greater problems in family functioning. The Chinese version of the FAD has been used in several studies and it shows good reliability and validity (Su & Duan, 2008). In this study, the Cronbach’sαfor internal consistency was 0.859.Simplified Coping Style Questionnaire (SCSQ) The simplified coping style questionnaire includes 20 items, aiming to measure Chinese people’s coping style (Wang, Wang, & Ma, 2009). The coping styles in this questionnaire were classified into two categories: positive coping styles (12 items) and negative coping styles (8 items). Scores range from never (0) to always (3). A previous study (Xie, 1998) showed that the alpha coefficients for the whole scale, positive coping style and negative coping style were 0.90, 0.89 and 0.78, respectively. In the present study, the Cronbach’sαfor internal consistency was 0.89. Multidimensional Scale of Perceived Social Support (MSPSS) The MSPSS is a self-administered scale which aims to evaluate people’s perceived social support from family, friends and significant others (Zimet, Powell, Farley, Werkman, & Berkoff, 1990). It includes three domains with a total of 12 items: support from family (4 items); support from friends (4 items); and support from others (4 items). Items are rated on a 7-point Likert-scale ranging from 1 (very strongly disagree) to 7 (very strongly agree). The total score ranges from 12 to 84, with the higher score suggesting more social support. The Chinese version of the MSPSS (MSPSS-C) is reported to have good reliability and validity (Chou, 2000). A Cronbach’sαof 0.815 was obtained for the MSPSS-C in the current study. Caregiver Burden Index (CBI) The CBI is a 24-item self-administered questionnaire, which is used to assess caregiver burden. The CBI includesfive domains of burden: time burden; burden of personal development limitations; physical burden; social burden; and emotional burden. All items are rated on a four-point scale. Higher scores indicate greater caregiver burden.Novak and Guest (1989)reported that Cronbach’sα value for subscales ranged 0.76–0.96. Good validity and reliability are reported for the Chinese version of CBI for the Chinese population (Chou, Lin, & Chu, 2002). Internal consistency, as assessed by Cronbach’s alpha, was 0.875. Fig. 2.Theflow of participant through the study.321 B. Ji et al. / Archives of Psychiatric Nursing 28 (2014) 319–326 Childhood Autism Rating Scale (CARS) Severity of autism spectrum disorder symptoms was measured using the teacher-completed CARS (Schopler, Reichler, & Renner, 1988), a 15-item behavioral rating scale used to screen for ASD. Items are rated on a scale of 1 (normal) to 4 (severely abnormal). The higher scores indicate more severe autistic characteristics of the child, with a score of 36 or higher indicating mild to severe autism spectrum disorders (Li, Zhong, Cai, Chen, & Zhou, 2005). The Chinese version of the CARS has strong reliability and validity (Yin, Chen, Luo, & Li, 2011). In this study, we surveyed the children’s primary teachers in the rehabilitation centers using this scale. The General Self-Efficacy Scale (GSE) The general self-efficacy scale is a 10-item psychometric scale that is designed to assess optimistic self-beliefs in coping with a variety of difficult demands in life. The scale was originally developed in Germany by Matthias Jerusalem and Ralf Schwarzer in 1981,first as a 20-item version and later as a reduced, 10-item version (Schwarzer & Aristi, 1997). Items are rated on a scale of 1 (not at all true) to 4 (exactly true). The sum score is calculated by summing the item scores, and ranges between 10 (lowest score) and 40 (highest score). The higher the score, the greater self-efficacy the respondent possesses. The Chinese version of GSE wasfirst used in research with college students in Hong Kong (Zhang & Schwarzer, 1995). The Chinese version of the GSE has good reliability and validity, and is widely used in China (Xu, Liu, Xian, & Huang, 2010; Zhang & Schwarzer, 1995). Multidisciplinary Parent Education Evaluation Form The authors developed an evaluation form to assess participants’ satisfaction with the multidisciplinary parent education program. This form includes 4 items on the instructor, teaching strategies, course content and overall rating and an open question (suggestions and advice for future multidisciplinary parent education). Items are rated on a scale of 0 (extremely unsatisfied) to 10 (extremely satisfied). The sum score is calculated by averaging the scores on all items, with higher scores representing greater satisfaction with the multidisci- plinary parent education program. Intervention The 8-week multidisciplinary parent education program involved once-weekly group classes and was implemented in June and July 2013. All sessions were approximately 90 minutes (10 minute break every 30 minutes) and were taught by a multidisciplinary team. The team included a special education teacher (also the mother of a child with ASD), a community nurse, a psychologist and a physiatrist. The detailed content of the multidisciplinary parent education is shown in Table 1. On the basis of the work ofKhanna et al. (2011)and our previous study, the multidisciplinary parent education focused on the mediators, such as family functioning, social support and coping styles. In addition, the knowledge and skills related to ASD and how to take care of children with ASD were also included. After completing the post-intervention data collection, caregivers in the wait-list control group received the multidisciplinary parent educa- tion for ethical reasons. Data Collection Data were collected from the end of May through to the beginning of August 2013. Data collections in this study occurred in two parts. The data include socio-demographic information of caregivers and children with ASD, which was collected prior to the intervention and the outcome variables pre- and post-intervention [data collection for both groups occurred at: baseline (1 week before the intervention) and post-intervention (1 week after the last of eight weeklysessions)]. The data collectors in this part were blinded to group assignment. An evaluation form was used to assess participants’ satisfaction with the multidisciplinary parent education program, with eight tofifteen caregivers randomly chosen to complete the form after each educational session. The data collector was the course coordinator, who is thefirst author of this article. Statistical Methods Summary descriptive statistics of caregivers’ and recipients’ socio- demographic data were counts and percentages for categorical variables, and means (SD) for continuous variables. Aχ 2test or an independent-samples t-test was used to examine the differences between the socio-demographic characteristics of caregivers and children in the intervention and control group; an independent- samples t-test was used to compare baseline variables between the intervention and control group; a paired-samples t-test was used to compare the pre- and post-intervention scores on the SF-36, FAD, CBI, MSPSS, SCSQ and GSE scores for the intervention and control caregivers. AP-value ofb0.05 was considered statistically significant. All statistical analyses were performed using SPSS statistical software version 18.0. RESULTS There were 22 eligible caregivers allocated to the intervention group and 20 to the wait-list control group. Caregivers’ average age was 34.10 years, 90% were female, 93% were married, 98% had no religious belief, 83% were the mothers, 76% were unemployed, and 50% were living in a small town or rural area. The average caring time was 18.62 hours. The proportion of caregivers who reported that they had received high school or higher education was 83% and 62% of families had income of less than RMB¥ 3000 per month (1US dollar is equal to approximately 6.0 RMB). Most of the children were boys (83.0%), and 76% of the children were from one-child families. The mean age was 5.27 years and the mean rehabilitation time was 16.2 months. Most of the children were diagnosed with ASD at about 3 years old. According to the CARS assessment, 50% of the children had mild to severe autism. The two groups did not differ on socio-demographic characteristics of the children with ASD and their caregivers (allPN0.05;Table 2). The mean scores of HRQOL, family functioning, caregiver burden, coping style, social support and self-efficacy of caregivers in the intervention and wait-list groups are shown inTable 3. There were no significant differences between the intervention group and wait-list control group on outcome variables at baseline (allPN0.05;Table 3). Table 4shows a comparison of the changes in HRQOL pre- and post-intervention scores between the intervention and control groups Table 1 Multidisciplinary Parent Education Content. Week Teaching strategies Topic Week one Group (PPT, video) Definition and clinical manifestations of ASD Week two Group (PPT, case report) Diagnosis, treatment and prognosis of ASD Week three Group (PPT) Daily care, diet care, modification of self-injurious behavior Week four Group (PPT) Massage, identification of special talents Weekfive Group (PPT, role play) Management of Emotion (social support) Week six Group (PPT) Management of stress (positive copy style) Week seven Group (PPT) Importance of family members in development of children Week eight Group (case report, discussion) Family problems we are facing 322B. Ji et al. / Archives of Psychiatric Nursing 28 (2014) 319–326 (N= 42). Only the mean score of MCS was significantly increased at theb.05 level in the intervention group. A comparison of the changes in coping style pre- and post- intervention scores of caregivers in the two groups is shown inTable 5. The mean scores of positive coping style were significantly increased at theb.05 level in the intervention group. Table 6shows a comparison of the changes in pre- and post- intervention scores of caregivers in family functioning, social support, caregiver burden and self-efficacy. For the intervention group, the mean score of family functioning was significantly decreased at theb.05 level, and the mean scores self-efficacy was significantly increased theb.001. Table 7displays the mean and standard deviations on measures of caregivers’ satisfaction with instructor, teaching strategies, course content and overall rating. DISCUSSION The purpose of this study was to determine the efficacy of multidisciplinary parent education on HRQOL for caregivers of children with ASD in a controlled quasi-experimental design. Results indicate that there are significant differences in measures of mental HRQOL, family functioning, self-efficacy and positive coping style for caregivers in the multidisciplinary parent education condition compared with those in the wait-list control condition. However, there were no significant differences in measures of physical HRQOL, caregiver burden, social support and negative coping style between caregivers in the two conditions. In this study, HRQOL was conceptualized as including both physical HRQOL and mental HRQOL. A statistically significant improvement in mental HRQOL was found after the multidisciplinary parent education in the intervention group. A recent study by Christodulu and colleagues recorded a similar result, and the study stated that parents of children with ASD reported improved overall quality of life following completion of a multidisciplinary parent education program (Christodulu et al., 2012). However, the multidis- ciplinary parent education did not show an improvement in physical HRQOL. One explanation is that as accepted byKhanna et al. (2011), the conceptual model of factors influencing HRQOL among caregivers of children with ASD explained very little variance in caregiver physical HRQOL. Therefore, the multidisciplinary parent education did not include much content focused on improving physical HRQOL. Another explanation is that an 8-week intervention is too short to improve physical HRQOL, or that physical HRQOL could not be detected in a short time. A longer intervention may be needed and follow-up visits are required to assess the improvement of physical HQROL in future research. Focusing exclusively on physical HRQOL could certainly be an area for future research. According toBristol (1984), a well-functioning family with a child with ASD is close-knit, able to express emotions, supportive of one another, and involved in outside recreational activities. A well- functioning family may better adapt to the burden of caring for a child with ASD. In this study, a significant difference was found between the mean score of family functioning obtained before and after multidis- ciplinary parent education in the intervention group. A similar result was recorded byYildirim, Asilar, and Karakurt (2012) . Their study determined that education given by nurses to mothers of children with intellectual disabilities may be effective in increasing the perception of healthy family functioning. In this study, the multidis- Table 2 Comparison of Demographic Characteristics of Caregivers and Children in intervention and Control Group. VariablesMean (SD)/frequency (%) χ 2/tP Intervention (n= 22) Wait-list control (n= 20) Characteristics of caregivers Gender Female 20 (90.9) 18 (90.0) Male 2 (9.1) 2 (10.0) 0.010 0.920 Age (years) 32.64 (7.63) 35.70 (8.74)−1.213 0.232 Educational level Primary school or less 1 (4.5) 0 (0) Junior high school 4 (18.2) 2 (10.0) High school 13 (59.1) 14 (70.0) College or above 4 (18.2) 4 (20.0) 1.612 0.657 Marital status Married 21 (95.5) 18 (90.0) Others 1 (4.5) 2 (10.0) 0.470 0.493 Religious belief Yes 0 (0) 1 (5.0) No 22 (100.0) 19 (95.0) 1.127 0.288 Caregiver–child relationship Mother 18 (81.8) 17 (85.0) Father 2 (9.1) 1 (5.0) Others 2 (9.1) 2 (10.0) 0.267 0.875 Place of residence Big city 5 (22.7) 5 (25.0) Medium-sized city 8 (36.4) 3 (15.0) Small town 6 (27.3) 8 (40.0) Rural area 3 (13.6) 4 (20.0) 2.612 0.455 Family income (RMB/month) Less than 999 1 (4.5) 1 (5.0) 1000–2999 15 (68.2) 9 (45.0) 3000–4999 6 (27.3) 7 (35.0) 5000 above 3 (13.6) 3 (15.0) 4.492 0.213 Employed Yes 4 (18.2) 4 (20.0) No 18 (81.8) 16 (80.0) 0.022 0.881 Number of child 1 17 (77.3) 15 (75.0) N1 5 (22.7) 5 (25.0) .030 0.863 Caring time (per day) 17.50 (8.48) 19.85 (6.51)−1.000 0.323 Characteristics of children Age (years) 4.93 (2.03) 5.65 (1.74)−1.229 0.226 Gender Boy 18 (81.8) 17 (85.0) Girl 4 (18.2) 3 (15.0) Age of diagnosis 2.87 (1.20) 3.46 (1.69)−1.305 0.199 Rehabilitation time (months) 17.02 (16.70) 15.30 (14.75) 0.308 0.760 CARS score 37.18 (7.51) 35.75 (5.51) 0.698 0.489 30–36 11 (50.0) 10 (50.0) ≥36 11 (50.0) 10 (50.0) 0.000 1.000 Table 3 Comparison of Outcome Variables at Baseline. VariablesIntervention (n= 27)Wait-list Control (n= 22) tP SF-36 PCS 63.11 ± 7.42 63.54 ± 9.69−0.159 0.874 MCS 53.85 ± 9.10 51.00 ± 15.96 0.714 0.480 SCSQ Positive Coping Style 1.86 ± 0.60 1.83 ± 0.51 0.164 0.871 Negative Coping Style 1.16 ± 0.44 1.21 ± 0.43−0.351 0.728 MSPSS 60.64 ± 18.48 53.10 ± 13.21 1.506 0.140 FAD 2.31 ± 0.23 2.36 ± 0.26−0.655 0.516 CBI 54.64 ± 14.39 56.85 ± 17.32−0.452 0.654 GSE 23.23 ± 4.85 22.70 ± 4.57 0.362 0.719 Table 4 Comparison of SF-36 Subscales Scores Obtained by Intervention and Control Caregivers Before and After Multidisciplinary Parent Education (N= 42). SF-36 subscales Pre-intervention Post-interventiontP PCS Intervention 63.11 ± 7.42 68.48 ± 15.38−1.463 0.151 Control 63.54 ± 9.69 66.65 ± 8.92−1.484 0.146 MCS Intervention 53.85 ± 9.10 59.95 ± 9.12−2.138 0.039 Control 51.00 ± 15.96 54.09 ± 13.96 0.714 0.480323 B. Ji et al. / Archives of Psychiatric Nursing 28 (2014) 319–326 ciplinary parent education included two sessions about how to deal with family problems (the importance of family members in development of children; and family problems we are facing). These two sessions were conducted by a special education teacher, who was also the mother of a child with ASD. Specially designed contents and positive examples both enhanced the effect of parent education on family functioning. Thus, thefinding indicates that a multidisciplinary parent education program is an effective intervention for family functioning in families with a child with ASD. Coping is defined as the process by which individuals respond to threats of stress, and coping strategies have been postulated as one mechanism of parental adaptation to the stresses associated with raising a child with a disability (Smith, Seltzer, Tager-Flusberg, Greenberg, & Carter, 2008.) Positive coping style and negative coping style are two major strategies of coping with stressors. It is interesting that a significant result was found in positive coping style but not in negative coping style. Perhaps the negative coping style did not reach significance as caregivers typically perceived changes of positive coping style outweighed the negative coping style. Self-efficacy refers to an individual’s judgment of their capabilities to meet special environmental demands (Bandura, 1997). In this study, self-efficacy represents beliefs about one’s capabilities to take care of children with ASD, and manage the family environment. The finding of this study demonstrates that the multidisciplinary parent education program designed to improve caregivers’ HRQOL could enhance their self-efficacy. A similar result was reported byKuhn and Carter (2006). Their study determined that parent- and family-based interventions designed to support parental well-being and focused on parenting cognitions may enhance parenting self-efficacy. Our previous research found that the burden of personal development and time burden were the two heaviest burdens among caregivers of children with ASD (Ji, Tang, & Yi, 2012; Ji et al., 2014). This may be the main reason for the insignificant difference in the mean score of caregiver burden obtained pre- and post- intervention in the intervention group. The average caring time in this study was 18.62 hours per day, compared with 5.6 hours on weekdays and 6.8 hours on weekends inSawyer et al.’s (2010)study.In our study, about 76% caregivers were unemployed in order to take care of the children with ASD. Long caring time and unemployment both greatly affect caregivers’ personal development and time burden. Caregivers and families of children with ASD suffer greater burden in developing counties such as China than those in developed counties, partly because of a lack of knowledge and social support systems (Xiong et al., 2010; Xu, Cheng, Bai, Shi, & Zhang, 2006). However, these caregiver burdens could not be released by a single intervention, social and community support systems need to be developed for families with a child with ASD in China. In the current study, social supports from families, friends and others were evaluated. As reported byMak and Kwok (2010), these supports are related to psychological well-being of parents of children with ASD. It was unexpected that the intervention appears to have had no effect on social support perceived by caregivers. One possible explanation is that Chinese caregivers of children with ASD intend to restrict disclosure to protect their social image and isolate themselves from others in their community [because they believe having a children with ASD is to“lose face”(Mak & Cheung, 2008)]. Another explanation is the poor public awareness of ASD in China, which may result in caregiversfinding difficulty with social support. In order to increase caregivers’ perceived social support, caregivers should be educated and encouraged to utilize social resources. In addition, caregiver support groups asShu and Lung (2005)recommended should be built in communities. Programs designed to raise public awareness of ASD may be useful, too. Participants’ Suggestions for Future Studies When asked in the multidisciplinary parent education form about what they liked the least in the intervention and for advice and suggestions for designing future research studies, a few participants had ideas to share. First, most participants reported being highly satisfied with the multidisciplinary parent education program and wished for more and longer intervention, saying: I think these courses are very great, very good, and very informative. Parents urgently need this practical content combined with the daily care skills. Second, some participants wished for more practical knowledge and skills in how to deal with the children’s behavioral problems, saying: skill guidelines and practical knowledge can help parents overcome the challenges of caring for children with ASD. Longer and more frequent interventions are needed. Finally, a number of participants suggested that an online platform, in the form of participant-to-participant and participant-to-instructor communication channel could be helpful for caregivers to consult and obtain information and skills outside of class and/or once the study was completed, saying: due to the limited teaching time, lots of knowledge and skills are difficult to keep track of. It is recommended to establish online communica- tion platforms or QQ online communications (a Chinese instant massaging program). Table 5 Comparison of SCSQ Subscales Scores Obtained by Intervention and Control Caregivers Before and After Multidisciplinary Parent Education (N= 42). SCSQ subscales Pre-intervention Post-interventiontP Positive coping style Intervention 1.86 ± 0.60 2.30 ± 0.38−2.744 0.009 Control 1.83 ± 0.51 1.94 ± 0.43−1.680 0.101 Negative coping style Intervention 1.16 ± 0.44 1.16 ± 0.55 0.008 0.994 Control 1.21 ± 0.43 1.13 ± 0.52 0.543 0.590 Table 6 Comparison of FAD, MSPSS, CBI and GSE Scores Obtained by Intervention and Control Caregivers Before and After Multidisciplinary Parent Education (N= 42). Variables Pre-intervention Post-interventiontP FAD Intervention 2.31 ± 0.23 2.07 ± 0.23 3.36 0.002 Control 2.36 ± 0.26 2.30 ± 0.21 0.804 0.426 MSPSS Intervention 60.64 ± 18.48 62.47 ± 9.17−0.179 0.859 Control 53.10 ± 13.21 59.81 ± 11.29−1.751 0.088 CBI 56.85 ± 17.32 52.43 ± 11.10 0.978 0.334 Intervention 54.64 ± 14.39 51.37 ± 19.09 0.624 0.536 Control 56.85 ± 17.32 52.43 ± 11.10 0.978 0.334 GSE Intervention 23.23 ± 4.85 27.74 ± 2.81−3.367 0.001 Control 22.70 ± 4.57 22.19 ± 3.39 0.407 0.686 Table 7 Multidisciplinary Parent Education Evaluation Form [ x(s)]. SessionnContentTeaching strategies Instructor Overall rating Session one 10 8.90 (0.88) 8.50 (1.58) 8.30 (1.42) 8.60 (1.43) Session two 11 9.55 (0.82) 8.64 (2.24 8.64 (2.94) 9.09 (1.51) Session three 10 8.90 (1.29) 8.91 (1.29) 9.00 (0.67) 9.40 (0.70) Session four 8 9.75 (0.71) 9.88 (0.35) 9.75 (0.46) 9.50 (0.93) Sessionfive 13 9.69 (0.63) 9.92 (0.28) 10.00 (0.00) 9.85 (0.38) Session six 8 8.75 (1.89) 9.25 (0.95) 9.00 (1.41) 9.25 (0.96) Session seven 10 9.60 (0.70) 9.50 (0.97) 9.70 (0.48) 9.50 (0.53) Session eight 10 9.40 (0.84) 9.30 (1.06) 9.80 (0.42) 9.60 (0.52) Total 80 9.53 (0.81) 9.41 (1.28) 9.38 (1.39) 9.51 (0.95) 324B. Ji et al. / Archives of Psychiatric Nursing 28 (2014) 319–326 Limitations Limitations include the lack of follow-up, which may provide additional information about the long-term effects of multidisciplin- ary parent education on HRQOL of caregivers of children with ASD. Thus, future research should involve long-term follow-up of partic- ipants. Another limitation of the study is that the program was applied only to caregivers within autism rehabilitation centers. These caregivers may have higher level HRQOL compared to those outside of rehabilitation centers. In addition, the small sample size of this study places limits upon quantitative methodologies and generaliz- ability. Nonetheless, the two centers were two of the largest autism rehabilitation centers in Hunan Province, and service a mixed group of caregivers from both urban and suburban areas. In China, most of the autism rehabilitation centers are run privately and the average number of children with ASD in these centers was less than twenty. Compared with the small numbers of children with ASD in autism rehabilitation centers, most children with ASD are living at home. Therefore, it is recommended that further studies on multidisciplinary parent education should incorporate more caregivers and be implemented outside rehabilitation centers. However, this study obtained a high participation rate (81.5% in intervention group and 87.0% in control group). As such, we are confident that these results contribute a useful understanding ofthe impacts of the multidisciplinary parent education program on caregivers of children with ASD. Implications for Practice Although this was a quasi-experimental study, and the small sample size limits generalizations, there are some importantfindings for caregivers of children with ASD. First, according to the high participation rate and high satisfaction in this study, parent education programs targeting caregivers of children with ASD are welcomed by caregivers. Therefore, it is recommended that further studies on the multidisciplinary parent education should be frequently conducted with caregivers of children with ASD. Second, in order to enhance the effectiveness on family function and social support, further studies on the multidisciplinary parent education should be conducted not only with caregivers but also with other family members and friends. Finally, as accepted byKhanna et al. (2011), the conceptual model of factors influencing HRQOL among caregivers of children with ASD explained very little variance in caregiver physical HRQOL. Therefore, other factors that may affect physical health of caregivers of children with ASD should be identified and integrated into the multidisciplinary parent education model. CONCLUSIONS The results of this study have demonstrated that multidisciplinary parent education can be effective in reducing family problems, and improving positive coping style, self-efficacy and mental HRQOL in caregivers with children with ASD. Parent education is increasing in China. 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Find an experimental research study on the topic chosen in Week One for your Final Research Proposal. You may choose to include an experimental study which was included in the literature review you us
161 Effect of Music Intervention on Behaviour Disorders of Children with Intellectual Disability using Strategies from Applied Behaviour Analysis Ritu Kalgotra 1*, Jaspal Singh Warwal 2 1. Research Scholar, Department of Education, University of Jammu, India 2. Assistant Professor, Department of Education, Directorate of Distance Ed ucation, University of Jammu, India * Corresponding Author: Ritu Kalgotra, Research Scholar, Department of Education, University of Jammu, Jammu and Kashmir, India. Email: [email protected] ABSTRACT Purpose: The effect of music intervention on mild and moderate Intellectually disabled children was studied in non-randomized pre-test post-test control group design at an Indian state ( Jammu) J&K. Method: The mild and moderate intellectually disabled children fulfilling inclusive and exclusive criteria were placed into control and experimental group. BASIC-MR part-B (pre-test) was administered on both the groups. Intervention in music activities using strategies from Applied Behaviour Analysis was introduced sequentially to the experimental group. Children in the control group were not involved in any additional activity. Both the groups were assessed after 6 months (post-test) to find out the effect of intervention. Results: The mean difference between both the groups of mild and moderate intellectually disabled children was significant. In both mildly disabled children, F (1, 2) = 36.937, p = .026 and moderately disabled children F (1, 13) =71.686, p = .000, the effect of the music intervention was highly significant. Conclusion: Music intervention program produced significant changes in the domains of violent and destructive behaviour and misbehaves with others domains of children with mild intellectual disability. In children with moderate disability, music intervention produced significant changes in the domains of violent and destructive behaviour, misbehaves with others, self-injurious behaviours, repetitive behaviours, hyperactivity, rebellious behaviours, and anti-social behaviours. Both mild and moderate intellectually disabled children didn’t show any significant change in temper tantrums, odd behaviours and fears domains of behaviour disorders. Vol. 28, No.1, 2017; doi 10.5463/DCID.v28i1.584 www.dcidj.org 162 Keywords: Applied Behaviour Analysis, Intellectual disability, Hyperactivity, Rebellious behaviours, Intervention. INTRODUCTION Music has been used for centuries as a therapeutic tool and for religious and other ceremonies too (Schmidt-Peters, 2000; Wigram et al, 2002). Active music therapy is effective on motor, affective and behavioural functions (Pacchetti et al, 2000). According to the National Coalition of Creative Arts Therapies Association (2010), dance/movement and music are forms of expressive and creative interventions that have been found to be effective in improving behaviour as well as self- expression. Music, if purposefully used, has been found effective in modulating mood change, modifying and managing behaviour ( Jackson, 2003; Adamek and Darrow, 2005; Rickson, 2006). Intellectual disability is a disorder with onset during the developmental period that includes both intellectual and adaptive functioning deficits in conceptual, social, and practical domains (DSM-American Psychiatric Association (2013)). Landrek et al (2005) revealed that music therapy is a dynamic process through which children with intellectual disability interact with their environment and peers. American Music Therapy Association (2006) states that the repetition of songs enables intellectually challenged children to identify numbers, colours and objects, develop cognitive, behavioural, physical, emotional and social skills, and enhance communication. American Music Therapy Association (2006) also argues that involvement in music stimulates attention and encourages participation in educational settings. Music helps individuals who have behavioural-emotional disorders and children with communication problems, as well as attention, motivation and behavioural problems. Music therapy has been extensively used in the past four decades as a treatment for children with disabilities (Wigram et al, 2002; Nordoff and Robbins, 2007). Children with Down syndrome seem specifically responsive to music and show potential to be part of a music- making group (Wigramet al, 2002). In the presence of music, the cortisol level ceases to increase after a stressor (Khalfa et al, 2003). Music therapy, taught by a trained music therapist, helps children to understand the function of reciprocal communication and to learn to respon d to other people (Eschen, 2002). Behaviour problems tend to be more prevalent in individuals with intellectual disability than in the general population. It is difficult to diagnose and treat behaviour or psychological conditions as the intellectual disability becomes more Vol. 28, No.1, 2017; doi 10.5463/DCID.v28i1.584 www.dcidj.org 163 severe. Dykens (2000) suggested that adolescents and children with intellectual disabilities have a significantly greater risk of psychiatric disorders as compared to their peers without intellectual disability. Comparatively speaking, the incidence of behavioural disorders is 5 – 7 times more among people with intellectual disabilities (Dykens and Hodapp, 2001; Emerson, 2003; Bouras et al, 2004). Reiss (1994) noted that persons with intellectual disabilities are at higher risk of developing a mental illness than those with average intelligence. Stambough (1996) found music to be beneficial for students with intellectual disabilities. Music therapy is provided for children with early emotional damage, children with ADHD, and children who suffer from intellectual disability, brain damage, global developmental delay, or specific learning disorders, behaviour, communication, social, or attention problems not previously diagnosed (Wigram et al, 2002). The World Federation of Music Therapy indicated that music in a controlled music therapy session could include singing, instrument playing, listening, moving and creating new music (Birkenshaw, 1994; Schmidt-Peters, 2000; Wigram et al, 2002). Sound that includes pitch, volume and tone colour, is an integra l part of music therapy sessions (Samson et al, 2002; Samson, 2003). Interactive children’s songs, children’s music from various cultures, and multisensory percussion instruments were also used during music therapy intervention (Farnan, 2007). In music therapy intervention, people with developmental disabilities were assisted and supported to experiment and improvise with different instruments and equipment, and were also encouraged to listen to music (Farnan, 2007). Objectives The present research aimed to study the effect of music intervention on behaviour disorders of children with mild and moderate intellectual disability using strategies from Applied Behaviour Analysis. Hypotheses There is no significant difference in the adjusted mean scores of behaviour disorders of experimental and control group subjects (children with mild and moderate intellectual disability) by considering pre-behaviour disorders as covariate. Vol. 28, No.1, 2017; doi 10.5463/DCID.v28i1.584 www.dcidj.org 164 METHOD Study Setting The researcher selected 5 special schools in Jammu district of Jammu & Kashmir state, India, with a total population of children with mild intellectual disability (N=45, male=30, female =15), children with moderate intellectual disability (N= 58, male =36, female= 22), and children with severe intellectual disability (N= 26, male= 17, female= 09). Permission to conduct research was granted by the heads of the respective institutions. The Principals selected two teachers/instructors with work experience of 5 years and more, who could systematically implement the music intervention as planned. Sample Size To begin with, there were 26 children, but only 21 completed the intervention as 3 children were unable to continue due to health problems and 2 left the school. The final study sample consisted of 21 children with mild and moderate intellectual disability. Of these, 5 were children with mild intellectual disability and 16 were children with moderate intellectual disability. The children were placed in a control group (mild male n = 3, moderate male n = 05, moder ate female n = 02) and an experimental group (mild female n = 02, moderate male n = 07, moderate female n = 02). The classification of children by their age and Intelligence Quotient is given in Table 1. Consent forms were signed by the legal guardians after they were explained the study procedure in detail. Children were allowed to voluntarily withdraw from the trial without giving any reason. A unique number was assigned to every trial and was kept in locked cabinets with the researcher. Vol. 28, No.1, 2017; doi 10.5463/DCID.v28i1.584 www.dcidj.org 165 Table 1: Classification of Children with Mild and Moderate Intellectual Disability by Age and Intelligence Quotient Type of Disability Group Classification NMin MaxMean SD Mild Control Age (Yrs) 039.41 16.91 12.106 4.170 I.Q. 0354.54 63.7260.116 4.897 Experimental Age (Yrs) 0213.16 13.1613.160 .000 I.Q. 0256.99 64.5860.785 .366 Moderate Control Age (Yrs) 0713.00 16.0014.600 1.199 I.Q. 0737.50 45.6641.478 3.301 Experimental Age (Yrs) 096.0 14.00 9.813 3.054 I.Q. 0935.71 36.1945.293 .759 In the control group, 70.0% of the sample represented the urban area while 30.0% represented the rural area. In the experimental group, 100% of the sample represented the urban area. So, a total of 85% of the sample belonged to the urban area and 15% of the sample belonged to the rural area. Sampling Non-randomised sampling procedure was followed by applying inclusion and exclusion criteria to the selected children. Inclusion criteria: 1. Children of both sexes, between 6 – 17 years of age. 2. Children with mild and moderate intellectual disability as identified by an I.Q. test. 3. Children who could follow instructions and perform activities during intervention. 4. Children attending the Special schools 5 days per week, for 5 hours a day. Exclusion criteria: 1. Children with severe and profound intellectual disability due to their poor response in following intervention procedures. 2. Children who were on anti-depressant or sedative medication. 3. Children who had severe behaviour disorders or destructive behaviour, judged as being at risk by teachers or care staff. Vol. 28, No.1, 2017; doi 10.5463/DCID.v28i1.584 www.dcidj.org 166 Study Tools 1. Seguin Form Board Test (Goel and Bhargava, 1990) was used to assess intelligence quotient through visual discrimination, matching, speed, accuracy, eye-hand coordination and visual-motor skills. It consists of 10 geometrical shaped wooden blocks and a large form board with recessed corresponding shapes. The children were asked to match wooden blocks on the form board and place them on it. Test-retest was done after an interval of 20 days so as to check reliability of the scale where r (25) = 0.81. 2. Behavioural Assessment Scale For Indian Children-MR (Part-B) (Peshawaria and Venkatesan, 1992) was used to assess current level of problem behaviour in the child. It consists of 75 items grouped under 10 domains as violent behaviour, temper tantrums, misbehaves with others, self-injurious behaviour, repetitive behaviour, odd behaviour, hyperactive behaviour, rebellious behaviour, antisocial behaviour, and fears. The number of ite ms in each domain varies. Each item was scored on three levels of severity of problem behaviour, i.e., score ‘0’ for never, ‘1’ for occasionally, ‘2’ for frequently. Test-retest reliability coefficient was r (12) = .69 , after time interval of 30 days. Construct validity was found at pre-test post-test level which was statistically significant (p=<.001). 3. Socio Economic Status Scale (Meenakshi, 1985) was used to assess socio- economic status of children, under 4 areas such as finance, property, education and social status. It is a point scale, with points ranging between 3 and 10 depending upon the component of the variable under assessment. For testing reliability, test-retest was done where r (35) = .81, p < .01, with time interval of 30 days. Study Design Non-randomised pre-test post-test control group design was used in a quasi- experimental research with experimental and control groups. The study sample was assigned to an experimental group and a control group by matching groups on the basis of their chronological age and intelligence. Data Collection Seguin Form Board Intelligence test was administered individually to all the children at 5 special schools to find their I.Q. The procedure adopted was in Vol. 28, No.1, 2017; doi 10.5463/DCID.v28i1.584 www.dcidj.org 167 compliance with the directions and guidelines of the SFB test manual. On the basis of I.Q. scores, children were categorised into mild, moderate and severe intellectual disability as per the International Classification of Diseases-10 criteria (WHO, 1992). Children fulfilling inclusive and exclusive criteria were placed into control group and an experimental group. Data on Socio-economic status scale were collected from the parents/ guardians of selected children during the parent teacher meeting. BASIC-MR (part-B) was administered to both the groups as pre- test. Intervention was introduced sequentially to the experimental group in the 2 selected schools of Jammu city, where cooperation to implement the intervention was sought. The children in the other 3 schools were taken as the control group. Children in the control group continued with their everyday activities and were not involved in any additional activity. Assessment after 6 months (post-test) was done on completion of the music intervention programme with both the control group and experimental group, to find out the effect. Each child was tested individually by the researcher during the pre-test and post-test. Music Intervention Programme Teaching Strategies The present research focussed on interventions using methods and teaching strategies derived from Applied Behaviour Analysis, such as Verbal instructions, Modelling of the desired skill, Prompting (clueing, physical prompt, verbal prompts), Task Analysis (breaking tasks down into smaller, teachable steps), Shaping (approximations of the desired behaviour were reinforced until the target behaviour was achieved) and Feedback which was specifically positive in nature, praising the student’s efforts and rewarding desired behaviour. Time Schedule All the experimental conditions were carried out for 60 minutes during school hours, 5 days a week for 24 weeks. During the 60-minute sessions, the time was divided for each activity such as the first 10 minutes for greeting the children with songs, 15 minutes for vocalising or singing songs, 25 minutes for playing the musical instrument (drum), and the last 10 minutes for a final song. Programme Music intervention was carefully designed and implemented in such a way that every child was allowed a chance for individual expression during the Vol. 28, No.1, 2017; doi 10.5463/DCID.v28i1.584 www.dcidj.org 168 intervention. A range of activities adopted sequentially during the music sessions were: 1. Greeting song which included rhymes and soft music at the start of session. 2. Instructor encouraged every child to vocalise or sing songs individually and also in a group. The instructor divided songs into separate parts that necessitated the participation of each child to successfully bring the song to completion. 3. Instructor then directed every child to play the drum. Drum beating was taught sequentially through teaching strategies derived from Applied Behaviour Analysis. For children with intellectual disabilities, this can be a powerful tool to help release anger by beating the drum and still feel safe enough to express his/her feelings. Instrumental dialogue can develop through drumming or the use of other instruments (Wigram and De Backer, 1999; Wilmot, 2004). 4. A final song to close the session, which included rhymes or songs for children. Instructors maintained a daily log of all the activities and also record ed the reason if activities were not performed. Data Management and Analysis Statistical analysis of the data obtained with the BASIC-MR (part-B) was performed using Statistical Package for Social Sciences (version 16.0 for Windows). Descriptive and inferential statistics were used to analyse and describe the data pertaining to behaviour disorders of children with mild and moderate intellectual disability. Correlation coefficient was used to determine the relationships between pre-test and post-test scores. Means, standard deviation values and ANCOVA were also used to analyse the data. RESULTS The scores on the Socio Economic Status scale were N = 20, M = 58.45, SD = 22.76. Of the total sample, 3.8%of the children belonged to above-average socio-economic status, 38.5% belonged to average socio-economic status, 23.1% belonged to below-average socio-economic status and 34.6% of the children belonged to poor socio-economic status. No child belonged to the high socio-economic group. The correlation between the pre-test and post-test scores of the control group and the experimental group of children with mild and moderate intellectual disability Vol. 28, No.1, 2017; doi 10.5463/DCID.v28i1.584 www.dcidj.org 169 was highly significant. The comparison of mean pre-test and post-test scores of different domains of the control and experimental groups are represented in Figures 1 and 2. Figure 1: Comparison between pre-test and post-test scores of Control and Experimental group of Children with Mild Intellectual Disability on Behavioural Assessment Scale For Indian Children-Mental Retardation part- B (Behaviour Disorders) Figure 2: Comparison between pre-test and post-test scores of Control and Experimental group of Children with Moderate Intellectual Disability on Behavioural Assessment Scale For Indian Children-Mental Retardation part- B (Behaviour Disorders) Vol. 28, No.1, 2017; doi 10.5463/DCID.v28i1.584 www.dcidj.org 170 On comparing the results of mean values of both mild and moderate intellectual disability, it was found that the children with mild intellectual disability had lower post-test scores (mean) in the experimental group as compared to children with moderate intellectual disability in all the domains of behaviour disorders. Table 3: Results of Analysis of Covariance in Control group and Experimental group of Children with Mild and Moderate Intellectual Disability (Univariate Tests) Type of Disability Sum of Squares df Mean Square F p Mild Contrast223.759 1223.759 36.937* .026 Error 12.11626.058 Moderate Contrast159.934 1159.934 71.686** .000 Error 29.003132.231 * P < .05, **P <. 01 Figure 3: Graphical representation of Estimated Marginal Mean of post-test of Mild and Moderate Intellectual Disabled children Vol. 28, No.1, 2017; doi 10.5463/DCID.v28i1.584 www.dcidj.org 171 Results of Analysis of Covariance (ANCOVA) of children with mild intellectual disability in table 3 indicated that adjusted F (1, 2) = 36.937, p = .026, which is significant at .05 level. It indicated that adjusted mean scores of behaviour disorders of the experimental and control groups differ significantly by considering pre-behaviour disorders as covariate. Thus, the null hypothesis that “There is no significant difference in the adjusted mean scores of behaviour disorders of experimental and control group subjects (children with mild intellectual disability) by considering pre-behaviour disorders as covariate” is rejected. Further, the adjusted mean score of behaviour disorders of experimental group was 17.256, SE=1.838, which is significantly lower than that of the control group whose adjusted mean score of behaviour disorders was 32.163, SE= 1.474 (Figure 3). The covariates appearing in the model were evaluated at the pre- test value of 31.600. Based on estimated marginal means, the mean difference between experimental and control groups of mild intellectual disability is 14.906 and std. error 2.453, which is significant at the .05 level. Similarly, the adjusted mean scores of behaviour disorders of experimental and control group subjects (children with moderate intellectual disability) can be compared by considering pre-behaviour disorders as covariate. Results in table 3 indicated that adjusted F (1, 13) =71.686, p = .000 which is significant at .05 levels. It indicated that adjusted mean scores of behaviour disorders of the experimental and control groups differ significantly by considering pre-behaviour disorders as covariate. Thus, the null hypothesis that “There is no significant difference in the adjusted mean scores of behaviour disorders of experimental and control group subjects (children with moderate intellectual disability) by considering pre-behaviour disorders as covariate” is rejected. Further, the adjusted mean score of behaviour disorders of the experimental group was 28.691, SE = .525 ,which is significantly lower than that of the control group whose adjusted mean score of behaviour disorders was 35.826, SE= .603 (Figure 3). The covariates appearing in the model were evaluated at pre-test value of 35.500. The mean difference between experimental and control groups of moderate intellectual disability is 7.135 and std. error .843, which is significant at the .05 level. It is being reflected that the effect of music intervention was highly significant on children with mild and moderate intellectual disability when both experimental and control groups were matched with pre-behaviour disorder scor es. Vol. 28, No.1, 2017; doi 10.5463/DCID.v28i1.584 www.dcidj.org 172 Table 4: Results of Analysis of Covariance of different domains of behaviour disorders of Children with Mild and Moderate Intellectual Disability on Behavioural Assessment Scale For Indian Children-Mental Retardation (part- B) (Univariate Tests). Type of Disability Domains df F P Mild AViolent and Destructive Behaviour 1, 2 22.911* .041 B Temper Tantrums 1, 21.600 .333 C Misbehaves with others 1, 220.085* .046 D Self-injurious Behaviour 1, 2.296 .641 E Repetitive Behaviour 1, 210.500 .083 F Odd Behaviour 1, 211.433 .077 G Hyperactivity 1, 210.667 .082 HRebellious Behaviour 1, 25.000 .155 IAnti-social Behaviour 1, 21.562 .338 JFears 1,28.450 .101 Moderate AViolent and Destructive Behaviour 1,13 43.054* .000 B Temper Tantrums 1,13.551 .471 C Misbehaves with others 1,138.730* .011 DSelf-injurious Behaviour 1,1317.297** .001 E Repetitive Behaviour 1,137.240* .019 F Odd Behaviour 1,133.921 .069 G Hyperactivity 1,139.049** .010 H Rebellious Behaviour 1,1310.254** .007 I Anti-social Behaviour 1,135.881* .031 J Fears 1,132.511 .137 * P < .05, **P < .01 Results in table 4 indicated that the music intervention programme produced significant changes in the domains of behaviour disorders such as violent and destructive behaviour F (1, 2) =22.911, p = .041, and misbehaves with others F (1, 2) = 20.085, p = .046, of children with mild intellectual disability but their control group did not show any significant difference in these domains of behaviour disorders. Similarly, in children with moderate intellectual disability, music Vol. 28, No.1, 2017; doi 10.5463/DCID.v28i1.584 www.dcidj.org 173 intervention produced significant changes in the domains of behaviour disorders such as violent and destructive behaviour F(1,13) = 43.054, p = .000, misbehaves with others F(1,13) = 8.730, p = .011, self-injurious behaviour F(1,13) = 17.297, p = .001, repetitive behaviour F(1,13) = 7.240, p = .019, hyperactivity F(1,13) = 9.049, p = .010, rebellious behaviour F(1,13) = 10.254, p = .007, and anti-so cial behaviour F(1,13) = 5.881, p = .031. Both groups of children with mild and moderate intellectual disability did not show any significant change in the domains of temper tantrums, odd behaviour and fears, at.05 level of significance. DISCUSSION Music is used to treat children and adolescents with mental disorders in many European countries. The influence of music on persons with intellectual disability has been widely investigated in the West but there is little research in India. The present study is an effort to highlight beneficial effects of music on children with intellectual disability in India. Present study assessed the effect of intervention in music activities which is similar to that of Surujlal (2013) who assessed the contributions made by music to improve learning in the classroom among children with intellectual disabilities through a qualitative approach. Three focus group interviews were conducted using interpretative phenomenological analysis procedures to analyse the data. Results indicated that music is positive medium that contribute significantly to the learning experience of children with intellectual disabilities and the themes that emerged were confidence in communicating, concentration and behaviour. Conclusion of the present research that music intervention using strategies from Applied Behaviour Analysis was effective in reducing behaviour disorders among children with intellectual disabilities is similar to that of Sze and Yu (2004) who concluded that normal teaching strategies accompanied by music benefits the learner emotionally as it releases tension. Present research concluded that music has significant effect on behaviour disorders of intellectually disabled children, which is inconsistent with that of Grimm and Pefley (1990) who concluded that music helps children with learning difficulties, mental illness and intellectual disability. Gold et al, (2005) in a meta-analysis of four studies with merely moderate methodological quality, gave similar conclusion to that of present research that active music therapy may be more effective for clients with psychosis. In the present study, the sample represented both rural and urban populations and belonged to below-average, average and poor socio-economic status. Therefore, Vol. 28, No.1, 2017; doi 10.5463/DCID.v28i1.584 www.dcidj.org 174 the results could be generalised and replicated in other states of India where the socio-economic, cultural, geographical and political environment is simi lar. This programme of music intervention can be part of the daily curriculum for children and adults with mild and moderate intellectual disability as it adds recreation and fun-filled excitement to their daily routine and can produce a practical change in the special educational practice. Only trained instructors are required, who could systematically implement the music intervention following the teaching strategies, teaching programme and time plan as mentioned in the module. Instructors should be strict in implementing the programme as pl anned and should be flexible simultaneously to the individual needs. It is further suggested that this programme can also be tested on children and adults with behaviour disorders, destructive behaviour, and on certain mental disorders. It can also be used as treatment for depression and anxiety among children and adults with intellectual disability. CONCLUSION The findings have important implications for parents, special teachers, developmental and clinical psychologists and researchers by highlighting the fact that involvement with music in any form (listening, instrumental, etc.) should be made a compulsory part of their daily routine. Music enhances mood, and these children are benefited greatly from upbeat, rhythmic music that they can sing and play instruments. It provides a positive relaxing experience and can also ease stress and anxiety by reducing muscle tension and slowing down the heart rate. Hence, music intervention can be part of the treatment of the psychosocial and physiological aspects of an intellectual disability. Limitations Children with severe and profound intellectual disability were excluded from the study. Also there was no follow-up after the post-test. ACKNOWLEDGEMENT The authors thank the Principals, staff, parents and children for their cooperation during data collection and implementation of the intervention programme. Sincere thanks to the experts who helped in preparing the intervention programme. The authors did not receive any financial support or sponsorship. Vol. 28, No.1, 2017; doi 10.5463/DCID.v28i1.584 www.dcidj.org 175 REFERENCES Adamek MS, Darrow AA (2005). Music in special education. MD, American Music Therapy Association: Silver Spring. American Music Therapy Association (2006). Music therapy and individuals with diagnoses on the autism spectrum, 1-6. Available from: http://www.musictherapy.org/research/. 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Sound communication music for teaching oral language skills: Activities for teachers and therapists. Australia: Music Therapy Matters. PMCid:PMC3938065 World Health Organisation (1992). Manual of the international classification of disease, injuries and causes of death-10. Geneva: WHO Press. Vol. 28, No.1, 2017; doi 10.5463/DCID.v28i1.584 www.dcidj.org Copyright ofDisability, CBR&Inclusive Development isthe property ofAsia Pacific Disability Rehabilitation Journalanditscontent maynotbecopied oremailed tomultiple sites orposted toalistserv without thecopyright holder’sexpresswrittenpermission. However, usersmayprint, download, oremail articles forindividual use.
Find an experimental research study on the topic chosen in Week One for your Final Research Proposal. You may choose to include an experimental study which was included in the literature review you us
ORIGINAL ARTICLE Comparing Three Augmentative and Alternative Communication Modes for Children with Developmental Disabilities Larah van der Meer &Robert Didden & Dean Sutherland &Mark F. O’Reilly & Giulio E. Lancioni &Jeff Sigafoos Published online: 8 May 2012 # Springer Science+Business Media, LLC 2012 AbstractWe compared acquisition, maintenance, and preference for three AAC modes in four children with developmental disabilities (DD). Children were taught to make general requests for preferred items (snacks or play) using a speech- generating device (SGD), picture-exchange (PE), and manual signs (MS). The effects of intervention were evaluated in a multiple-probe across participants and alternating- treatments design. Preference probes were also conducted to determine if children would choose one AAC mode more frequently than the others. During intervention, all four children learned to request using PE and the SGD, but only two also reached criteria with MS. For the AAC preference assessments, three participants chose the SGD most frequently, while the other participant chose PE most frequently. The results suggest that children’s preference for different AAC modes can be assessed J Dev Phys Disabil (2012) 24:451–468 DOI 10.1007/s10882-012-9283-3 L. van der Meer :J. Sigafoos Victoria University of Wellington, Wellington, New Zealand R. Didden Radboud University Nijmegen, Nijmegen, The Netherlands D. Sutherland University of Canterbury, Christchurch, New Zealand M. F. O’Reilly The Meadows Center for the Prevention of Educational Risk, University of Texas at Austin, Austin, TX, USA G. E. Lancioni University of Bari, Bari, Italy L. van der Meer (*) School of Educational Psychology and Pedagogy, Victoria University of Wellington, PO Box 17-310, Karori, 6147 Wellington, New Zealand e-mail: [email protected] during the early stages of intervention and that their preferences may influence acquisition and maintenance of AAC-based requesting responses. KeywordsAugmentative and alternative communication. Developmental disabilities. Manual sign. Picture-exchange. Speech-generating devices Children with developmental disabilities (DD) often present with significant deficits in speech and language development. To enable these children to communicate, intervention typically involves teaching the use of augmentative and alternative communication (AAC; Beukelman and Mirenda2005;Schlosser2003a). Three common AAC modes are speech-generating devices (SGDs; Lancioni et al.2007), picture exchange (PE) or more specifically the Picture Exchange Communication System (PECS; Bondy and Frost1994; Bondy and Frost2001), and manual signing (MS; Lloyd, Fuller, & Arvidson,1997). Data indicate that all three of these AAC modes can be successfully taught to children with DD (Flippin et al.2010; Goldstein 2002; Hart and Banda2010; Lancioni et al.2007; Preston and Carter2009; Rispoli et al.2010; Schlosser and Wendt2008a; Schlosser and Wendt2008b; Sulzer-Azaroff et al.2009; Tien2008; van der Meer and Rispoli2010; Wendt2009). While there is evidence to support the use of each of these three AAC modes, debate continues regarding the relative efficacy of these three options for individuals with DD (Mirenda2003; Schlosser and Sigafoos2006). To shed some empirical light on this debate, one can turn to the results of several studies that have compared acquisition of PE versus MS (Adkins and Axelrod2001; Gregory et al.2009; Rotholz et al.1989; Tincani2004), as well as SGDs versus PE (Beck et al.2008; Bock et al. 2005). Few studies have compared SGD and MS (Iacono and Duncum1995; Iacono et al.1993; van der Meer et al.2012). One consistent finding from these comparison studies is that there seems to be no large or consistent differences in terms how effectively and efficiently the compared AAC modes can be taught to participants with DD. In light of such evidence, Sigafoos et al. (2003) proposed that clinicians might profitably examine the extent to which individuals show a preference for using one AAC mode over another. Along these lines, a number of studies have assessed preferences for different AAC modes (Cannella-Malone et al.2009; Sigafoos et al. 2009; Sigafoos et al.2005; Son et al.2006; Soto et al.1993; Winborn-Kemmerer et al. 2009). In these studies, the participant’s preference for using one mode of communication over another was assessed using a structured choice-making arrange- ment (Sigafoos1998). Specifically, participants were taught to use two different AAC options (e.g., SGD and PE) for functional communication (e.g., requesting a snack) and were then given the opportunity to choose which AAC mode to use during subsequent requesting opportunities. A communication system was considered pre- ferred when it was consistently chosen more often than the other option. A systematic review of this AAC-preference literature (van der Meer et al.2011b) indicated that individuals with DD do in fact often demonstrate a preference for using (choosing) one AAC mode over another. However, this existing group of studies is limited in a number of ways. First, preference assessments were only undertaken after participants already learned to use each communication option. This post-hoc 452 J Dev Phys Disabil (2012) 24:451–468 approach effectively prevented an evaluation of whether preference can be incorpo- rated into, and identified during, the initial stages of AAC intervention. Second, only one study (Soto et al.1993) collected maintenance data. It is therefore unclear whether employing preferred AAC systems improves intervention outcomes in terms of long-term maintenance of newly acquired communication skills or if preferences remain stable over time. Third, the majority of studies reviewed only assessed preference between two (SGD and PE) AAC modes (van der Meer et al.2011b). Although one study (Iacono and Duncum1995) compared the use of MS versus SGD and inferred a preference for one over the other based upon effectiveness of use, none of the studies assessed preferences among all three of these commonly used AAC modes (i.e., SGD, PE, and MS). Along those lines, van der Meer et al. (2012) addressed each of these limitations. They compared acquisition of augmented requesting responses using an iPod®-based SGD versus MS. Preference assessments were implemented throughout the interven- tion in order to determine whether four participants (with DD, aged 5 to 10 years) made relatively greater progress with the SGD or MS. Results showed that all four participants learned to request preferred objects. Three participants exhibited a preference for using the SGD, while one participant demonstrated a preference for using MS. Additionally, participants were more proficient at using their preferred AAC option and maintenance of communication skills was better with their preferred option. The present study was designed to systematically replicate and extend the work of van der Meer et al. (2012) by including more participants and comparing acquisition of, and preference for, SGD, MS, and PE. Based on the results of the van der Meer et al. (2012) study, we hypothesized that the four children participating in the current study would show a preference for using one AAC mode over the other two, that these preferences would vary across children, and that the children would learn to request preferred stimuli more quickly with their most preferred AAC mode. Method Participants Four children were recruited due to their severely limited speech from a child- care center for children with DD. All four participants met the following criteria: (a) diagnosis of intellectual/developmental disability or autism spectrum disorder (ASD), (b) less than 18 years of age, (c) very limited or no communication skills as determined by an age equivalency of 2 years or less on the Communication Domain of the Vineland–Z, Dutch edition (Sparrow et al.2003), (d) no auditory or visual impairments that would interfere with the use of AAC, and (e) sufficient motor skills to operate/perform the motor actions required to use each of the three AAC modes. Joe was a 12-year-old male diagnosed with ASD. On the Vineland–Z (Sparrow et al.2003), Joe received age equivalencies of 1:2 (years:months) for communication, 1:4 for daily living skills, and 1:1 for socialization. Joe did not have any spoken language, but made sounds that were presumed to be his way of expressing how he J Dev Phys Disabil (2012) 24:451–468 453 was feeling (e.g., happy versus sad). He also appeared to communicate his wants and needs by taking a person’s hand and leading them to an object. Joe’s teachers used a picture communication board to explain routine activities for the day (toilet, food and drink, gym, bus, free choice, and outside play). Teachers had also introduced him to MS for receptive language. He did not have any prior experience with SGDs, PE, or MS as a communication mode for requesting access to preferred objects. Joe’s fine and gross motor skills appeared to be adequate for his chronological age. He demonstrated frequent stereotypical and repetitive behaviors, such as flapping small toys and pieces of paper in front of his eyes. Sam was a 6-year-old male diagnosed with childhood disintegrative disorder and intellectual disability. He received age equivalencies on the Vineland–Z (Sparrow et al.2003) of 1:2, 1:8, and 0:11 (years:months) for the communication, daily living skills, and socialization domains, respectively. Sam did not have any spoken lan- guage. He would take a person’s hand and lead them to objects to seemingly express his wants and needs. He also made sounds that were thought to be indications of disapproval. His teachers tried to use MS when communicating with Sam, but he did not appear to show any interest in using MS to make requests. As was the case for Joe, Sam did not have any prior experience with SGDs, PE, or MS as a communi- cation mode for requesting access to preferred objects. Saskia was a 10-year-old female with Angelman syndrome. She received an age equivalency of 1:4 (years:months) for the communication domain and 1:0 for both the daily living skills and socialization domains of the Vineland–Z (Sparrow et al. 2003). Saskia was able to speak several single words, mostly in the form of echolalia. She would often take people’s hands to seemingly direct them to what she wanted, to open things, or to clap for her. Saskia had some experience with MS and was able to produce the signs for FINISHED and OPEN. She had no further experience with other forms of AAC. She exhibited difficulty with social interaction and engaged in stereotypic and repetitive behaviors. For example, she seemed more interested in adults and, although she appeared to enjoy watching other children play, she would push them away if they approached her. She also flapped keys and other objects in front of her eyes. Nicky was a 13-year-old female diagnosed with pervasive developmental disorder not otherwise specified (PDD-NOS). Nicky received age equivalencies on the Vine- land–Z (Sparrow et al.2003) of 1:3, 1:1, and 1:3 (years:months) for the commu- nication, daily living skills, and socialization domains respectively. She was able to verbalize several single words, but generally only made babbling sounds. She had previously received MS training and was able to produce several signs, including OPEN, EAT, and CIRCLE TIME. Nicky appeared to understand symbols from her daily picture communication book and from routine activities. She was able to match picture cards and had received 1 year of PECS training, but reportedly had made little progress. She had received no further training in the use of AAC for requesting preferred objects. Nicky was able to maintain good eye contact and seek social contact, but she was said to be very excitable and did not seem to understand social boundaries. She was not able to play cooperatively with other children. Nicky often cried in an apparent attempt to gain attention. Nicky’s fine motor skills were adequate for her developmental level. She was able to walk on her own, although she was hypotonic. 454 J Dev Phys Disabil (2012) 24:451–468 Setting and Intervention Context Participants were recruited from a Dutch childcare center for children with DD. The procedures related to this study were conducted in a small therapy room across the hall from the children’s main classrooms. Sessions occurred during morning and afternoon snack/leisure activities. The procedures were implemented in a one-to-one context consisting of the trainer (first author) and one participant at a time. All instructions/interactions with the participants and responses programmed on the AAC systems were in the Dutch language. Preferred Stimuli Stimuli that the children seemed to prefer, and which would be appropriate for them to request during a snack or leisure activity, were identified by a systematic two-stage stimulus preference assessment (Green et al.2008). Stage 1 of the preference assessment involved an indirect assessment in which teachers were asked to list foods, sensory stimuli, and toys that the participants appeared to enjoy and would be appropriate for the intervention. For Stage 2, three to six of the most preferred food or play stimuli were then selected for a direct stimulus assessment. The direct preference assessment for Joe focused on identifying preferred foods because his intervention occurred during a snack activity and his teachers reported that he seemed to be highly motivated by snack foods. The direct preference assessment for Sam, Saskia, and Nicky, in contrast, focused on identifying preferred toys because their intervention occurred during a play activity and their teachers had concerns about using food reinforcers. Stage 2 involved the simultaneous presentation of multiple items, without replace- ment (DeLeon and Iwata1996; Duker et al.2004). Each participant was presented with an array of items from Stage 1 (random placement) and allowed to select one item. Items were not replaced once they had been selected, thereby eliminating the chance of the participant choosing only one or a few items, as well as allowing the trainer to develop a rank order of items in terms of preference. The top three food items for Joe and top three play items for Sam, Saskia, Nicky were identified by calculating a rank order of the percentage of times that the stimuli were selected. Across two to three sessions, each item was offered a total of nine times. Rank orders were calculated using the formula: Number of Selections/Number of Offers x 100 %. Preferred stimuli for Joe included‘skittles’lollies (75 %),‘tumtum’lollies (33 %), and potato chips (32 %). Preferred stimuli for Sam included a puzzle (60 %), venting ball (36 %), and windmill (33 %). Preferred stimuli for Saskia included a musical toy (82 %), keys and lanyard (39 %), and bubbles (31 %). Preferred items for Nicky included a tea set (69 %), dolls (56 %), and a mirror (32 %). Speech-Generating Device (SGD) Participants were taught to request preferred toys or snacks using an Apple iPod Touch® with Proloquo2Go™software. The iPod was placed inside an iMainGo®2 speaker case to increase sound amplification. The iPod was configured to show a single page containing two graphic symbols (2.5 × 2.5 cm), representing requests for J Dev Phys Disabil (2012) 24:451–468 455 SNACKSandPLAY. The messages were programmed in Dutch. Touching each symbol activated corresponding synthetic speech-output (i.e.,“I want something to eat.”,and“I want to play.”). Picture Exchange (PE) Participants were also taught to request their preferred toys or snacks using PE. Three (6 × 6 cm) symbols from the PECS 2009 Dictionary (Pyramid Educational Products 2009) were affixed with Velcro™to a 19 × 13 cm card. One symbol contained a colored line drawing showing two hands reaching out and the wordsI WANT.The second symbol consisted of a colored line drawing of various different toys and the wordsTO PLAY.The third symbol consisted of a colored line drawing of various snack items and the messageSOMETHING TO EAT.All words were written in Dutch. The symbols were randomly allocated to the six (6 × 6 cm) panels of the card. Manual Signing (MS) Participants were taught to request their preferred toys or snacks using signs from the Dutch sign language system for children (Nederlands Gebarencentrum2006). Par- ticipants were taught the sign for SNACK or PLAY. The MS option was represented by a laminated photograph (15 × 8 cm) of the trainer making the hand formations for the signs for SNACK and PLAY. Response Definitions and Measurement For SGD use, correct responding was defined as independently (without a gestural or verbal prompt) touching the symbol on the screen of the SGD to activate the corresponding speech output in exchange for a desired item from the trainer. MS was defined as independent (without a gestural or verbal prompt) hand gestures to produce correct signs in exchange for a desired item from the trainer. For PE, participants were required to independently (without gestural or verbal prompt) place theI WANTand corresponding (SNACKSorPLAY) symbols—depending on whether they were requesting snacks or play—onto the two panels (6 × 6 cm) provided on a separate (21 × 7 cm) card in exchange for the desired item from the trainer. The percentage of correct responses (requests) was calculated for each session. Each session consisted of 10 offers to request snack or play items. The SGD target response for Joe was touching theSNACKsymbol on the SGD to activate the message“I want a snack.”His target response for PE was placing theI WANTand SOMETHING TO EATsymbols on the separate card. ThePLAYsymbol (SGD and PE) was intended as a distracter. Joe’s target response for MS was to produce the manual sign for SNACK. The PLAY sign (on the laminated card) was intended as a distractor. The SGD target response for Sam, Saskia, and Nicky was touching the PLAYsymbol on the SGD to activate the message“I want to play.”Their target response for PE was placing theI WANTandTO PLAYsymbols on the separate card. TheSNACKsymbol (SGD and PE) was intended as a distracter. The MS target response for Sam, Saskia, and Nicky was to produce the manual sign for PLAY. The SNACK sign (on the laminated card) was intended as a distractor. 456 J Dev Phys Disabil (2012) 24:451–468 Experimental Design The study included the following phases arranged in a multiple-probe across partic- ipants design (Kennedy2005): Baseline, Intervention, Preference Assessment (throughout Intervention, Post-Intervention, and Follow-Up), Post-Intervention, and Follow-Up. An alternating-treatments design was embedded within each phase of the multiple-probe to compare children’s performance with the SGD, PE, and MS options. Session Schedule Two to four sessions were conducted 5 days per week. Each session lasted about 10 min and consisted of 10 discrete trials. The AAC option available (i.e., SGD, PE, or MS) was counterbalanced across sessions to prevent order effects (Kennedy2005). For all sessions, the participant and trainer were seated next to each other at a table with one or two additional reliability and procedural integrity observers seated nearby. Once a participant showed an increase in requesting behavior above the level established in baseline for three consecutive sessions with at least one of the AAC options, training commenced with the next participant. Training was first provided to Joe, then Sam, Saskia, and finally Nicky. Training was provided in this order in accordance with results from the baseline phase (i.e., the participant with the most stable baseline commenced intervention first). Once a participant reached criterion for one AAC condition (i.e., 80 % correct requesting across three consecutive sessions for each AAC option), maintenance probes were initiated with that system while the other communication systems continued to be taught using the intervention proce- dures. One maintenance session with the acquired AAC device was conducted after three sessions with each of the AAC options still being taught. Procedures Because participants were considered to be at the beginning stages of AAC interven- tion, they were taught to make general requests for either snacks or toys from which they could select one highly preferred item after each request. However, to ensure some level of symbol discrimination, the distractor symbols/signs were included on the AAC options. If participants requested a snack when they were undertaking training to request a toy or vice versa, the trainer explained:We are learning to request toys (snack) at the moment, you can request a toy (snack) another time. It was considered natural to provide them with this feedback when they activated the non- target symbols/produced the non-target signs, but not to reinforce it with preferred tangibles. Similarly, producing MS to request snacks or toys during SGD or PE sessions was ignored in order to bring the use of each device under stimulus control. BaselineDuring baseline, a tray containing three different snack (play) items was placed on the table in view, but out of the participants’reach. The SGD, PE, and MS option (represented by the photograph of the trainer making the two signs) were randomly placed on different sides of the table for each baseline session. Each session J Dev Phys Disabil (2012) 24:451–468 457 involved 10 discrete trials for snacks or toys. The session began with the trainer telling the participant:Here is a tray of snacks (toys), let me know if you want something. After 10 s, the trainer moved the tray within reach and allowed the participant to take one item. This 10-s fixed time schedule of reinforcement was provided to ensure continued motivation to participate in sessions. When offering snacks, participants were allowed to select one item from the tray, which was then replenished before the next offer. When offering the tray of toys, participants were allowed to select one toy and play with it for approximately 30 s before it was returned to the tray. SGD, PE, and MS responses were recorded, but had no programmed consequences. InterventionThis phase was conducted in a discrete trial format until participants reached criterion (i.e., 80 % correct requesting across three consecutive sessions for each AAC option). The SGD, PE, or the MS option was placed on the table (counter- balanced across trials) within reach of the child in accordance with the alternating treatments design. Each trial consisted of the trainer pointing to a tray of snacks (toys) and saying:Here’s a tray of snacks (toys). Let me know if you want something. Training involved a 10-s time delay between the verbal cue (i.e.,Let me know if you want something.) and the use of graduated guidance to prompt a correct request. Graduated guidance involved use of the least amount of physical guidance necessary to ensure the child made a correct request, while simultaneously explaining the required response (e.g.,Press PLAY to ask to play with a toy.orMove your hand to your mouth to make the sign forEAT. orPut the I WANT and PLAY pictures on the velcro strips). Immediately after the child had used the SGD to produce the correct synthesized speech output, or had placed the appropriate symbols on the Velcro strips, or had made the correct manual sign, the trainer moved the tray containing the snacks (or toys) within reach of the participant. The participant was allowed to select one item from the tray and consume the chosen snack or play with the chosen toy for about 30 s. After this, the next trial was initiated. Procedural ModificationsJoe did not reach criterion for each communication system during the initial intervention phase so he received a modified intervention. The modification was developed in response to what appeared to be a problem in teaching him to discriminate among the different symbols. Therefore, thePLAYsymbol was removed from the SGD and PE options (see the 1 Symbol phase of Fig.1). The only icon displayed on the SGD screen was therefore theSNACKicon, which was also enlarged to fit the entire screen. Joe was only required to press this icon to activate the voice-output in order to make a correct request for a preferred snack item. For the PE option, he no longer had to discriminate between theSNACKandPLAYsymbols. For a correct request he had to place theI WANTandSOMETHIG TO EATsymbols onto the two locations provided on the separate card in exchange for the desired item from the trainer. Sam also failed to learn how to use the SGD and MS communication options during the initial intervention sessions. Therefore, for SGD and for MS (see the 20 s Time Delay & Differential Reinforcement phase of Fig.1), the procedures changed to using a longer (20 s) time delay followed by graduated guidance, as well as differ- ential reinforcement (where Sam was only given the opportunity to play if he 458 J Dev Phys Disabil (2012) 24:451–468 independently used the SGD or MS to request to play). Prompted trials were not reinforced. Because little progress was evident with these changes, a 0 s time delay was then implemented and immediate reinforcement was reintroduced (See the 0 s Time Delay and Differential Reinforcement phase of Fig.1). That is, Sam was Fig. 1Percentage of correct requests using the SGD, PE, and MS options across sessions for each participant J Dev Phys Disabil (2012) 24:451–468 459 immediately prompted to make a correct request and then given access to the tray. However, for the MS option Sam appeared to become dependent on the trainer immediately prompting a correct response. He did not attempt to make the sign for PLAY and so for this reason, a 10 s time delay with reinforcement was reintroduced (10 s Time Delay and Differential Reinforcement phase of Fig.1). AAC Preference AssessmentsThese assessments were undertaken to determine if participants would show a preference for using one of the three AAC options. They were undertaken after every sixth intervention session (i.e., after two sessions for each AAC option). During each preference assessment, the SGD, PE, and MS options were presented (randomly) at different positions on the table. While pointing to each option, the trainer asked the participant:Which communication option would you like to use? The SGD, PE, or MS?The child had 10 s in which to make a choice by touching one of the options. Once a choice was made, the trainer initiated one requesting opportunity with the chosen AAC option before reverting back to initiat- ing requesting opportunities with the AAC device that was scheduled to be used for the session. If the child did not choose an option within 10 s, the device preference assessment was terminated and training continued with the AAC option that was scheduled for use in that session. Post-InterventionOnce the participant reached criterion for each AAC device, post- intervention preference assessments were introduced. These were identical to the previously described preference assessments, except that once an AAC option had been chosen, the participant continued to request preferred items using the chosen communication method for the entire 10-trial session. Follow-upSix follow-up sessions were conducted 2 weeks following post- intervention for Joe (Session 88 of Fig.1) and Saskia (Session 85 of Fig.1). Participants did not use either communication option during the break. Because Sam and Nicky did not complete all phases of the study due to time constraints, they did not receive any follow-up probes. Procedures for follow-up were identical to the intervention phase, except no prompting occurred and reinforcement was contingent upon a correct request. One AAC preference assessment was implemented before each follow-up session. Inter-Observer Agreement The trainer collected data on the frequency of correct requesting, the level of prompting required during intervention for each trial, as well as which communication mode was selected during the AAC preference assessments. To assess the reliability of the trainer’s data collection, an independent observer also collected data on the frequency of requesting, level of prompting, and communication mode chosen. For each session, percentages of agreement between the independent observer and the trainer were calculated using the formula: Agreements=AgreementsþDisagreements ðÞ 100% . Inter-observer agreement data were collected on 28 % of all sessions and ranged from 80 to 100 % with a mean of 99.2 %. 460 J Dev Phys Disabil (2012) 24:451–468 Procedural Integrity To assess procedural integrity, the independent observer used a checklist of the procedures and recorded whether or not the trainer had correctly implemented each procedural step in its proper sequence. Procedural integrity was assessed on 28 % of all sessions and ranged from 85 to 100 % correct implementation of the procedural steps with an overall mean of 99.8 %. A second independent observer collected inter- observer agreement data on 7 % of these integrity checks with 100 % agreement. Results Figure1shows the percentage of correct requests during each session/phase of the study and for each of the three AAC modes. Figure2provides a summary of the results from the AAC preference assessments conducted during intervention and subsequent phases. In baseline (Fig.1), none of the participants ever used MS or PE to make the targeted requests. Saskia and Nicky made one and two correct SGD- based requests, respectively during baseline. JoeWhen intervention was introduced, Joe reached the acquisition criterion for the MS option on his 15th MS training session. Similarly, when intervention was introduced, and then modified by removing the distractor symbols, Joe achieved acquisition with PE and SGD on his 16th and 17th intervention sessions, respectively. During the post-acquisition phase, Joe chose to use the SGD (55 %) more often than PE (45 %). Once chosen, he then used the selected option (i.e., either SGD or PE) Fig. 2Results from the device preference assessment probes depicting the number of times each communication option (SGD, PE, and MS) was chosen and the number of time a device was not chosen (No Selection) across each phase of the study for each participant J Dev Phys Disabil (2012) 24:451–468 461 with 100 % proficiency. During follow-up, Joe maintained his level of correct SGD- and PE-based requests at 100 %, but his performance dropped to 20 and 50 % correct for MS. Overall, Joe received a total of 33 opportunities to choose between the SGD, PE and MS option (Fig.2) and he chose the SGD most frequently (61 %). SamWhen intervention was introduced with Sam, he reached acquisition with PE on his 9th training session. When the intervention procedures were modified, he achieved acquisition of SGD on his 17th such session. However, even with additional procedural modifications, Sam did not achieve acquisition for MS within the time- frame of this study. Sam did not progress to the post-intervention or follow-up phases due to his failure to acquire use of the MS option. During intervention, Sam received nine AAC preference assessments (Fig.2) and he chose PE most frequently (56 %). SaskiaSaskia achieved acquisition of PE-, MS- and SGD-based requests on her fifth, sixth, and eighth respective intervention sessions. During the post-intervention phase, Saskia always chose to use the SGD and then used it with 80 to 100 % proficiency. During follow-up, her performance maintained at 100 % correct for the SGD, but decreased to 40 % and 0 % correct for the PE and MS modes, respectively. Overall, Saskia received 23 AAC preference assessments (Fig.2) during which she always chose the SGD. NickyNicky achieved acquisition of SGD- and PE-based requests on her fifth and sixth respective intervention sessions. She showed an initial increase in the percent- age of correct requests using MS, but failed to achieve acquisition within the time- frame of the study and did not progress to the post-intervention or follow-up phases. Across her four AAC preference assessments conducted during intervention (see Fig.2), she chose the SGD three times (75 %). Discussion The present study extends previous research by van der Meer et al. (2012)by comparing acquisition of three common modes of AAC, namely SGD, PE, and MS. The findings suggest that the systematic instructional procedures used for each AAC option (Duker et al.2004) were largely effective in teaching each participant to use at least two of the three AAC options. Furthermore, a key aspect of the study was to assess preferences for one mode of communication over the others throughout the intervention process, allowing participants some degree of self-determination with respect to AAC modes (Sigafoos2006). Specifically, two participants (Joe and Saskia) reached criterion for use with each communication option and demonstrated a preference for using the SGD. The other two participants (Sam and Nicky) reached criterion for SGD and PE, but not MS. Nicky exhibited a preference for using the SGD, while Sam demonstrated a slight preference for using PE. The findings support those of previous studies suggesting that students with DD can learn to use a SGD, PE, and MS for functional communication and that many will also indicate a preference for using a particular communication system (van der Meer 462 J Dev Phys Disabil (2012) 24:451–468 et al.2012,2011b). The findings also provide further evidence indicating that most of the children assessed to date appear to show a preference for using SGD over PE and MS (van der Meer et al.2012,2011b), although the present study appears to be the only one to date that has compared acquisition of, and preference for SGD, PE, and MS. While all of the participants learned to use PE and SGD, Sam and Nicky failed to reach criterion for MS, even with modifications to the intervention process. This finding could suggest that MS communication is more difficult for some children to learn or that the instructional procedures used in the present study were better suited for teaching use of the SGD and PE options. With respect to the first possibility, Iacono and colleagues (Iacono and Duncum1995; Iacono et al.1993) suggested that graphic symbols, such as those used for the SGD and PE options in this study, are less demanding on children’s working memory because only recognition memory is needed, whereas MS requires the use of recall memory. This could be one reason why MS is sometimes learned at a slower rate than other AAC systems and this might also explain some of the patterns with respect to preferences for SGD and PE over MS. Alternatively, MS might simply be a more difficult AAC system to teach because forming the signs requires more and varied physical movements than simply pointing to or handing a graphic to a partner (van der Meer et al.2012). Another possibility is that Sam and Nicky’s failure to reach criterion for MS reflected the fact that they did not prefer to use it and were therefore less motivated to participate in the MS intervention sessions, once they started to make progress with the other options. This possibility suggests that preference, or lack of preference, for an AAC option may influence motivation to learn to use that option. If this explanation has validity, it would highlight the value of assessing preferences for different AAC options during the early stages of intervention, as was attempted in the present study. However, it is unclear how early such assessments might be implemented. Pre-baseline assessments could be configured, for example, but it is unclear if participants would require some level of exposure to each option before their choices would represent valid indicators of preference. For one participant (Joe), it appeared that discrimination of graphic symbols was difficult. Specifically, Joe did not learn to discriminate theSNACKsymbol from thePLAYsymbol, and only reached criterion for SGD and PE when the distracter (PLAY) symbol was removed. As with Sam and Nicky’s failure to reach criterion for MS, Joe’s difficulty could reflect either a problem in his discrimination learning abilities or ineffective instructional procedures. In any event, these problems in teaching Sam, Nicky, and Joe suggest there may be some value in implementing a pre-intervention assessment of children’s learning and behavioral characteristics (Light et al.1998), such as determining the level of iconicity appropriate for an individual to acquire graphic symbol and MS understanding (Koul et al.2001). From a research perspective it is important to ensure each AAC system is comparable in terms of cognitive demands so as to maintain functional equivalence in order to compare acquisition and preference between AAC systems (Schlosser2003b). It might also be important to ensure a match between the AAC system and skills being taught and the instructional strategies that are implemented to teach that system and those skills. J Dev Phys Disabil (2012) 24:451–468 463 A limitation of the present study is that the PE system was not equivalent to the SGD and MS systems. While the SGD and MS options required only a one-step request, the former included two steps. That is, for the PE option, participants were required to not only place theI WANT,but also the correspondingSNACKorPLAY symbols, depending on whether they were requesting snacks or toys, onto the two locations provided on a separate card. This may have increased the response effort for the PE system, which in turn may have negatively influenced acquisition and preferences (Ringdahl et al.2009; Winborn-Kemmerer et al.2009). Although this did not appear to influence acquisition of the PE system in the present study, it may have diminished preference for that system. However, Sam did in fact demonstrate a preference for PE. Furthermore, due to the inherent differences in response top- ographies of SGD and PE versus MS, another potential limitation outlined by van der Meer et al. (2012) was that a MS response could be produced during SGD and PE sessions, but not vice versa, possibly influencing preferences and rapidity of acquisition. Joe, Saskia, and Nicky appeared to show a preference for using the SGD. While this could suggest that it was easier to use than either the PE or MS option, it is also possible that the SGD required somewhat more refined motor control, which might in fact make this a more difficult option to learn. That is, activating the speech-output function of the iPod-based SGD required a level of finesse (i.e., lightly touching or tapping the icon), which has been documented to be difficult for some adolescents with DD to master (Kagohara et al.2010; van der Meer et al.2011a,2012). Despite what could be a slightly more difficult system to activate, Joe, Saskia, and Nicky showed a preference for using the SGD. While van der Meer et al. (2012) suggested that some participants may prefer AAC options that are easier to use, others may prefer SGD due to the dynamic display and speech-output features. Therefore, perhaps in addition to ease of use, it could be hypothesized that inherent features of some AAC options (e.g., speech-output) influence such preferences as suggested by Sigafoos et al. (2005). While our results suggest children showed idiosyncratic preferences for the AAC options, future research would be needed to determine variables that might influence such preferences. Joe and Sam did not come to make any consistent choices for one communication device over the others until they had reached criteria with each system. Saskia and Nicky, in contrast, appeared to show a preference (for the SGD) before they had learned to use the communication options. These results suggest that preference for different AAC options may emerge at different times in the intervention process. In line with previous research (van der Meer et al.2012) Joe and Saskia showed better performance during follow-up with their preferred communication option. This finding suggests that preference may influence maintenance of newly acquired AAC-based requesting skills. Future research is needed to examine whether these findings might extend to interventions that focus on teaching more complex commu- nication skills, such as asking and answering questions and commenting on the environment. It did not appear that differing reinforcement histories accounted for the children’s preferences for the different AAC options because they received the same number of sessions/reinforcements with each option during baseline and intervention. While Sam did later receive differential reinforcement schedules for the SGD and MS 464 J Dev Phys Disabil (2012) 24:451–468 options in an effort to increase his performance with these two options, he was already showing a preference for PE prior to this procedural manipulation. However, it could be that the children’s prior (pre-baseline) experiences may have influenced their preferences to some degree. Specifically, prior to this study, it appeared that while none of the children had any experience with SGDs, they reportedly had experience with one or more of the other AAC modes. Joe, Sam, and Saskia, for example, were reported to have had prior experience with manual signing and Joe and Nicky were reported to have had some experience with picture-based communication systems. Unfortunately, it is impossible to know if any of these prior experiences influenced their learning rates and choices during the AAC preference assessments that were conducted in this study. Future research could be improved by controlling for the potential bias that may arise when children enter a study with differing amounts and types of prior experiences with the to be compared AAC options. In practice, however, it may be difficult to determine the precise amount and nature of any such prior experiences given the often subjective and anecdotal nature of the information available to researchers about children’s prior AAC experiences. It is also perhaps inevitable that children with DD who have limited or no speech will be exposed to one or more AAC modes and that such exposure could influence acquisition of, and preference for, different AAC modes. We would argue that even with such difficulties and uncertainties with respect to children’s prior AAC experi- ences, it would still seem useful to assess their preference for different AAC options so as to promote greater self-determination. In summary, the results of present study extend the findings of van der Meer et al. (2012) by comparing acquisition of, and preference for, three commonly used AAC modes (SGD, PE, and MS) among four children with DD. The results showed that two children learned to use all three AAC modes, whereas the other two children learned to use SGD and PE, but not MS. Preference checks suggested that three of the four children appeared to prefer using the SGD, whereas the other child showed a preference for using PE. Preference appeared to influence acquisition and mainte- nance, but more research is needed to confirm any such effects. AcknowledgmentsSupport for this research was provided from the New Zealand Government through the Marsden Fund Council, administered by the Royal Society of New Zealand; and by Victoria University of Wellington, The University of Canterbury, and The New Zealand Institute of Language, Brain & Behaviour. Declaration of InterestsThe authors report no conflicts of interests. 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