Sentence production and sentence repetition in autistic adolescents and young adults: Linguistic sensitivity to finiteness-marking

Sentence production and sentence repetition in autistic adolescents and young adults: Linguistic sensitivity to finiteness-marking | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Sentence production and sentence repetition in autistic adolescents and young adults: Linguistic sensitivity to finiteness-marking View ORCID Profile Teresa Girolamo , View ORCID Profile Samantha Ghali , View ORCID Profile Caroline Larson doi: https://doi.org/10.1101/2024.03.26.24304924 Teresa Girolamo 1 San Diego State University Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Teresa Girolamo For correspondence: tgirolamo{at}sdsu.edu Samantha Ghali 2 University of Kansas Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Samantha Ghali Caroline Larson 3 University of Missouri Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Caroline Larson Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Purpose Despite the clinical utility of sentence production and sentence repetition to identify language impairment in autism, little is known about the extent to which these tasks are sensitive to potential dialectal variation. One promising method is strategic scoring ( Oetting et al., 2016 ), which has good clinical utility for identifying language impairment in nonautistic school-age children across dialects of English. This report applies strategic scoring to analyze sentence repetition and sentence production in autistic adolescents and adults. Method Thirty-one diverse autistic adolescents and adults with language impairment (ALI; n =15) and without language impairment (ASD; n =16) completed the Formulated Sentences and Recalling Sentences subtests of the Clinical Evaluation of Language Fundamentals-5 th Ed ( Wiig et al., 2013 ). Descriptive analyses and regression evaluated effects of scoring condition, group, and scoring condition by group on outcomes, as well as group differences in finiteness-marking across utterances and morphosyntactic structures. Results Strategic and unmodified item-level scores were essentially constant on both subtests and significantly lower in the ALI than the ASD group. Only group predicted item-level scores. Group differences were limited to: percent grammatical utterances on Formulated Sentences and percent production of overt structures combined on Sentence Repetition (ALI < ASD). Discussion Findings support the feasibility of strategic scoring for sentence production and sentence repetition to identify language impairment and indicate that potential dialectal variation in finiteness-marking did not confound outcomes in this sample. To better understand the clinical utility of strategic scoring, replication with a larger sample varying in age and comparisons with dialect-sensitive measures are needed. Half of all autistic individuals are estimated to have LI, which is characterized by challenges with structural language ( Boucher, 2012 ). LI in autism is tied to poorer educational, health, occupational and social outcomes ( Howlin et al., 2013 ; Johnson et al., 2010 ; Magiati et al., 2014 ). Yet, autistic youth – especially if racially and ethnically minoritized – face unreliable access to speech/language services ( Newman et al., 2011 ; Pope et al., 2022 ; Taylor & Henninger, 2015 ). One barrier to access is that given current diagnostic criteria for autism ( American Psychiatric Association, 2013 ), schools may only assess behavior and not language in autistic students ( Musgrove, 2015 ). Reducing disparities requires quality language assessment. Quality language assessment requires evidence-based practice. On one hand, evidence-based practice requires reliable methods for identifying LI in autism. Finiteness-marking, or marking of tense and agreement, is one aspect of morphosyntax and a clinical marker of LI in autism ( Eigsti et al., 2011 ; Modyanova et al., 2017 ); see Ash and Redmond (2014) . In turn, sentence repetition and sentence production are useful for assessing morphosyntax in autism ( Schaeffer et al., 2023 ). Evidence-based assessment also requires understanding how to interpret and use measures ( Girolamo et al., 2022b ; Messick, 1990 ). Yet, this knowledge may be inadequate. From 2004 to 2014, 75% of states disproportionately represented Black students in the disability category of speech/language impairment ( Robinson & Norton, 2019 ). In research, studies using norm-referenced assessments in English to characterize structural language in autistic youth (ages 3-21) systematically exclude or do not account for those with LI and those who are racially and ethnically minoritized ( Girolamo et al., 2023b , 2023c ). Thus, the evidence base needed to inform language assessment in autism is incomplete. A broader consideration in assessment involves the linguistic reality of the United States. Individuals of all races and ethnicities speak over 25 dialects of English ( Wolfram & Schilling, 2015 ). Dialects vary in how they mark for finiteness, such that dialectal variation and LI each influence expressive language in terms of morphosyntactic production ( Oetting et al., 2016 ). Yet, clinical language research assumes General American English (GAE) is the norm ( Oetting, 2020 ), and evaluators of science may perpetuate the myth that only minoritized individuals speak dialects other than GAE ( Girolamo et al. 2022a ). Accurate language assessment requires both representation in research and linguistic sensitivity without racialized assumptions about language background ( Plaut, 2010 ). This report examines sentence production and sentence repetition, focusing on finiteness-marking, in diverse autistic adolescents and adults. Sentence Production and Sentence Repetition in Autism Epidemiological data support the utility of sentence repetition and sentence production for identifying LI in nonautistic youth ( ages 4-10) ( Calder et al., 2023 ; Klem et al., 2015 ). These tasks are often part of assessments, such as the Clinical Evaluation of Language Fundamentals (CELF) ( Wiig et al., 2013 ), which are commonly used in practice and psychometrically validated ( Betz et al., 2013 ; Nitido & Plante, 2020 ). While autism research does not have population-level evidence, cross-linguistic applications of these tasks support their clinical utility. Group Comparisons Prior work evaluating sentence repetition and sentence production has compared groups of autistic individuals without mention of LI (ASD) and nonautistic peers. In addition, some studies compared within-autism heterogeneity: autistic individuals without LI (ASD) and autistic individuals with LI (ALI). A summary is presented in Supplementary Table 1. In studies comparing ASD and nonautistic peers, one pattern was lower performance in ASD (Hedges’ g = 0.62 to 1.34). Some samples showed this pattern on both tasks using percent accuracy on the Russian Child Language Assessment Battery (ages 7-10) ( Arutiunian et al., 2022 ; Lopukhina et al., 2019 ) and scaled scores on the English CELF-4 (ages 8-21) ( Larson et al., 2022 ; Semel et al., 2003 ; Tyson et al., 2014 ). Some studies found lower scores in ASD using just sentence repetition: (a) raw scores on a Danish translation of the CELF-Preschool, 2 nd Ed. (ages 4-6) ( Brynskov et al., 2017 ; Wiig et al., 2004 ), (b) raw scores on the Hebrew PETEL Test (ages 9-18) ( Friedmann, 2000 ; Sukenik & Friedmann, 2018 ), and (c) percent accuracy on the Saudi Sentence Repetition Task (ages 5-7) ( Alhassan & Marinis, 2021 ). In turn, scaled scores were lower in ASD (ages 7-17) on the English CELF-Revised Formulated Sentences ( Landa & Goldberg, 2005 ; Semel et al., 1987 ). A second pattern was no group differences in scaled scores on the English CELF-4 Formulated Sentences and Recalling Sentences (ages 9-16) ( Harper-Hill et al., 2013 ; Semel et al., 2003 ) or in percent accuracy on the LITMUS-French sentence repetition (ages 18-56) ( Manenti et al., 2023 ; Prevost et al., 2012 ). A third pattern was mixed performance, with lower raw scores in ASD (ages 6-12) on sentence production but not sentence repetition using the Greek Expressive and Receptive Language Evaluation (EREL) ( Georgiou & Spanoudis, 2021 ; Spanoudis & Pahiti, 2014 ). In all, findings and measurement varied. Studies comparing ALI and ASD showed similar variation. ALI had lower percent accuracy on the Saudi Sentence Repetition Task (ages 5-7; Hedges’ g = 1.53) ( Alhassan & Marinis, 2021 ) and the LITMUS-French Sentence Repetition (ages 18-56; r = −0.784) ( Manenti et al., 2023 ; Prevost et al., 2012 ). Some studies provided only frequencies suggesting lower performance in ALI: average raw score on the English CELF-4 Recalling Sentences and Formulated Sentences ( M age = 11.0) ( McGregor et al., 2012 ; Semel et al., 2003 ) and percent accuracy on the LITMUS-French Sentence Repetition (ages 6-12) ( Prevost et al., 2012 ; Silleresi et al., 2020 ). A second pattern was no group differences in: (a) raw scores on the Greek EREL sentence repetition or sentence production (ages 6-12) ( Georgiou & Spanoudis, 2021 ), (b) scaled scores on the English NEPSY Memory for Sentences (ages 7-15) ( Korkman et al., 1998 ; Whitehouse et al., 2008 ), or (c) raw scores on the English CELF-3 Recalling Sentences (ages 14-15) ( Riches et al., 2010 ). As with ASD and nonautistic comparisons, there was no one pattern. Limitations to Generalizability Findings indicate sentence production and sentence repetition tasks capture linguistic heterogeneity in ALI and ASD, but there are limitations to generalizability. Like autism research overall ( Russell et al., 2019 ), most studies selected against individuals with NVIQ < 70 and used IQ cutoffs as high as −1 SD ( McGregor et al., 2012 ). Yet, estimates of NVIQ < 70 in autism are 38% to 50% ( Charman et al., 2003 ; Loomes et al., 2017 ; Maenner et al., 2023 ). Such IQ cutoffs may fail to reflect language across the spectrum or the ways in which language and NVIQ may dissociate. In an epidemiological study of nonautistic youth with LI, the severity of LI did not differ when NVIQ was −1 to −2 SD versus within 1 SD , and only one of five language subtests differed when NVIQ was < −2 SD ( Norbury et al., 2016 ). Further, except for two instances ( Manenti et al., 2023 ; Riches et al., 2010 ), studies focused primarily on children, amid a need for information on autism in adulthood ( Howlin & Taylor, 2015 ). Finally, consistent with multiple areas of autism research ( Girolamo et al., 2023c ; Larson et al., 2023 ; Steinbrenner et al., 2022 ; West et al., 2016 ), full reporting of race and ethnicity was rare ( Larson et al., 2022 ). Hence, the utility of these tasks for diverse, older autistic individuals varying in NVIQ is unknown. Linguistically Sensitivity to Finiteness-Marking Interpreting sentence production and sentence repetition from diverse autistic adolescents and adults in American English requires attention to differences in finiteness-marking due to dialectal variation ( Beyer & Hudson Kam, 2012 ). This is not because there is a one-to-one ratio between race, ethnicity, and dialectal variation. Rather, given the linguistic reality of the United States, being linguistically sensitive through strategic scoring and examination of finiteness-marking is clinically and scientifically sound ( Oetting et al., 2016 ). To our knowledge, this approach has yet to be used in autism research or with individuals older than ages 4 to 6 years. Scoring Methods for Finiteness-Marking Recall that dialects of American English differ in how they mark for finiteness ( Oetting et al., 2019 ; Wolfram & Schilling, 2015 ). In GAE, the past-tense of “eat” requires overt inflection ( Wolfram & Schilling, 2015 ); see (1a). In African American English (AAE) and Southern White English (SWE), the past-tense of “eat” can be zero-marked, with the null marker indicating inflection; see (1b) ( Wolfram & Schilling, 2015 ). It is not that AAE or SWE speakers would only produce (1b). Rather, (1a) and (1b) are each plausible, with differences in production rate by dialect and LI status ( Cleveland & Oetting, 2013 ; Garrity & Oetting, 2010 ; Oetting & McDonald, 2001 ; Seymour et al., 1998 ). (1) Examples of the past-tense irregular “eat” ( Oetting et al., 2019 ) (1a) she ate [overt] (1b) she eat Ø [zero] (1c) the girl [other] A question, then, is how to characterize these differences in dialect, clinical status, and rate. Descriptively examining finiteness-marking can inform development of systematic approaches to scoring ( Oetting & McDonald, 2001 ). In sentence (1), unmodified scoring only counts GAE overt forms, or (1a), as correct; any other response, including (1b) and (1c), is incorrect ( Oetting et al., 2019 ). Hence, unmodified scoring over-identifies LI in speakers of dialects other than GAE ( Hendricks & Adlof, 2017 ). Conversely, modified scoring is responsive to all possible instances of dialectal variation and counts (1a) and (1b) as correct ( Oetting et al., 2019 ). In only considering other responses like (1c), as incorrect ( Oetting et al., 2019 ), rather than production rate of overt and zero marking, modified scoring can under-identify LI ( Craig et al., 2004 ; Hendricks & Adlof, 2017 ). To optimize differences across dialects, Oetting and colleagues (2019 , 2016, 2021) developed strategic scoring. While strategic scoring counts (1a) and (1b) as correct, it considers the proportion of GAE and non-GAE overt forms to all overt GAE and zero forms; only other responses like (1c) are excluded ( Oetting et al., 2019 ). Importantly, strategic scoring has differentiated LI and typical language in 106 diverse nonautistic youth (ages 4-6) who speak AAE, GAE, and SWE when using dialect-sensitive sentence repetition and sentence production tasks. In administering a sentence repetition probe with auxiliary BE, Oetting et al. (2016) counted responses that differentiate AAE and SWE from GAE, but do not confound identification of LI, as correct: “is” for “are,” “was” for “were,” and zero third-person singular ( Cleveland & Oetting, 2013 ; Oetting & Garrity, 2006 ). Strategic scoring showed high classification accuracy when considering AAE and SWE together (Se = .91, Sp = .85) and separately (AAE: Se = .89, Sp = .86; SWE: Se = .94, Sp = .83) ( Oetting et al., 2016 ). The presence of effects of clinical status (partial η 2 = .55, p < .001), but not dialect (partial η 2 = .02, p = .13), on sentence repetition probe raw scores indicated dialectal variation did not confound group differences ( Oetting et al., 2016 ). Turning to sentence production, Oetting et al. (2019) administered four probes to nonautistic youth (ages 4-6) that targeted past-tense regular and irregular, singular and plural present auxiliary BE, singular and plural past auxiliary BE, and habitual and nonhabitual third-person singular. Across all structures, strategic scoring had higher classification accuracy than modified scoring (75% versus 66%) and more balanced sensitivity and specificity than unmodified or modified scoring (strategic: .72 and .77; unmodified: .81 and .68; modified: .51 and .81) ( Oetting et al., 2019 ). In addition, unmodified scoring yielded differential effects of clinical group by dialect for AAE and SWE (η 2 : .27 versus .56), indicating lack of measurement invariance; conversely, strategic scoring had twice the effect of clinical group (η 2 : .38 versus .17) compared to modified scoring ( Oetting et al., 2019 ). When separating structures, only unmodified and strategic scoring yielded differences by clinical group, and strategic scoring had the highest classification accuracy (78%) ( Oetting et al., 2019 ). In sum, strategic scoring and descriptive evaluation of sentence repetition and sentence production tasks may help identify LI when considering linguistic diversity. Considerations in Strategic Scoring For autistic adolescents and adults, there are considerations in using strategic scoring. One involves evaluating finiteness-marking patterns ( Oetting et al., 2016 ), as findings on finiteness-marking in autism are mixed. While LI in autism includes persistent challenges with structural language, prior work focuses on broad patterns that disallow for focusing on finiteness-marking or adolescents and adults (e.g., Girolamo et al., 2023b ). For instance, Modyanova and colleagues (2017) documented group differences in youth ranging from 4 to 16 years. There, ALI (NVIQ 40-112) had lower accuracy than ASD (NVIQ 65-151) on norm-referenced probes for English third-person singular (65.3% < 87.8%) and past-tense (67.8% 10.8%), unscorable final responses (19% > 1.9%), and percent responses with wrong tense (30.6% > 0.4%) ( Modyanova et al., 2017 ; Rice & Wexler, 2001 ). Given this age range, it is unclear whether bare stem responses might be associated with potential dialectal variation or dynamic changes in the language system ( Modyanova et al., 2017 ). A second consideration involves having a sufficient number of productions of morphosyntactic structures to determine finiteness-marking patterns ( Oetting et al., 2021 ). With little precedent and no one standard for finiteness-marking across dialects ( Oetting et al., 2019 ), examining item-level responses and morphosyntactic structures is prudent. Summary Linguistic sensitivity to finiteness-marking in language assessment of autistic individuals is of clinical and scientific relevance. Evidence to date supports the utility of: 1) sentence repetition and sentence production tasks to assess structural language in autism, 2) finiteness-marking to identify LI in autism, and 3) strategic scoring and descriptive examination of utterances and morphosyntactic structures to characterize finiteness-marking. A next step is applying these methods to characterize language in diverse autistic adolescents and adults. The Current Study This study extends the work of Oetting and colleagues (2019 ; 2016; 2021) to diverse adolescent and adult ALI and ASD ranging in NVIQ. Research questions were: Do scoring method (unmodified and strategic), group (ALI, ASD), and scoring method by group predict item-level scores on sentence repetition and sentence production? Do ALI and ASD groups differ in percent grammatical utterances, percent utterances with zero marking, and percent utterances with wrong tense on sentence repetition and sentence production? Do ALI and ASD groups differ in percent productions of overt, zero, and other responses of: third-person singular–regular and irregular, past-tense–regular and irregular, auxiliary BE, and copula BE on sentence repetition and sentence production? There were no hypotheses about differences in strategic and unmodified scores, as prior work documenting the clinical utility of strategic scoring was based on significantly younger youth (ages 4-6; Oetting et al., 2016 , 2019 , 201) and finiteness-marking patterns in autistic youth (ages 4-16) included a wide age range when changes in the language system (including acquisition of finiteness-marking) are dynamic ( Modyanova et al., 2017 ; Rice et al., 1998 ); thus, disentangling developmental effects from finiteness-marking patterns associated with potential dialectal variation is impossible. Given recent findings from English-speaking ALI that included adolescents with NVIQ < 70 ( Modyanova et al., 2017 ), it was expected that, when considering scoring method, percent grammatical utterances would be lower in the ALI than the ASD group. It was also hypothesized that ALI would have a higher percent of utterances with zero-marking and wrong tense. Finally, it was expected that lower percent overt-marked structures combined and separate would be lower in the ALI than the ASD group. Method This preregistered study ( https://osf.io/hzuc4 ) received institutional board approval. With no precedent of strategic scoring of sentence repetition and sentence production in diverse autistic adolescents and adults, this report examines a subset of participants. Selection Criteria Selection criteria were: (a) ages 13 to 30 years; (b) meet diagnostic criteria for DSM-5 autism ( American Psychiatric Association, 2013 ), per a formal medical or educational diagnosis and independent confirmation using expert clinical judgment plus assessment; (c) use primarily spoken language to communicate, as determined during screening, since study tasks required use of spoken language; and (d) proficiency in American English per self-report during screening, as study tasks were conducted in American English. Participants who did not have sufficient hearing or vision thresholds for hearing and seeing audiovisual stimuli were excluded, as tasks used audiovisual stimuli. Per prior work ( Girolamo & Rice, 2022 ; Girolamo et al., 2023b ; Tomblin et al., 1997 ), the cutoff for ALI was −1.25 SD on CELF-5 core language ( Wiig et al., 2013 ) or −1.25 SD on at least two measures of overall expressive language, overall receptive language, expressive vocabulary, receptive vocabulary, and nonword repetition (see Measures ). Procedures Participants were recruited and assessed through two larger studies from 2021 to 2022 ( Eigsti & Fein, 2018 ; Girolamo et al., 2023a ). Participants received compensation for their time and effort. Trained examiners administered direct behavioral assessments to participants remotely following test developer guidance ( Pearson, 2023 ). After assessment, CELF-5 ( Wiig et al., 2013 ) Formulated Sentences and Recalling Sentences responses were transcribed, coded, and scored in SALT 20 ( Miller & Iglesias, 2020 ). A trained research assistant ignorant of the study purpose or participant clinical status independently checked point-by-point accuracy. Training involved establishing reliability in a multi-step process: training on transcription and coding manuals; reaching 85% reliability on transcription for utterances and words on three consecutive transcripts; reaching 90% reliability on codes and morphemes on three consecutive transcripts; and reaching 90% reliability on scoring of three consecutive transcripts. Monitoring reliability took place on 20% of data randomly selected for checks by a third, independent trained research assistant; the assumption was that if an examiner did not meet these ongoing checks, they would re-train until they re-established reliability. In this case, research assistants met ongoing reliability checks. All disagreements were discussed until consensus was reached. This procedure resulted in 100% interrater reliability for utterances (as by nature of these tasks, utterances were typically one sentence), 98% for words, and 97.83% for codes and morphemes. Measures Characterizing measures included participant demographics: chronological age, race, ethnicity, sex assigned at birth, and gender. Other measures characterized individual differences: CELF-5 core language score for overall expressive-receptive language ability ( Wiig et al., 2013 ), ADOS-2 calibrated severity scores ( Lord et al., 2012 ) or SRS-2 total t- scores ( Constantino, 2012 ) for autism traits, and Wechsler Abbreviated Scale of Intelligence full-scale IQ ( Wechsler, 1999 ) or Raven’s 2 Progressive Matrices–Clinical Edition NVIQ ( Raven et al., 2018 ) for cognitive ability. LI status was determined using a cutoff of −1.25 SD on CELF-5 core language ( Wiig et al., 2013 ) or −1.25 SD on at least two measures: CELF-5 Expressive Language Index, CELF-5 Receptive Language Index ( Wiig et al., 2013 ), Expressive Vocabulary Test-3 rd Ed. ( Williams, 2019 ), Peabody Picture Vocabulary Test-5 th Ed. ( Dunn, 2019 ), and the Syllable Repetition Task ( Shriberg et al., 2009 ). Characterizing information did not include language background, as the aim was to evaluate finiteness-marking and not to test differences by dialect. Measures in analysis came from item responses from the CELF-5 Formulated Sentences and Recalling Sentences ( Wiig et al., 2013 ). For the first research question, the primary outcome was the effect of scoring method (strategic versus unmodified) on item-level score differences. In the second research question, primary outcomes were group differences in percent grammatical utterances, percent zero-marked utterances, and percent utterances with wrong tense. Primary outcomes of the third research question were group differences in percent production of overt, zero, and other structures for third-person singular, past-tense, copula BE, and auxiliary BE. Data Processing Coding Responses were coded for overt and omission of overt finiteness-marking in the following morphosyntactic structures per SALT conventions ( Miller & Iglesias, 2020 ): third person singular regular and irregular, past-tense regular and irregular, auxiliary BE, and copulas ( Oetting et al., 2019 ; Oetting et al., 2016 ; Oetting et al., 2021 ). Here, there were only final responses (versus multiple attempts at an item-level response), and coding for omission of overt marking was used to evaluate finiteness-marking patterns versus accuracy. Responses were also coded using the SALT ( Miller & Iglesias, 2020 ) error code for excluded (i.e., other) responses per the schema of Oetting et al. (2021) , such as no verb (e.g., “boy”), no subject (e.g., “eats”), wrong tense (e.g., “was eating” or “were eating” for a past-tense form), or some other response (e.g., no response or “I don’t know”). Coded transcripts were used to generate frequencies of production for each morphosyntactic structure (overt, zero or omission of overt-marking, other), as well as utterance types (grammatical, omission of overt marking, wrong tense). Error codes were manually inspected for use of wrong tense or other response. Scoring Using the CELF-5 manual ( Wiig et al., 2013 ), Formulated Sentences and Recalling Sentences items received unmodified and strategic scores of 0 to 2 or 0 to 3, respectively. Scoring followed manual instructions in terms of number of deviations from the target response. Formulated Sentences responses received scores of 2 if they were complete sentences, semantically, syntactically, and pragmatically appropriate, and included the exact stimulus word; scores of 1 if they met all criteria except for one or two deviations in syntax or semantics; and scores of 0 if responses did not include the stimulus word, were incomplete or illogical sentences, were completely unrelated to the stimulus picture, or had three or more semantic or syntactic deviations ( Wiig et al., 2013 ). Recalling Sentences responses received scores of 3 if sentences had no deviations from the stimulus sentence; scores of 2 if there was one deviation in terms of word changes, additions, substitutions, omissions, or transpositions; scores of if there were 2 or 3 deviations; and scores of 0 if there were four or more deviations ( Wiig et al., 2013 ). The difference in scoring method involved what was considered a deviation. Again, unmodified scoring only considers GAE norms for finiteness-marking ( Oetting et al., 2019 ). In contrast, strategic scoring accounted for dialectal variation in finiteness-marking that does not confound identification of LI ( Oetting et al., 2016 ). In response to (2a), unmodified scoring counts “work” as a deviation, as in GAE, third-person singular forms must have overt finiteness-marking (i.e., “works”); see (2b). Together with deviations from “nurse,” “community,” and “clinic,” the score would be zero. As in (2c), strategic scoring would not consider “workØ” as a deviation, as omission of overt marking (or zero marking) is acceptable in some dialects, and result in a score of one. After scoring each item-level response, item-level unmodified and strategic scores were translated into overall scores for each subtest. (2) Example of unmodified and strategic scores from the CELF-5 ( Wiig et al., 2013 ) (2a) my mother is the nurse who works in the community clinic [stimulus] (2b) my mother is the woman who work in the place [unmodified] = 4 deviations, score of 0 (2c) my mother is the woman who workØ in the place [strategic] = 3 deviations, score of 1 Analyses All responses were included, and basal scores were imputed following CELF-5 scoring rules ( Wiig et al., 2013 ), which mimics real-world practice. One ASD participant had missing information on race and full-scale IQ. No other data were missing. Prior to analysis, variables were inspected to see that they met assumptions of normality, linearity, and heteroscedasticity. Data that did not meet these assumptions used nonparametric analysis. All analyses used an a priori significance level of p < .05. To address the first research question, scoring condition (unmodified, strategic), group (ASD, ALI) and group by scoring condition were regressed on item-level strategic and unmodified scores of each subtest: CELF-5 Formulated Sentences and Recalling Sentences. This analysis tested for an effect of scoring condition on item-level outcomes and whether groups differed in the effect of condition. To address the second research question, Welch independent sample t -tests analyzed group differences in percent grammatical utterances, percent utterances with omission of overt marking, and percent utterances with wrong tense; in effect, utterances were item-level responses. To address the third research question, Welch independent sample t -tests analyzed group differences in percent production of overt structures, zero structures, and other responses for third-person singular regular and irregular, past-tense regular and irregular, auxiliary BE, copula BE, and auxiliary DO. Results Participants Participants were 31 autistic adolescents and adults ( M age = 20.80, SD = 4.28, 14-30 years); see Table 1 . ALI ( n =15) and ASD ( n =16) groups did not significantly differ in age. In the full sample, 61.3% of participants were racially minoritized per U.S. Census categories ( Office of Management and Budget, 1997 ): 3.2% Asian, 41.9% Black, 9.7% multiracial, 6.5% Native American, and 29% white. In the ALI group, two participants selected “don’t know” for race and reported they were Puerto Rican. About one quarter (22.6%) of participants were Hispanic or Latine. Most participants were male for sex assigned at birth (female: 19.4%, male: 80.6%), which is similar to male-to-female estimates in autism of 3:1 to 4:1 ( Loomes et al., 2017 ), and gender (female: 22.6%, male: 77.4%). Due to small sample size, Fisher’s exact test was used and revealed no significant group differences in sex assigned at birth, p = .172, or gender, p = .083. View this table: View inline View popup Table 1: Participant Sociodemographics Per grouping criteria, language scores differed; see Table 2 . ALI had significantly lower outcomes than the ASD group on CELF-5 core language standard scores (56.2 versus 101.4), Formulated Sentences scaled scores (3.4 versus 11), and Recalling Sentences scaled scores (2.53 versus 9.92). ADOS-2 calibrated severity scores met cutoffs for ASD in both groups ( Lord et al., 2012 ). Mean SRS-2 scores corresponded to “severe” autistic traits in the ASD group and “moderate” autistic traits in the ALI group ( Constantino, 2012 ). For participants with available IQ in the ASD group ( n = 15), the lowest full scale IQ or NVIQ was 80; NVIQ was −1 to −2 SD for two participants (12.5%). In the ALI group, NVIQ was < 70 for three participants (20%) and −1 to −2 SD for three participants (20%). View this table: View inline View popup Download powerpoint Table 2: Participant Language and Nonverbal Cognitive Characteristics Effect of Scoring Method, Group, and Scoring Method by Group on Item-Level Scores To address the first research question, analyses explored effects of scoring method (unmodified and strategic), group (ALI and ASD), and the interaction of scoring method by group on item-level scores on CELF-5 Formulated Sentences and Recalling Sentences. There was no effect of scoring method, with essentially constant item-level unmodified scores and strategic scores; see Table 3 . However, there was a significant effect of group. Welch’s two-sample t -tests showed mean item-level scores in the ALI group were significantly lower than the ASD group: Formulated Sentences (0.96 < 1.67) and Recalling Sentences (0.52 < 1.99). Thus, item-level scores differed on the basis of group but not differences in finiteness-marking that could potentially indicate dialectal variation. View this table: View inline View popup Download powerpoint Table 3: Group Differences in CELF-5 Formulated Sentences and Recalling Sentences Item-Level Strategic and Unmodified Scores, Percent Grammatical Utterances, Percent Omission, and Percent Wrong Tense Next, regression models were run to predict item-level Formulated Sentences and Recalling Sentences scores from group, scoring method, and an interaction of group by scoring method. Each multiple regression model significantly predicted outcomes, Formulated Sentences scores, F (3,50) = 17.17, p < .0001, and Recalling Sentences scores, F (3,50) = 9.51, p < .00. In addition, there were main effects of group ( p ’s < .0001), but effects of scoring method and group by scoring method were non-significant; see Table 4 . Findings indicate that scores differed by LI status but not by scoring method. Thus, differences in finiteness-marking associated with dialectal variation did not confound outcomes. View this table: View inline View popup Download powerpoint Table 4 Regression Results for CELF-5 Formulated Sentences and Recalling Sentences Strategic Versus Unmodified Scores Group Differences in Percent Grammatical Utterances, Percent Utterances with Overt Omission, and Percent Utterances with Wrong Tense in Sentence Repetition and Sentence Production To contextualize responses, analyses for the second research question examined group differences in percent grammatical utterances, percent utterances with omission of overt marking, and percent utterances with wrong tense. ALI and ASD groups did not significantly differ in number of utterances on Formulated Sentences (12.8 versus 14.5) or Recalling Sentences (16.8 versus 15); see Table 3 . Groups significantly differed in percent grammatical utterances on Formulated Sentences (77.93% versus 94.95%); see Figure 1 and Table 3 . However, ALI and ASD groups did not significantly differ on percent utterances with omission of overt marking or with use of wrong tense: Formulated Sentences wrong tense (1.77% versus 0.97%), Recalling Sentences omission of overt marking (3.02% versus 3.11%), and Recalling Sentences wrong tense (2.35% versus 1.14%). Other group differences were not significant: Formulated Sentences percent utterances with omission of overt marking (ALI: 9.23% versus ASD: 1.26%) and Recalling Sentences percent grammatical utterances (ALI: 79.62% versus ASD: 92.42%). In sum, the ALI group had significantly lower percent grammatical utterances on sentence production than the ASD group but not on other measures. Download figure Open in new tab Figure 1. Percent grammatical utterances, percent utterances with omission of overt finiteness marking, and percent utterance with wrong tense. Note. ASD = autism without language impairment. ALI = autism plus language impairment. PGU = percent grammar Group Differences in Percent Productions of Overt, Zero, and Other Responses of Finiteness-Marking Structures The third research question tested group differences in percent production of overt, zero, and other responses of finiteness-marking patterns of morphosyntactic structures combined and separately that differentiate LI across dialects of American English ( Oetting et al., 2016 , 2019 , 2021 ). Specific structures were: third-person singular regulars and irregulars, past-tense regulars and irregulars, auxiliary BE, and copulas. Finiteness-Marking in Morphosyntactic Structures Combined When considering structures together, ALI and ASD groups did not significantly differ in number of morphosyntactic structures produced on CELF-5 Formulated Sentences (15.13 versus 15), or CELF-5 Recalling Sentences (18.93 versus 22.92); see Table 5 . ALI and ASD groups both produced structures with overt marking >90% on Formulated Sentences (92.69% versus 99.4%) and Recalling Sentences (90.06% versus 98.8%); see Figure 2 . However, the ALI group had significantly lower percent structures with overt marking than the ASD group on Recalling Sentences. Groups did not significantly differ on other outcomes. On both Formulated Sentences and Sentence Repetition, percent structures with omission of overt marking (ALI: 6.38% and 5.23% versus ASD: 0.6% and 0.4%) and percent structures with other productions on (ALI: 0.93% and 4.71% versus ASD: 0% and 0.78%) were low. Overall, group differences were minimal, with overt marking above 90% on both sentence production and sentence repetition. Download figure Open in new tab Figure 2. Percent overt marking, zero marking, and other production of morphosyntactic structures combined. Note. ASD = autism without language impairment. ALI = autism plus language impairment. Overt = overt finiteness-marking. Zero = omission of overt marking. Other = other production (e.g., no verb). View this table: View inline View popup Table 5: Group Differences in Finiteness-Marking on All Morphosyntactic Structures Combined on CELF-5 Formulated Sentences and Recalling Sentences ( Wiig et al., 2013 ) Finiteness-Marking in Individual Morphosyntactic Structures As a final check, analyses examined finiteness-marking in individual morphosyntactic structures: third-person singular regulars and irregulars, past-tense regulars and irregulars, auxiliary BE, and copulas. Because frequencies of individual structures were low (e.g., auxiliary be singular present; range: 0-3.83), forms were combined into third-person regular and irregular, past-tense regular and irregular, auxiliary BE, and copula BE in order to analyze finiteness-marking patterns ( Oetting et al., 2019 ); see Tables 6 and 7 . Groups differed in number of productions for auxiliary BE present on Formulated Sentences (ALI: 3.73 > ASD: 1.25), t (25) = −2.54, p = .018; auxiliary BE present on Recalling Sentences (ALI: 0.87 > ASD: 0.08), t (24) = - 3.44, p = .002; and past-tense regular and irregulars (ASD: 16 > ALI: 7.27), t (18.32) = 4.85, p < .001 on Recalling Sentences. View this table: View inline View popup Table 6: Total and Mean Production of Morphosyntactic Structures on CELF-5 Formulated Sentences and Recalling Sentences View this table: View inline View popup Download powerpoint Table 7: Overt, Zero, and Other Productions of Morphosyntactic Structures by Group Analyses showed no group differences in finiteness-marking patterns of third-person regulars and irregulars, past-tense regulars and irregulars, auxiliary BE, or copulas; see Tables 7 and 8 . The ALI group had lower percent overt production of third-person (81.25% versus 98.86%) and auxiliary BE past (83.33% versus 100%) on Formulated Sentences, as well as third-person (81.94% versus 95.83%) and past-tense copulas (75.98% versus 98.83%) on Recalling Sentences than the ASD group, but these differences were not significant. Similarly, the ALI group had higher percent production of zero forms than the ASD group on third person on Formulated Sentences (18.75% versus 1.14%) and Recalling Sentences (18.06% versus 4.17%), as well as of other production for past-tense copula on Recalling Sentences (20.75% versus 1.17%). Altogether, groups did not differ in percent productions of overt, omission of overt, or other production of third-person, past tense, auxiliary, or copula structures. View this table: View inline View popup Download powerpoint Table 8: Statistical Comparisons of Production of Overt, Zero, and Other Morphosyntactic Structures by Group Summary Item-level strategic scores and unmodified scores did not differ on sentence repetition or sentence production. When accounting for scoring method, group, and group by scoring method, only group predicted item-level score outcomes. At the level of utterances and morphosyntactic structures, groups differed in: (a) percent grammatical utterances on sentence production (ALI < ASD) and (b) percent production of overt structures combined on sentence repetition – and percentages were each over 90% (ALI < ASD). Discussion Analyzing sentence production and sentence repetition tasks in diverse autistic adolescents and adults showed no differences in strategic and unmodified scores and limited group differences in utterance-, item- and structure-level outcomes. The motivation was not to prove differences in strategic and unmodified scores, but rather, to understand possible confounds in finiteness-marking. Findings have implications for understanding structural language in autism beyond the school-aged years. Comparisons to Published Patterns In this sample, the ASD group only had significantly higher performance than the ALI group on percent grammatical utterances of sentence production. Percent utterances with omission of overt marking was low in both groups for sentence repetition (∼3%) but qualitatively higher in ALI than ASD for sentence production (9.23% versus 1.26%). One interpretation might be that dialectal variation in finiteness-marking confounded scoring outcomes, as the ALI group was primarily minoritized and the ASD group was primarily white – even though strategic and unmodified scores did not differ. However, recall that group comparisons for each subtest was based on a mean of about 15 utterances (or items). By nature, this number of utterances limits the number of productions of morphosyntactic structures ( Oetting et al., 2021 ). Regardless, the fact that percentage of utterances with omission of overt marking was near 0% indicates that it is not the case that minoritized autistic adolescents and adults are universally only proficient in dialects that differ in finiteness-marking norms from GAE ( Oetting, 2020 ). Results shed light on finiteness-marking in ALI and ASD groups across age ranges. Compared to Modyanova et al. (2017) , ALI and ASD groups showed fewer differences. In Modyanova et al. (2017) , ALI and ASD groups (ages 6-16) differed in percent total responses (or utterances) with overt finiteness-marking on third-person singular (ALI: 65.3% versus ASD: 87.8%) and past tense (ALI: 67.83% versus ASD: 92.82%) elicitation probes, as well as percent omission on both third-person singular (ALI: 15.1% versus ASD: 7.2%) and past-tense probes (ALI: 19.7% versus ASD: 10.8%) ( Modyanova et al., 2017 ). In this study of older individuals ( M age : 20.80), comparisons were limited by a low number of productions on each task for third-person singular (1.7 to 3.59) and past-tense (4.22 to 11.15). Still, the ALI group had a lower percent production of overt third-person singulars than the ASD group on both sentence production and sentence repetition (81.25% to 81.94% versus 95.83% to 98.86%). Unlike Modyanova et al. (2017) , production of overt past-tense structures was near ceiling in both groups on both tasks (range: 98.1% to 100%), while omission of overt marking was low in both utterances (range: 1.26% to 9.23%) and structures (range: 1.37% to 6.38%). While deeper conclusions about finiteness-marking cannot be made without a higher number of productions of utterances or structures, one possibility is that early acquired morphosyntactic forms may be less clinically useful for identifying LI in older autistic individuals (e.g., Rice & Wexler, 2001 ). In contrast, our findings do not replicate Manenti et al. (2023) , who administered a French sentence repetition task to 39 autistic adults (ages 18-56), one-third of whom had intellectual disability. There, the “ASD-low language” group ( n =10) had lower finiteness-marking than the “ASD-normal language” group ( n = 24) (26.7% versus 98.6%) ( Manenti et al., 2023 ). In this study, group differences were much more modest, with overt finiteness-marking in sentence repetition in both groups near ceiling (90.06% versus 98.8%). Differences between studies might explain these differences in outcomes. First, finite clauses in the French sentence repetition task focused on present-tense, whereas the sentence repetition task of this study also included past-tense, auxiliaries, and copulas. A second possible factor involves participant characteristics. Manenti et al. (2023) recruited autistic adults from residential facilities, reporting that six of 10 “ASD-low” participants did not attend “regular school” and that nine of 10 had NVIQ < 68. As only age equivalencies for NVIQ were reported, the variability of NVIQ in the ASD-low sample is unknown. Here, NVIQ was variable in both ALI (52-101) and ASD (80-152) groups, and participants were recruited from the community versus more restrictive settings. Third, as with Modyanova et al. (2017) , Manenti et al. (2023) used an experimental probe designed to elicit certain morphosyntactic forms; this study did not. Last, it may be that while finiteness-marking norms in English are well understood in childhood ( Rice et al., 1998 ), norms in adolescence and adulthood differ. Overall, findings in this study showed that sentence production and sentence repetition outcomes were sensitive to heterogeneity in autistic adolescents and adults varying in NVIQ and language skills. However, as some participants were beyond the age range for the CELF-5 ( Wiig et al., 2013 ), the clinical utility is unknown. One issue is that norm-referenced language assessments designed for adults do not comprehensively measure language, as they are designed to measure the severity of specific acquired disorders, such as the Western Aphasia Battery and the Montreal Cognitive Assessment ( Kertesz, 2022 ; Nasreddine et al., 2005 ). Further, some assessments are succinct by design to maximize utility for point-of-service clinical design-making in inpatient settings. As such, despite excitement about increasing accessibility of language assessment across the autism spectrum ( Kover & Abbeduto, 2023 ; Schaeffer et al., 2023 ), best practices for language assessment that is developmentally appropriate and age appropriate in autistic adolescents and adults remains elusive ( Howlin & Taylor, 2015 ). Implications for Clinical Practice and Research Beyond empirical findings, the approach of this study has implications for assessment in clinical and research settings. Clinicians often use norm-referenced assessments, follow workplace eligibility policy, and report limitations in ability to conduct linguistically sensitive assessment ( Denman et al., 2021 ; Selin et al., 2022 ). One challenge is that assessments sensitive to language disorders across dialects in English, such as the Diagnostic Evaluation of Language Variation ( Seymour et al., 2018 ), only extend to ages 9;11. Though imperfect, for assessments that extend to adolescence and early adulthood, examiners can implement a multi-pronged approach to enhance linguistic sensitivity: evaluation of finiteness-marking patterns ( Oetting & Garrity, 2006 ; Oetting & McDonald, 2001 ), evaluation of finiteness-marking at the item level ( Oetting et al., 2016 ), and comparison of whether accounting for dialectal variation in finiteness-marking impacts scores on clinical assessment ( Oetting et al., 2019 ). Such an approach can help provide incremental evidence to support clinical decision-making. In research, methodologies that yield replicable results not only provide data on the soundness of measurement, but also have the potential to support researchers in reducing biases toward groups who are systematically excluded from research ( Maye et al., 2022 ; Russell et al., 2019 ). Indeed, researchers have the responsibility to ensure research methodologies are accessible and responsive to participants, including linguistic sensitivity ( Oetting, 2020 ). When researchers fail to include feasible methods, such as strategic scoring, they risk perpetuating assumptions often made about the language backgrounds of individuals, solely based on perceptions about individuals or groups ( Dunham et al., 2015 ; Marques et al., 1988 , 1998 ). This responsibility is even more pronounced when considering the overlap between pragmatic language, an area of particular interest in autism research, and structural language ( Schaeffer et al., 2023 ). To combat such bias, researchers can – and should – integrate linguistically sensitive methodological tools into tasks, such as sentence repetition, which provide useful information on structural language in autism-appropriate tasks ( Schaeffer et al., 2023 ). Limitations Although this study focused on a new approach to characterizing finiteness-marking in autism, it had several limitations. First, though unmodified and strategic scores did not differ, participants also produced few morphosyntactic structures that differentiate LI across dialects. Per Oetting et al. (2019) , an insufficient frequency distribution disallows from examining patterns that could inform development of probes. In addition, analyzing data from adolescents and adults, when changes in the language system are less dynamic ( Howlin, 1984 ; Miniscalco & Carlsson, 2022 ), may mask differences in the sensitivity of scoring approaches to dialectal variation and structural language skills in autism. To better understand the clinical utility of strategic scoring, replication with a larger, more developmentally diverse sample is needed. Second, this study did not include comprehensive information on language background. The purpose was to analyze finiteness-marking using through a multi-step approach, which is congruent with arguments for valid test interpretation and use ( Messick, 1990 ). Per the American Speech-Language-Hearing Association Code of Ethics (2023) and scope of practice in speech-language pathology (2016), examiners may not always have the full range of knowledge about an individual’s language experiences or environment yet are nevertheless responsible for working to linguistically sensitive in assessment. While more robust examination of language variation is needed for language in autism (e.g., Oetting et al., 2016 , 2019 , 2021 ), this initial investigation describes one strategy to evaluate patterns and check for potential differences in finiteness-marking associated with dialectal variation that could confound scoring of sentence production or sentence repetition tasks ( Oetting & McDonald, 2001 ). Finally, this study did not examine effects of vocabulary and memory on sentence repetition ( Manenti et al., 2023 ). While relevant to understanding overall sentence repetition performance, this study focused on potential dialectal variation in finiteness-marking. Future Directions This study lays groundwork for future research on structural language in older autistic individuals that is high-quality ( Howlin & Taylor, 2015 ), linguistically sensitive ( Oetting, 2020 ), and inclusive of individuals varying in IQ ( Manenti et al., 2023 ). A broader understanding of finiteness-marking on sentence repetition and sentence production is needed to establish the clinical utility of strategic scoring for diverse autistic individuals ( Oetting & McDonald, 2001 ). Evaluating finiteness-marking in large samples of autistic youth and adults who are chronologically and developmentally diverse would allow for observation of finiteness-marking when changes in the language system are more dynamic ( Kwok et al., 2015 ). This work, which is underway, is relevant in understanding the potential of sentence repetition to sensitively characterize structural language in autism ( Schaeffer et al., 2023 ). An additional future direction for research involves comparison of sentence repetition and sentence production to dialect-sensitive probes that elicit a sufficient number of morphosyntactic structures sensitive to structural language skills across dialects for autistic adolescents and adults ( Oetting et al., 2016 , 2019 , 2021 ). Such comparison would inform to what extent these tasks, which are common in clinical settings and neuroimaging ( Betz et al., 2013 ; Larson et al., 2023 ), are clinically useful. These are two of many directions that support achieving a dimensional understanding of language in autism ( Kover & Abbeduto, 2023 ). Conclusion In evaluating finiteness-marking on sentence repetition and sentence production data from diverse autistic adolescents and adults in English, this report offers one way to infuse linguistically sensitivity in common language assessment measures. The aim was not to “prove” differences existed. The take-home point is that carefully evaluating scores, utterance types, and productions of morphosyntactic structures are feasible and ought to inform interpretation and use of assessment data to characterize structural language as a dimensional construct ( Kover & Abbeduto, 2023 ). In the long-term, these efforts may contribute to much-needed reliable and generalizable approaches to language assessment in autistic adolescents and adults. Data Availability Statement The datasets generated during the current study are not publicly available due to participants opting not to share their de-identified individual data in the consent process or due to ongoing analyses. Information about the datasets (structure, code) are available from the corresponding author on reasonable request. CRediT statement TG: conceptualization, methodology, formal analysis, investigation, resources, data curation, writing – original draft, project administration, funding acquisition. SG: formal analysis, validation, writing – review & editing. CL: formal analysis, data curation, writing – review & editing, visualization. Author Information Teresa Girolamo, School of Speech, Language, and Hearing Sciences, San Diego, CA, San Diego State University. Samantha Ghali, Child Language Doctoral Program, Lawrence, KS, University of Kansas. Caroline Larson, Department of Speech, Language, and Hearing Sciences, Interdisciplinary Neuroscience Program, Columbia, MO, University of Missouri. Funding TG was supported by T32DC017703 (PD: Eigsti), L70DC021323 (PI: Girolamo) & an ASHFoundation New Investigators Research Grant (PI: Girolamo). SG was supported by an ASHFoundation Graduate Student Scholarship & a University of Kansas Diversity, Equity, Inclusion, and Belonging Graduate Fellowship (PI: Ghali). CL was supported by R01MH112687 (PI: Eigsti). Conflict of Interest The authors have no financial or nonfinancial disclosures. Acknowledgements TG was supported by T32DC017703 (PD: Eigsti), L70DC021323 (PI: Girolamo) & an ASHFoundation New Investigators Research Grant (PI: Girolamo). SG was supported by an ASHFoundation Graduate Student Scholarship & a University of Kansas Graduate Fellowship (PI: Ghali). CL was supported by R01MH112687 (PI: Eigsti). We also thank community partners, participants and their families, research assistants, and lab personnel. Footnotes Girolamo, T., Ghali, S., & Larson, C. (minor revision). Sentence production and sentence repetition in autistic adolescents and young adults: Linguistic sensitivity to finiteness-marking. Journal of Speech, Language, and Hearing Research . References ↵ Alhassan , M. A. , & Marinis , T. ( 2021 ). Sentence repetition in children with autism spectrum disorder in Saudi Arabia . In Experimental Arabic Linguistics (Vol. 10 , pp. 143 – 176 ). John Benjamins Publishing . doi: 10.1075/sal.10.06alh OpenUrl CrossRef ↵ American Psychiatric Association . ( 2013 ). Diagnostic and statistical manual of mental disorders : DSM-5 ( 5th ed.). Author. ↵ American Speech-Language-Hearing Association . ( 2023 ). Code of ethics . https://www.asha.org/policy American Speech-Language-Hearing Association . ( 2016 ). Scope of practice in speech-language pathology . https://www.asha.org/policy ↵ Arutiunian , V. , Lopukhina , A. , Minnigulova , A. , Shlyakhova , A. , Davydova , E. , Pereverzeva , D. , Sorokin , A. , Tyushkevich , S. , Mamokhina , U. , Danilina , K. , & Dragoy , O . ( 2022 ). Language abilities of Russian primary-school-aged children with autism spectrum disorder: Evidence from comprehensive assessment . Journal of Autism and Developmental Disorders , 52 ( 2 ), 584 – 599 . doi: 10.1007/s10803-021-04967-0 OpenUrl CrossRef ↵ Ash , A. C. , & Redmond , S. M . ( 2014 ). Using finiteness as a clinical marker to identify language impairment . Perspectives on Language Learning and Education , 21 ( 4 ), 148 – 158 . doi: 10.1044/lle21.4.148 OpenUrl CrossRef ↵ Betz , S. K. , Eickhoff , J. R. , & Sullivan , S. F . ( 2013 ). Factors influencing the selection of standardized tests for the diagnosis of specific language impairment . Language, Speech, and Hearing Services in Schools , 44 ( 2 ), 133 – 146 . doi: 10.1044/0161-1461(2012/12-0093) OpenUrl CrossRef PubMed ↵ Beyer , T. , & Hudson Kam , C. L . ( 2012 ). First and second graders’ interpretation of Standard American English morphology across varieties of English . First Language , 32 ( 3 ), 365 – 384 . doi: 10.1177/014272371142761 OpenUrl CrossRef ↵ Boucher , J . ( 2012 ). Research review: structural language in autistic spectrum disorder– characteristics and causes . Journal of Child Psychology and Psychiatry , 53 ( 3 ), 219 – 233 . OpenUrl CrossRef PubMed Web of Science ↵ Brynskov , C. , Eigsti , I. M. , Jørgensen , M. , Lemcke , S. , Bohn , O. S. , & Krøjgaard , P . ( 2017 ). Syntax and morphology in Danish-speaking children with autism spectrum disorder . Journal of Autism and Developmental Disorders , 47 ( 2 ), 373 – 383 . doi: 10.1007/s10803-016-2962-7 OpenUrl CrossRef ↵ Calder , S. D. , Brennan-Jones , C. G. , Robinson , M. , Whitehouse , A. , & Hill , E . ( 2023 ). How we measure language skills of children at scale: A call to move beyond domain-specific tests as a proxy for language . International journal of speech-language pathology , 1-9. doi: 10.1080/17549507.2023.2171488 OpenUrl CrossRef ↵ Charman , T. , Drew , A. , Baird , C. , & Baird , G . ( 2003 ). Measuring early language development in preschool children with autism spectrum disorder using the MacArthur Communicative Development Inventory (Infant Form) . Journal of Child Language , 30 ( 1 ), 213 – 236 . doi: 10.1017/S0305000902005482 OpenUrl CrossRef PubMed Web of Science ↵ Cleveland , L. H. , & Oetting , J. B . ( 2013 ). Children’s marking of verbal–s by nonmainstream English dialect and clinical status . American Journal of Speech-Language Pathology , 22 ( 4 ), 604 – 614 . doi: 10.1044/1058-0360(2013/12-0122) OpenUrl CrossRef PubMed ↵ Constantino , J. N. ( 2012 ). Social Responsiveness Scale , Second Edition . W. P. Services . ↵ Craig , H. K. , Thompson , C. A. , Washington , J. A. , & Potter , S. L . ( 2004 ). Performance of elementary-grade African American students on the Gray Oral Reading Tests . Language, Speech, and Hearing Services in Schools , 35 ( 2 ), 141 – 154 . doi: 10.1044/0161-1461(2004/015) OpenUrl CrossRef PubMed Web of Science ↵ Denman , D. , Cordier , R. , Kim , J.-H. , Munro , N. , & Speyer , R . ( 2021 ). What influences speech-language pathologists’ use of different types of language assessments for elementary school-age children? Language, Speech, and Hearing Services in Schools , 52 ( 3 ), 776 – 793 . doi: 10.1044/2021_LSHSS-20-00053 OpenUrl CrossRef ↵ Dunham , Y. , Stepanova , E. V. , Dotsch , R. , & Todorov , A . ( 2015 ). The development of race-based perceptual categorization: skin color dominates early category judgments . Developmental science , 18 ( 3 ), 469 – 483 . doi: 10.1111/desc.12228 OpenUrl CrossRef ↵ Dunn , D. M . ( 2019 ). Peabody Picture Vocabulary Test , Fifth Edition: Manual. Pearson . ↵ Eigsti , I.-M. , de Marchena , A. B. , Schuh , J. M. , & Kelley , E. ( 2011 ). Language acquisition in autism spectrum disorders: A developmental review . Research in Autism Spectrum Disorders , 5 ( 2 ), 681 – 691 . OpenUrl ↵ Eigsti , I. M. , & Fein , D. ( 2018 ). Optimal outcomes in ASD: Adult functioning, predictors, and mechanisms [Grant] . University of Connecticut . https://reporter.nih.gov/project-details/10308078 ↵ Friedmann , N . ( 2000 ). PETEL: A Sentence Repetition Test . T. A. University . ↵ Garrity , A. W. , & Oetting , J. B . ( 2010 ). Auxiliary BE production by African American English– speaking children with and without specific language impairment . Journal of Speech, Language, and Hearing Research , 53 ( 5 ), 1307 – 1320 . doi: 10.1044/1092-4388(2010/09-0016) OpenUrl CrossRef PubMed ↵ Georgiou , N. , & Spanoudis , G . ( 2021 ). Developmental language disorder and autism: Commonalities and differences on language . Brain Sciences , 11 ( 5 ), 589 . doi: 10.3390/brainsci11050589 OpenUrl CrossRef ↵ Girolamo , T. , Castro , N. , Fannin , D. K. , Ghali , S. , & Mandulak , K . ( 2022a ). Inequity in peer review in communication sciences and disorders . American Journal of Speech-Language Pathology , 31 ( 4 ), 1898 – 1912 . doi: 10.1044/2022_AJSLP-21-00252 OpenUrl CrossRef ↵ Girolamo , T. , Ghali , S. , Campos , I. , & Ford , A . ( 2022b ). Interpretation and use of standardized language assessments for diverse school-age individuals . Perspectives of the ASHA Special Interest Groups , 7 ( 4 ), 981 – 994 . doi: 10.1044/2022_PERSP-21-00322 OpenUrl CrossRef ↵ Girolamo , T. , Ghali , S. , & Eigsti , I.-M . ( 2023a ). A community-based approach to longitudinal language research with racially and ethnically minoritized autistic individuals: Lessons learned and new directions . American Journal of Speech-Language Pathology , 1-12. doi: 10.1044/2023_AJSLP-22-00341 OpenUrl CrossRef ↵ Girolamo , T. , & Rice , M. L . ( 2022 ). Language impairment in autistic adolescents and young adults . Journal of Speech, Language, and Hearing Research , 65 ( 9 ), 3518 – 3530 . doi: 10.1044/2022_JSLHR-21-00517 OpenUrl CrossRef ↵ Girolamo , T. , Shen , L. , Gulrick , A. M. , Rice , M. L. , & Eigsti , I.-M . ( 2023b ). Studies assessing domains pertaining to structural language in autism vary in reporting practices and approaches to assessment: A systematic review . Autism . doi: 10.1177/13623613231216155 OpenUrl CrossRef ↵ Girolamo , T. , Shen , L. , Gulrick , A. M. , Rice , M. L. , & Eigsti , I.-M . ( 2023c ). Studies pertaining to language impairment in autism underreport participant sociodemographics: A systematic review . Autism . doi: 10.1177/13623613231166749 OpenUrl CrossRef ↵ Harper-Hill , K. , Copland , D. , & Arnott , W . ( 2013 ). Do spoken nonword and sentence repetition tasks discriminate language impairment in children with an ASD? Research in Autism Spectrum Disorders , 7 ( 2 ), 265 – 275 . doi: 10.1016/j.rasd.2012.08.015 OpenUrl CrossRef ↵ Hendricks , A. E. , & Adlof , S. M . ( 2017 ). Language assessment with children who speak nonmainstream dialects: Examining the effects of scoring modifications in norm-referenced assessment . Language, Speech, and Hearing Services in Schools , 48 ( 3 ), 168 – 182 . doi: 10.1044/2017_LSHSS-16-0060 OpenUrl CrossRef ↵ Howlin , P . ( 1984 ). The acquisition of grammatical morphemes in autistic children: A critique and replication of the findings of Bartolucci, Pierce, and Streiner, 1980 . Journal of Autism and Developmental Disorders , 14 ( 2 ), 127 – 136 . doi: 10.1007/BF02409656 OpenUrl CrossRef PubMed ↵ Howlin , P. , Moss , P. , Savage , S. , & Rutter , M . ( 2013 ). Social outcomes in mid-to later adulthood among individuals diagnosed with autism and average nonverbal IQ as children . Journal of the American Academy of Child & Adolescent Psychiatry , 52 ( 6 ), 572 – 581 . e571. OpenUrl ↵ Howlin , P. , & Taylor , J. L . ( 2015 ). Addressing the need for high quality research on autism in adulthood . Autism , 19 ( 7 ), 771 – 773 . doi: 10.1177/1362361315595582 OpenUrl CrossRef PubMed ↵ Johnson , C. J. , Beitchman , J. H. , & Brownlie , E . ( 2010 ). Twenty-year follow-up of children with and without speech-language impairments: Family, educational, occupational, and quality of life outcomes . American Journal of Speech-Language Pathology , 19 ( 1 ), 51 – 65 . doi: 10.1044/1058-0360(2009/08-0083) OpenUrl CrossRef PubMed Web of Science ↵ Kertesz , A . ( 2022 ). The Western Aphasia Battery: A systematic review of research and clinical applications . Aphasiology , 36 ( 1 ), 21 – 50 . doi: 10.1080/02687038.2020.1852002 OpenUrl CrossRef ↵ Klem , M. , Melby-Lervåg , M. , Hagtvet , B. , Lyster , S. A. , Gustafsson , J. E. , & Hulme , C . ( 2015 ). Sentence repetition is a measure of children’s language skills rather than working memory limitations . Developmental Science , 18 ( 1 ), 146 – 154 . doi: 10.1111/desc.12202 OpenUrl CrossRef PubMed ↵ Korkman , M. , Kirk , U. , & Kemp , S. I . ( 1998 ). NEPSY: A developmental neuropsychological assessment . Psychological Corporation . ↵ Kover , S. T. , & Abbeduto , L . ( 2023 ). Toward equity in research on intellectual and developmental disabilities . American Journal on Intellectual and Developmental Disabilities , 128 ( 5 ), 350 – 370 . doi: 10.1352/1944-7558-128.5.350 OpenUrl CrossRef ↵ Kwok , E. Y. , Brown , H. M. , Smyth , R. E. , & Cardy , J. O . ( 2015 ). Meta-analysis of receptive and expressive language skills in autism spectrum disorder . Research in Autism Spectrum Disorders , 9 , 202 – 222 . doi: 10.1016/j.rasd.2014.10.008 OpenUrl CrossRef ↵ Landa , R. J. , & Goldberg , M. C . ( 2005 ). Language, social, and executive functions in high functioning autism: A continuum of performance . Journal of Autism and Developmental Disorders , 35 ( 5 ), 557 – 573 . doi: 10.1007/s10803-005-0001-1 OpenUrl CrossRef PubMed Web of Science ↵ Larson , C. , Rivera-Figueroa , K. , Thomas , H. R. , Fein , D. , Stevens , M. C. , & Eigsti , I.-M . ( 2022 ). Structural language impairment in autism spectrum disorder versus Loss of autism diagnosis: Behavioral and neural characteristics . NeuroImage: Clinical , 34 , 103043 . doi: 10.1016/j.nicl.2022.103043 OpenUrl CrossRef ↵ Larson , C. , Thomas , H. R. , Crutcher , J. , Stevens , M. C. , & Eigsti , I.-M . ( 2023 ). Language networks in ASD: A systematic review of connectivity-based fMRI studies . Review Journal of Autism and Developmental Disorders . doi: 10.1007/s40489-023-00382-6 OpenUrl CrossRef ↵ Loomes , R. , Hull , L. , & Mandy , W. P. L . ( 2017 ). What Is the male-to-female ratio in autism spectrum disorder? A systematic review and meta-analysis . Journal of the American Academy of Child and Adolescent Psychiatry , 56 ( 6 ), 466 – 474 . doi: 10.1016/j.jaac.2017.03.013 OpenUrl CrossRef PubMed ↵ Lopukhina , A. , Chrabaszcz , A. , Khudyakova , M. , Korkina , I. , Yurchenko , A. , & Dragoy , O. ( 2019 ). Test for assessment of language development in Russian «KORABLIK» . Satellite of AMLaP conference «Typical and Atypical Language Development Symposium» ↵ Lord , C. , Rutter , M. , DiLavore , P. C. , Risi , S. , Gotham , K. , & Bishop , S . ( 2012 ). Autism Diagnostic Observation Schedule-2nd edition . Western Psychological Services . ↵ Maenner , M. J. , Warren , Z. , Williams , A. R. , Amoakohene , E. , Bakian , A. V. , Bilder , D. A. , Durkin , M. S. , Fitzgerald , R. T. , Furnier , S. M. , Hughes , M. M. , Ladd-Acosta , C. M. , McArthur , D. , Pas , E. T. , Salinas , A. , Vehorn , A. , Williams , S. , Esler , A. , Grzybowski , A. , Hall-Lande , J. , … Shaw , K. A. ( 2023 ). Prevalence and characteristics of autism spectrum disorder among children aged 8 years — Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2020 . MMWR Surveillance Summaries , 72 , 1 – 14 . doi: 10.15585/mmwr.ss7202a1 OpenUrl CrossRef ↵ Magiati , I. , Tay , X. W. , & Howlin , P . ( 2014 ). Cognitive, language, social and behavioural outcomes in adults with autism spectrum disorders: a systematic review of longitudinal follow-up studies in adulthood . Clinical Psychology Review , 34 ( 1 ), 73 – 86 . doi: 10.1016/j.cpr.2013.11.002 OpenUrl CrossRef PubMed ↵ Manenti , M. , Tuller , L. , Houy-Durand , E. , Bonnet-Brilhault , F. , & Prévost , P . ( 2023 ). Assessing structural language skills of autistic adults: Focus on sentence repetition . Lingua , 294 , 103598 . doi: 10.1016/j.lingua.2023.103598 OpenUrl CrossRef ↵ Marques , J. , Abrams , D. , Paez , D. , & Martinez-Taboada , C . ( 1998 ). The role of categorization and in-group norms in judgments of groups and their members . Journal of Personality and Social Psychology , 75 ( 4 ), 976 – 988 . doi: 10.1037/0022-3514.75.4.976 OpenUrl CrossRef Web of Science ↵ Marques , J. M. , Yzerbyt , V. Y. , & Leyens , J.-P . ( 1988 ). The “Black Sheep Effect”: Extremity of judgments towards ingroup members as a function of group identification . European Journal of Social Psychology , 18 ( 1 ), 1 – 16 . doi: 10.1002/ejsp.2420180102 OpenUrl CrossRef Web of Science ↵ Maye , M. , Boyd , B. A. , Martínez-Pedraza , F. , Halladay , A. , Thurm , A. , & Mandell , D. S . ( 2022 ). Biases, barriers, and possible solutions: Steps towards addressing autism researchers under-engagement with racially, ethnically, and socioeconomically diverse communities . Journal of Autism and Developmental Disorders , 52 ( 9 ), 4206 – 4211 . doi: 10.1007/s10803-021-05250-y OpenUrl CrossRef ↵ McGregor , K. K. , Berns , A. J. , Owen , A. J. , Michels , S. A. , Duff , D. , Bahnsen , A. J. , & Lloyd , M . ( 2012 ). Associations between syntax and the lexicon among children with or without ASD and language impairment . Journal of Autism and Developmental Disorders , 42 ( 1 ), 35 – 47 . doi: 10.1007/s10803-011-1210-4 OpenUrl CrossRef PubMed ↵ Messick , S. ( 1990 ). Validity of test interpretation and use . Educational Testing Service . ↵ Miller , J. , & Iglesias , A. ( 2020 ). Systematic Analysis of Language Transcripts (SALT), Research Version 20 [Computer Software] . In SALT Software, LLC ,. ↵ Miniscalco , C. , & Carlsson , E . ( 2022 ). A longitudinal case study of six children with autism and specified language and non-verbal profiles . Clinical Linguistics & Phonetics , 36 ( 4-5 ), 398 – 416 . doi: 10.1080/02699206.2021.1874536 OpenUrl CrossRef ↵ Modyanova , N. , Perovic , A. , & Wexler , K . ( 2017 ). Grammar is differentially impaired in subgroups of autism spectrum disorders: Evidence from an investigation of tense marking and morphosyntax . Frontiers in Psychology , 8 , 320 . doi: 10.3389/fpsyg.2017.00320 OpenUrl CrossRef ↵ Musgrove , M. ( 2015 ). Dear Colleague Letter on speech and language services for students with autism spectrum disorder . Office of Civil Rights. U.S. Department of Education . https://sites.ed.gov/idea/idea-files/osep-dear-colleague-letter-on-speech-and-language-services-for-students-with-autism-spectrum-disorder/ ↵ Nasreddine , Z. S. , Phillips , N. A. , Bédirian , V. , Charbonneau , S. , Whitehead , V. , Collin , I. , Cummings , J. L. , & Chertkow , H. ( 2005 ). The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment . Journal of the American Geriatrics Society , 53 ( 4 ), 695 – 699 . doi: 10.1111/j.1532-5415.2005.53221.x OpenUrl CrossRef PubMed Web of Science ↵ Newman , L. , Wagner , M. , Huang , T. , Shaver , D. , Knokey , A.-M. , Yu , J. , Contreras , E. , Ferguson , K. , Greene , S. , Nagle , K. , & Cameto , R. ( 2011 ). Secondary school programs and performance of students with disabilities: A special topic report of findings from the National Longitudinal Transition Study-2 (NLTS2) ( NCSER 2012-3000 ). ↵ Nitido , H. , & Plante , E . ( 2020 ). Diagnosis of developmental language disorder in research studies . Journal of Speech, Language, and Hearing Research , 63 ( 8 ), 2777 – 2788 . doi: 10.1044/2020_JSLHR-20-00091 OpenUrl CrossRef ↵ Norbury , C. F. , Gooch , D. , Wray , C. , Baird , G. , Charman , T. , Simonoff , E. , Vamvakas , G. , & Pickles , A . ( 2016 ). The impact of nonverbal ability on prevalence and clinical presentation of language disorder: evidence from a population study . Journal of Child Psychology and Psychiatry , 57 ( 11 ), 1247 – 1257 . OpenUrl PubMed ↵ Oetting , J. B. ( 2020 ). General American English as a dialect: A call for change . Retrieved March 2, 2023, from https://leader.pubs.asha.org/do/10.1044/leader.FMP.25112020.12/full/ ↵ Oetting , J. B. , Berry , J. R. , Gregory , K. D. , Rivière , A. M. , & McDonald , J . ( 2019 ). Specific language impairment in African American English and Southern White English: Measures of tense and agreement with dialect-informed probes and strategic scoring . Journal of Speech, Language, and Hearing Research , 62 ( 9 ), 3443 – 3461 . doi: 10.1044/2019_JSLHR-L-19-0089 OpenUrl CrossRef ↵ Oetting , J. B. , & Garrity , A. W . ( 2006 ). Variation within dialects: A case of Cajun/Creole influence within child SAAE and SWE . Journal of Speech, Language, and Hearing Research , 49 ( 1 ), 16 – 26 . doi: 10.1044/1092-4388(2006/002) OpenUrl CrossRef PubMed ↵ Oetting , J. B. , & McDonald , J. L . ( 2001 ). Nonmainstream dialect use and specific language impairment . Journal of Speech, Language, and Hearing Research , 44 ( 1 ), 207 – 223 . doi: 10.1044/1092-4388(2001/018) OpenUrl CrossRef PubMed Web of Science ↵ Oetting , J. B. , McDonald , J. L. , Seidel , C. M. , & Hegarty , M . ( 2016 ). Sentence recall by children with SLI across two nonmainstream dialects of English . Journal of Speech, Language, and Hearing Research , 59 ( 1 ), 183 – 194 . doi: 10.1044/2015_JSLHR-L-15-0036 OpenUrl CrossRef ↵ Oetting , J. B. , Rivière , A. M. , Berry , J. R. , Gregory , K. D. , Villa , T. M. , & McDonald , J . ( 2021 ). Marking of tense and agreement in language samples by children with and without specific language impairment in African American English and Southern White English: Evaluation of scoring approaches and cut scores across structures . Journal of Speech, Language, and Hearing Research , 64 ( 2 ), 491 – 509 . doi: 10.1044/2020_JSLHR-20-00243 OpenUrl CrossRef ↵ Office of Management and Budget . ( 1997 ). Standards for Maintaining, Collecting, and Presenting Federal Data on Race and Ethnicity . https://obamawhitehouse.archives.gov/omb/fedreg_1997standards/ ↵ Pearson . ( 2023 ). Staying connected through telepractice . Author. Retrieved April 3, 2023, from https://www.pearsonassessments.com/professional-assessments/digital-solutions/telepractice/about.html ↵ Plaut , V. C . ( 2010 ). Diversity science: Why and how difference makes a difference . Psychological Inquiry , 21 ( 2 ), 77 – 99 . doi: 10.1080/10478401003676501 OpenUrl CrossRef Web of Science ↵ Pope , L. , Light , J. , & Franklin , A . ( 2022 ). Black children with developmental disabilities receive less augmentative and alternative communication intervention than their white peers: Preliminary evidence of racial disparities from a secondary data analysis . American Journal of Speech-Language Pathology , 31 ( 5 ), 2159 – 2174 . doi: 10.1044/2022_AJSLP-22-00079 OpenUrl CrossRef ↵ Prevost , P. , Zebib , R. , & Tuller , L . ( 2012 ). The LITMUS-SR-French . Université de Tours . ↵ Raven , J. , Rust , J. , Chan , F. , & Zhou , X. ( 2018 ). Raven’s 2 Progressive Matrices, Clinical Edition . Pearson . ↵ Rice , M. , & Wexler , K. ( 2001 ). Rice Wexler Test of Early Grammatical Impairment . Hove . ↵ Rice , M. L. , Wexler , K. , & Hershberger , S. ( 1998 ). Tense over time: The longitudinal course of tense acquisition in children with specific language impairment . Journal of Speech, Language, and Hearing Research , 41 ( 6 ), 1412 – 1431 . doi: 10.1044/jslhr.4106.1412 OpenUrl CrossRef PubMed Web of Science ↵ Riches , N. G. , Loucas , T. , Baird , G. , Charman , T. , & Simonoff , E . ( 2010 ). Sentence repetition in adolescents with specific language impairments and autism: an investigation of complex syntax . International Journal of Language & Communication Disorders , 45 ( 1 ), 47 – 60 . doi: 10.3109/13682820802647676 OpenUrl CrossRef ↵ Robinson , G. C. , & Norton , P. C . ( 2019 ). A decade of disproportionality: A state-level analysis of African American students enrolled in the primary disability category of speech or language impairment . Language, Speech, and Hearing Services in Schools , 50 ( 2 ), 267 – 282 . doi: 10.1044/2018_LSHSS-17-0149 OpenUrl CrossRef ↵ Russell , G. , Mandy , W. , Elliott , D. , White , R. , Pittwood , T. , & Ford , T . ( 2019 ). Selection bias on intellectual ability in autism research: A cross-sectional review and meta-analysis . Molecular Autism , 10 ( 1 ), 1 – 10 . doi: 10.1186/s13229-019-0260-x OpenUrl CrossRef ↵ Schaeffer , J. , Abd El-Raziq , M. , Castroviejo , E. , Durrleman , S. , Ferré , S. , Grama , I. , Hendriks , P. , Kissine , M. , Manenti , M. , & Marinis , T . ( 2023 ). Language in autism: domains, profiles and co-occurring conditions . Journal of Neural Transmission , 130 ( 3 ), 433 – 457 . doi: 10.1007/s00702-023-02592-y OpenUrl CrossRef ↵ Selin , C. M. , Rice , M. L. , Girolamo , T. M. , & Wang , C. J . ( 2022 ). Work setting effects on speech-language pathology practice: Implications for identification of children with specific language impairment . American Journal of Speech-Language Pathology , 31 ( 2 ), 854 – 880 . doi: 10.1044/2021_AJSLP-21-00024 OpenUrl CrossRef ↵ Semel , E. , Wiig , E. , & Secord , W . ( 1987 ). Clinical Evaluation of Language Fundamentals – Revised . Psychological Corporation . ↵ Semel , E. , Wiig , E. H. , & Secord , W. A . ( 2003 ). Clinical Evaluation of Language Fundamentals-Fourth Edition . Pearson . ↵ Seymour , H. N. , Bland-Stewart , L. , & Green , L. J . ( 1998 ). Difference versus deficit in child African American English . Language, Speech, and Hearing Services in Schools , 29 ( 2 ), 96 – 108 . doi: 10.1044/0161-1461.2902.96 OpenUrl CrossRef ↵ Seymour , H. N. , Roeper , T. W. , & de Villiers , J. ( 2018 ). Diagnostic Evaluation of Language Variation . Ventris Learning . ↵ Shriberg , L. D. , Lohmeier , H. L. , Campbell , T. F. , Dollaghan , C. A. , Green , J. R. , & Moore , C. A . ( 2009 ). A nonword repetition task for speakers with misarticulations: The Syllable Repetition Task (SRT) . Journal of Speech, Language, and Hearing Research , 52 ( 5 ), 1189 – 1212 . doi: 10.1044/1092-4388(2009/08-0047) OpenUrl CrossRef PubMed ↵ Silleresi , S. , Prévost , P. , Zebib , R. , Bonnet-Brilhault , F. , Conte , D. , & Tuller , L . ( 2020 ). Identifying Language and Cognitive Profiles in Children With ASD via a Cluster Analysis Exploration: Implications for the New ICD-11 . Autism Research , 13 ( 7 ), 1155 – 1167 . doi: 10.1002/aur.2268 OpenUrl CrossRef ↵ Spanoudis , G. , & Pahiti , J. ( 2014 ). Expressive and Receptive Language Evaluation: 5–12 Years of Age . University of Cyprus . ↵ Steinbrenner , J. R. , McIntyre , N. , Rentschler , L. F. , Pearson , J. N. , Luelmo , P. , Jaramillo , M. E. , Boyd , B. A. , Wong , C. , Nowell , S. W. , & Odom , S. L . ( 2022 ). Patterns in reporting and participant inclusion related to race and ethnicity in autism intervention literature: Data from a large-scale systematic review of evidence-based practices . Autism , 13623613211072593 . doi: 10.1177/13623613211072593 OpenUrl CrossRef ↵ Sukenik , N. , & Friedmann , N . ( 2018 ). ASD Is not DLI: Individuals With autism and individuals with syntactic DLI show similar performance level in syntactic tasks, but different error patterns [Original Research] . Frontiers in Psychology , 9 . doi: 10.3389/fpsyg.2018.00279 OpenUrl CrossRef ↵ Taylor , J. L. , & Henninger , N. A . ( 2015 ). Frequency and correlates of service access among youth with autism transitioning to adulthood . Journal of Autism and Developmental Disorders , 45 ( 1 ), 179 – 191 . doi: 10.1007/s10803-014-2203-x OpenUrl CrossRef PubMed ↵ Tomblin , J. B. , Records , N. L. , Buckwalter , P. , Zhang , X. , Smith , E. , & O’Brien , M . ( 1997 ). Prevalence of specific language impairment in kindergarten children . Journal of Speech, Language, and Hearing Research , 40 ( 6 ), 1245 – 1260 . doi: 10.1044/jslhr.4006.1245 OpenUrl CrossRef PubMed Web of Science ↵ Tyson , K. , Kelley , E. , Fein , D. , Orinstein , A. , Troyb , E. , Barton , M. , Eigsti , I.-M. , Naigles , L. , Schultz , R. T. , & Stevens , M . ( 2014 ). Language and verbal memory in individuals with a history of autism spectrum disorders who have achieved optimal outcomes . Journal of Autism and Developmental Disorders , 44 , 648 – 663 . doi: 10.1007/s10803-013-1921-9 OpenUrl CrossRef ↵ Wechsler , D . ( 1999 ). Wechsler Abbreviated Scale of Intelligence . The Psychological Corporation . ↵ West , E. A. , Travers , J. C. , Kemper , T. D. , Liberty , L. M. , Cote , D. L. , McCollow , M. M. , & Stansberry Brusnahan , L. L. ( 2016 ). Racial and ethnic diversity of participants in research supporting evidence-based practices for learners with autism spectrum disorder . The Journal of Special Education , 50 ( 3 ), 151 – 163 . OpenUrl CrossRef ↵ Whitehouse , A. J. O. , Barry , J. G. , & Bishop , D. V. M . ( 2008 ). Further defining the language impairment of autism: Is there a specific language impairment subtype? Journal of Communication Disorders , 41 ( 4 ), 319 – 336 . doi: 10.1016/j.jcomdis.2008.01.002 OpenUrl CrossRef PubMed Web of Science ↵ Wiig , E. H. , Secord , W. , & Semel , E . ( 2004 ). Clinical Evaluation of Language Fundamentals-Preschool , 2nd. Ed. Pearson . ↵ Wiig , E. H. , Semel , E. , & Secord , W . ( 2013 ). Clinical Evaluation of Language Fundamentals - 5th Ed. Pearson . ↵ Williams , K. T . ( 2019 ). Expressive Vocabulary Test-3 rd Ed.: Manual. Pearson . ↵ Wolfram , W. , & Schilling , N. ( 2015 ). American English: Dialects and variation ( 3rd ed.). John Wiley & Sons . View the discussion thread. 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