Procedures

Experiment 1 was completed during Visit 1 after all inclusion criteria were met and the participants were given a bathroom break. Spontaneous, play-based language samples were collected using procedures adapted from Rispoli et al. (2009). Language samples were collected in a play area in the participants’ home environments. One language sample was collected in an office board room. A controlled set of age-appropriate toys was provided for the participants to play with to standardize language sampling protocols as much as possible given variation across the participants’ home environments. The toys included a ball, a work bench and hammer, wordless picture books to facilitate labeling, a set of Little Red Riding Hood puppet characters to facilitate narratives, a baby doll to facilitate feeding and bathing routines, cars and a play mat featuring a variety of community settings (hospital, police station, etc.) to facilitate pretend play, and a Little Tikes® Cook n’ Store Kitchen to facilitate pretend play. These toys were selected for a mobile language sampling lab because they facilitated robust language use about a range of topics and they could be transported and set up with minimal difficulty. Stimulus toys are shown in Figure 3.2.

Following Rispoli et al. (2009), parents were instructed to talk with their child as they normally would at home. Language samples were recorded using a stationary video camera.

The author monitored equipment and observed from an adjacent area to minimize interaction with participants during the recording session. The author responded politely any time participants addressed him directly.

Language samples were recorded for one continuous hour. Some samples were ended a few minutes early to accommodate participant schedules or to limit the total time needed to

Figure 3.2. Portable play set for Experiment 1 language sample task.

Language samples were transcribed using C-unit segmentation (Loban, 1976). C-unit segmentation is a transcription process that follows syntactic rules, which can be used to identify utterances in both spoken and text-based language data. A C-unit is structurally defined as “an independent clause and its modifiers” (Loban, 1976, p. 9). Mazes were excluded from analysis.

All language samples were transcribed in CLAN software (MacWhinney, 2000) using the CHAT transcription and coding format (MacWhinney, 2000). The CHAT format is the preferred transcription and coding format used for transcribing and coding language sample data contributed to the CHILDES database (MacWhinney, 2000) after analysis. CLAN software is a program used for transcribing and analyzing spoken language samples in the CHAT format.

CLAN software was used to create time-stamped linkages between video recorded language samples data and corresponding text transcripts.

Following Rispoli et al. (2009), non-spontaneous utterances were excluded from morpheme analysis. Rispoli et al. (2009, p. 934) define non-spontaneous utterances as

“immediate imitations of adult, self, songs, counting, etc.” or other instances in which there is evidence that a child did not generate an utterance. Rispoli et al. (2009) argued that instances of morpheme use activated through associative mechanisms can inflate measures of language performance above a child’s true developmental level. Their measures of spontaneous tense marker use are intended to capture instances of tense marker use in formulated word combinations and exclude instances of tense marker use that are directly activated through associative connections. The conservative step of excluding non-spontaneous utterances from analysis helps filter out instances of tense marker use activated through associative connections.

Each transcript began at the beginning of the child’s recorded language sample and continued until the child produced 150 spontaneous novel utterances with at least two

morphemes. These 150 multi-morpheme child utterances were selected for analysis. Gladfelter and Leonard (2013) found that tense marker total and productivity score measures could differentiate between typically developing 4-5 year olds and those with specific language impairment in language samples with a controlled number of total utterances. They argued that discriminability of these measures may be specific to sample sizes that approximate their samples of 152 utterances because these measures are totals influenced by each new instance of a scorable morpheme. Larger samples may suppress evidence of developmental patterns if participant performance approaches ceiling levels.

The transcribed samples of 150 multi-morpheme child utterances were analyzed using CLAN. The mor program in CLAN was used to automatically add a %mor tier to the transcribed samples with tags on each morpheme. The morpheme tags for tense markers were then manually reviewed to verify the automatically generated tags and make corrections as needed. A series of searches were then run through CLAN to extract all child utterances containing tags corresponding to each of the 15 different tense markers.

Mean syntactic length (MSL) was obtained as a measure of utterance length and used to calculate a predicted mean length of utterance in morphemes (predicted MLUm). MSL is defined as the average number of morphemes per utterance with single-morpheme utterances excluded (Klee & Fitzgerald, 1985). MSL could be obtained from utterances included in analysis even though single-morpheme utterances were excluded (Kovacs & Hill, 2017). Kovacs and Hill (2017) found that MSL predicted MLUm in typically developing children in the single word stage and Brown’s Stages I-V. Predicted MLUm was found for each participant using Kovacs and Hill’s (2017) regression formula.

Tense marker use was reported for each individual tense marker as a binary variable indicating whether or not the child used each individual tense marker at least once in the language sample. Hadley and Short (2005) considered a single grammatically correct, spontaneous production of a given tense marker in a language sample to be sufficient to be evidence that that tense marker was at least emergent. Overregularizations of –ed were counted as instances of correct –ed use. Contractions to pronominal subjects were not counted because these high frequency patterns are likely to be directly activated through associative connections (Hadley & Short, 2005). The irregular forms don’t and ain’t were not counted because they are specifically negative (Rispoli & Hadley, 2011). Tense marker total (Hadley & Short, 2005;

Rispoli & Hadley, 2011) was reported as the total number of tense markers the child used at least once in the language sample (range: 0-15).

Category productivity scores were found to characterize the productivity of each morpheme category. The number of sufficiently different uses of tense markers in each morpheme category was counted using Hadley and Short’s (2005) operational criteria for sufficiently different uses of each morpheme category. For -ed and -3s, sufficiently different uses were counted for each lexical verb that was correctly inflected using the tense marked suffix, including overregularizations. For COPULA BE, AUXILIARY BE, and AUXILIARY DO, sufficiently different uses were counted for each different subject-tense marker combination that the child generated. Contractions to pronominal subjects and the specifically negative forms don’t, ain’t were not counted (Hadley & Short, 2005; Rispoli & Hadley, 2011). The first 5 sufficiently different uses were counted towards the category productivity score for each morpheme category. Additional uses were not counted. A composite productivity score (range:

0-25) was found by calculating the sum of the five category productivity scores.

Inter-judge agreement for language sample transcription was achieved using transcription-by-consensus (Hill, Kovacs, & Shin, 2014). Inter-judge agreement was found for language samples from two randomly selected children ages 30-42 months and two children randomly selected children ages 43-54 months. A two-step process was used to achieve 100%

consensus for utterance transcription and tense-marked morpheme tagging. In the first step, consensus was achieved for transcription of utterances in the selected language samples. A second, trained rater independently reviewed video recordings of these language samples and the speaker tier of linked transcripts generated by the author. The second rater independently made corrections on the speaker tier of the author’s transcripts when she found discrepancies between the author’s transcripts and the words spoken in the actual recordings. Once this was completed, the author and second rater met and resolved all transcription discrepancies by consensus.

Morpheme tags in the final transcripts were reviewed using a similar process to achieve a consensus on morpheme tagging.