Interactive Book Reading to Accelerate Word Learning by Children With SLI
Phase: NA
Conditions: Language Development Disorders
Sponsor: University of Kansas

O"icial Title: Kindergarten Children Acquiring Words Through Storybook Reading 
(KAWStory) 
NCT number: NCT03586479 
Date of the Document: 8/31/2018 

Study Information from https://osf.io/rh9ps 
Title 
Kindergarten Children Acquiring Words through Storybook Reading (DC012824), Study 3 
Research Questions 
3.1. What amount of testing during treatment (low, medium, high): (a) enhances learning 
during treatment and/or (b) reduces forgetting post-treatment? 3.2. What pre-treatment 
characteristics of kindergarten children with Developmental Language Disorder (DLD) are 
associated with (a) learning during treatment and/or (b) forgetting post-treatment? 3.3. At 
what point (a) during treatment can learning outcomes be accurately predicted; and (b) 
during post-treatment monitoring can retention outcomes be accurately predicted? 
Hypotheses 
3.1. (a) Lower testing during treatment will lead to steeper positive slopes during treatment 
than higher testing. The exact pattern of the treatment conditions is not predicted (e.g., low 
and mid better than high versus low better than mid and high versus low better than mid 
better than high) (b) Higher testing during treatment will lead to smaller negative slopes 
(i.e., less forgetting) post-treatment than lower testing. The exact pattern of the treatment 
conditions is not predicted (e.g., high and mid better than low versus high better than mid 
and low versus high better than mid better than low). 3.2. (a) Higher language (speciﬁcally 
vocabulary and narrative skills), working memory, and/or episodic learning scores will be 
associated with steeper positive slopes during treatment. (b) Better treatment learning 
and/or decay scores will be associated with smaller negative slopes post-treatment, 
indicating less forgetting. 3.3. (a) Accuracy in predicting learning outcomes will increase at 
each treatment point, and there will be a point during treatment when outcomes can be 
accurately predicted. The speciﬁc earliest point in treatment (reading 1, 2, 3, 4, or 5) when 
accurate predictions can be made is unknown without any speciﬁc hypothesis. (b) 
Accuracy in predicting retention outcomes will increase at each post-treatment point, and 
there will be a point during post-treatment when outcomes can be accurately predicted. 
The speciﬁc earliest point in post-treatment (immediate, 4-week, 8-week) when accurate 
predictions can be made is unknown without any speciﬁc hypothesis. 
Sampling Plan 
Existing Data 
Registration prior to creation of data 
Explanation of existing data 

N/A -- Not using existing data 
Data collection procedures 
See attached ﬁle for details. 
• 
Data collection procedures.pdf 
Sample size 
Sixty kindergarten children with DLD will participate. Children will be randomized to one of 
the 3 treatment manipulations (see manipulated variables) yielding 20 children per 
treatment condition. For each child deﬁnition and naming data will be collected for 30 
treated words and 30 control words: 1 time pre-treatment, 6 times during treatment 
(repeated book readings 1-6), and 4 times post-treatment (immediate, 4-weeks, 8-weeks, 
and 12-weeks post-treatment). 
Sample size rationale 
3.1. Power Analysis for Research Question 3.1. Monte Carlo studies were conducted using 
Mplus to determine power to detect treatment differences in slope across 7 (treatment) or 
5 (retention) data points. Slope reliabilities of .6 and .8 were considered in the speciﬁcation 
of the population parameters. Both linear growth models and latent basis models with 
nonlinear growth were examined given that the expected shape of the trajectories is 
uncertain. Intraclass Correlation Coefficients (ICCs) from our prior studies indicated that 
between 10% and 30% of the variance in word learning was between persons. Thus, ICCs 
of both .10 and .30 were used in the power analysis. We will have .80 power or greater to 
detect moderate slope differences (d =.5) between two groups of 20 individuals in the 
treatment phase with 7 data points. In the post-treatment phase, with 5 data points, we will 
have .80 power to detect slightly larger slope differences (d =.6). Attrition has been minimal 
(1.9%). Thus, we will recruit additional children, as needed, to replace any children who do 
not complete the study. We will check the assumptions used in our power analysis yearly 
and update target sample size to maintain power. 3.2. Power Analysis for Research 
Question 3.2. Additional Monte Carlo studies were conducted using the same software, 
intraclass correlation coefficients, and slope reliabilities as for RQ3.1. Prior research by the 
team has indicated that correlations between predictors and learning are in the range of .4 
and .5. Power to detect the relationship between level two predictors of this magnitude and 
slopes added to the treatment model will range from .70 to .85. Power to detect the main 
effect of each covariate at the last treatment point will similarly range from .73 to .87. 
Although power appears sufficient with a sample size of 60, an alternative approach if 
power is insufficient is to loosen our criteria for statistical signiﬁcance and search for 
“promising” key moderators. Promising key moderators would need further testing as the 

development of this treatment moves forward but would inform our understanding of 
which children with DLD may beneﬁt from the treatment, addressing the research 
question. 3.3 There is no speciﬁc power analysis for research question 3.3. 
Stopping rule 
Power estimates based on simulations indicate that a total of 60 participants will be 
needed to detect the expected effects. Because we are interested in outcomes that are 
assessed one year after treatment, and there is minimal risk to treatment participation, we 
will not stop study recruitment until we have reached our recruitment goal.  
Variables 
Manipulated variables 
See attached ﬁle for details. 
• 
Manipulate Variables.pdf 
Measured variables 
See attached ﬁle for details. 
• 
Measured variables.pdf 
Indices 
No indices of any type will be calculated. Rather we will use all of the scores described in 
the measures section for the proposed analyses. 
No ﬁles selected 
Design Plan 
Study type 
Experiment - A researcher randomly assigns treatments to study subjects, this includes 
ﬁeld or lab experiments. This is also known as an intervention experiment and includes 
randomized controlled trials. 
Blinding 
• 
For studies that involve human subjects, they will not know the treatment group to 
which they have been assigned. 
• 
Research personnel who interact directly with the study subjects (either human or 
non-human subjects) will not be aware of the assigned treatments. 

• 
Research personnel who analyze the data collected from the study are not aware of 
the treatment applied to any given group. 
Study design 
The study is a three-group randomized design, with each participant being randomly 
assigned to one of three conditions. The design is mixed as there are both between 
participant (treatment condition) and within participant (word list) factors of interest. The 
factors will be counterbalanced to ensure that both word lists are represented in each 
treatment condition. Additional notes on blinding of research personnel who interact 
directly with subjects: The research assistant who administers the treatment cannot be 
blind to the treatment condition. They must know the treatment condition to correctly 
deliver it. This same research assistant collects during treatment because it would be cost 
prohibitive to send a second research assistant to every treatment to collect these data. 
Blinding of research personnel occurs in the following ways: (1) The research assistant (RA) 
administering pre- and post-treatment testing is blind to condition assignment to reduce 
bias. (2) All tests are scored by RAs who are blind to the treatment condition to reduce bias. 
No ﬁles selected 
Randomization 
Random assignment to the three treatment condition occurs at the participant level. 
Participants will also be randomly assigned to one of two treated word lists within each 
treatment condition.  
Analysis Plan 
Statistical models 
Research Question 3.1: We will use a multi-level modeling approach for (a) learning during 
treatment (7 observations: pre-treatment, repeated readings 1-6); (b) post-treatment 
forgetting (5 observations: reading 6, immediate, 4-week, 8-week, 12-week post). We will 
examine deﬁnition and naming data separately, yielding four models: treatment deﬁnition, 
treatment naming, post-treatment deﬁnition, post-treatment naming. For each model, 
observations at each test (level 1) are available for each participant (level 2). The expected 
shape of the trajectories is not known so the analytic approach will use either linear growth 
modeling or latent basis modeling, depending on which is most appropriate. Participants 
are randomly assigned to treatment (low, mid, high testing), and treatment will be included 
in the model as a ﬁxed factor at level 2. The dependent variable is number of words correct. 
This count variable will likely not ﬁt a normal distribution, and thus, an appropriate link 
function, such as a Poisson distribution, will be used. The research aim is to detect 

differences between treatment conditions, therefore all possible pairwise comparisons will 
be conducted rather than an omnibus test. The slope by treatment condition interaction 
will be the effect of interest in determining whether there is differential learning or 
forgetting across low, mid, high testing. While the focus of our analyses is on treated words, 
a similar modeling approach will be used to model any learning of control words during 
treatment. We expect relatively ﬂat trajectories for control words in all three treatment 
conditions and this will allow for inferences to be made about the effect of treatment rather 
than development or maturation Research Question 3.2: We will add pre-treatment 
measures as covariates to the models used in RQ3.1 to examine the relationship between 
these covariates and (a) learning slopes during treatment and (b) forgetting slopes post-
treatment. Recall that there are several skills relevant for learning (language, attention, 
working memory, episodic learning) and forgetting (treatment learning, decay) and that we 
have multiple measures to tap different aspects of each skill (see measured variables 
table). Likewise, we also will examine biological and environmental variables as predictors. 
Previous research has indicated that the test measures are moderately correlated at 
around r =.50. Since these measures are theoretically and mathematically distinct (which 
we will conﬁrm via correlations), we will examine the effect of each of these predictors 
individually. We hypothesize that higher language, attention, working memory, and/or 
episodic learning scores will be associated with steeper slopes during treatment. Similarly, 
we hypothesize that better treatment learning and decay scores will be associated with 
smaller slopes, indicating less decline post-treatment. These hypotheses are consistent 
with theories of learning and forgetting. However, it is likely that not all of these covariates 
will be signiﬁcantly related to performance. Research Question 3.3: We will classify 
responses at the end of treatment (learner vs. non-learner) and post-treatment 
(rememberer vs. forgetter). Then, earlier performance will be analyzed to determine when 
treatment and post-treatment outcomes can be predicted. This yields empirically based 
benchmarks for progress and an understanding of the stability of learning and forgetting 
over time. As with RQ 3.1 and 3.2, we will separate treatment and post-treatment data as 
well as deﬁnition and naming data. Within each dataset, we will classify each child as 
having a negative or positive outcome based on performance at the last point in the series 
(i.e., reading 6 for treatment, 12-month post for post-treatment). We will use the accuracy 
range for the untreated control words to determine the cut-offs for this classiﬁcation. Then, 
we will select an appropriate cut-off score at each earlier point during treatment or post-
treatment. Because we have decided that the cost of false positives is greater than that of 
false negatives (see progress report), we will select cutoffs that minimize false positives. At 
each test point, measures of sensitivity, speciﬁcity, and positive/negative likelihood ratios 
and predictive values will be calculated. We will then determine the earliest point when we 
can accurately predict treatment and post-treatment outcomes 

No ﬁles selected 
Transformations 
One of the primary dependent variables is a count of the number of words correct which 
will not ﬁt a normal distribution. Rather than transforming the variable, an appropriate link 
function such as a Poisson distribution will be used. We will center time at the last 
intervention reading point for all models. 
Follow-up analyses 
The analysis plan for RQ 3.1 describes the pair-wise contrasts that will be used to follow-up 
on signiﬁcant interactions. The analysis plan for 3.3 describes the subgroup analyses we 
will conduct for learners vs non-learners and forgetters vs. rememberers. 
Inference criteria 
Likelihood ratio tests will be used to compare nested models and determine what random 
and ﬁxed effects are needed for the growth models as well as the signiﬁcance of the 
treatment effects. 
Data exclusion 
We will analyze all available data. If extreme outliers occur, we will run the models both 
including and excluding the outliers and report both sets of models. 
Missing data 
The proposed analyses will be conducted within SAS Proc MIXED, SAS Proc GLIMMIX, or 
MPLUS using maximum likelihood estimation which will include all available data in the 
multidimensional likelihood function. 
Exploratory analysis 
If the there is evidence in the analysis for RQ3.1 that the amount of testing inﬂuences the 
number of treated words learned, we will examine whether there are differences between 
low, mid, and high testing in post-treatment broader vocabulary measures (DELV Semantic, 
CELF-4 Word Classes, CCC-2 Semantic, and vocabulary measures derived from 
interview/narrative tasks). The treatment is relatively short, which may limit broader 
change.