Bayesian Generalized Linear Mixed-Model Analysis of Language Samples: Detecting Patterns in Expository and Narrative Discourse of Adolescents With Traumatic Brain Injury

2021 ◽  
Vol 64 (4) ◽  
pp. 1256-1270
Author(s):  
Gavin Collins ◽  
Jennifer P. Lundine ◽  
Eloise Kaizar
Keyword(s):  
Traumatic Brain Injury ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Generalized Linear Mixed Model ◽  
Syntactic Complexity ◽  
Typical Development ◽  
Sample Analysis ◽  
Lexical Diversity ◽  
Language Sample Analysis ◽  
Language Sample

Purpose Generalized linear mixed-model (GLMM) and Bayesian methods together provide a framework capable of handling a wide variety of complex data commonly encountered across the communication sciences. Using language sample analysis, we demonstrate the utility of these methods in answering specific questions regarding the differences between discourse patterns of children who have experienced a traumatic brain injury (TBI), as compared to those with typical development. Method Language samples were collected from 55 adolescents ages 13–18 years, five of whom had experienced a TBI. We describe parameters relating to the productivity, syntactic complexity, and lexical diversity of language samples. A Bayesian GLMM is developed for each parameter of interest, relating these parameters to age, sex, prior history (TBI or typical development), and socioeconomic status, as well as the type of discourse sample (compare–contrast, cause–effect, or narrative). Statistical models are thoroughly described. Results Comparing the discourse of adolescents with TBI to those with typical development, substantial differences are detected in productivity and lexical diversity, while differences in syntactic complexity are more moderate. Female adolescents exhibited greater syntactic complexity, while male adolescents exhibited greater productivity and lexical diversity. Generally, our models suggest more advanced discourse among adolescents who are older or who have indicators of higher socioeconomic status. Differences relating to lecture type were also detected. Conclusions Bayesian and GLMM methods yield more informative and intuitive results than traditional statistical analyses, with a greater degree of confidence in model assumptions. We recommend that these methods be used more widely in language sample analysis. Supplemental Material https://doi.org/10.23641/asha.14226959

Assessing Functional Language in School-Aged Children Using Language Sample Analysis

2020 ◽  
Vol 5 (3) ◽  
pp. 622-636
Author(s):  
John Heilmann ◽  
Alexander Tucci ◽  
Elena Plante ◽  
Jon F. Miller
Keyword(s):  
Computer Hardware ◽  
School Age Children ◽  
Functional Language ◽  
School Age ◽  
Clinical Use ◽  
Sample Analysis ◽  
Validity And Reliability ◽  
Language Sample Analysis ◽  
Accuracy And Repeatability ◽  
Language Sample

Purpose The goal of this clinical focus article is to illustrate how speech-language pathologists can document the functional language of school-age children using language sample analysis (LSA). Advances in computer hardware and software are detailed making LSA more accessible for clinical use. Method This clinical focus article illustrates how documenting school-age student's communicative functioning is central to comprehensive assessment and how using LSA can meet multiple needs within this assessment. LSA can document students' meaningful participation in their daily life through assessment of their language used during everyday tasks. The many advances in computerized LSA are detailed with a primary focus on the Systematic Analysis of Language Transcripts (Miller & Iglesias, 2019). The LSA process is reviewed detailing the steps necessary for computers to calculate word, morpheme, utterance, and discourse features of functional language. Conclusion These advances in computer technology and software development have made LSA clinically feasible through standardized elicitation and transcription methods that improve accuracy and repeatability. In addition to improved accuracy, validity, and reliability of LSA, databases of typical speakers to document status and automated report writing more than justify the time required. Software now provides many innovations that make LSA simpler and more accessible for clinical use. Supplemental Material https://doi.org/10.23641/asha.12456719

Issues in Language Sample Collection and Analysis With Children Using AAC

2014 ◽  
Vol 23 (2) ◽  
pp. 65-74 ◽  
Author(s):  
Gail Van Tatenhove
Keyword(s):  
Clinical Practice ◽  
Augmentative And Alternative Communication ◽  
Language Production ◽  
Expressive Language ◽  
Sample Analysis ◽  
Alternative Communication ◽  
Sample Collection ◽  
Speech Language Pathologists ◽  
Language Sample Analysis ◽  
Language Sample

Language sample analysis is considered one of the best methods of evaluating expressive language production in speaking children. However, the practice of language sample collection and analysis is complicated for speech-language pathologists working with children who use augmentative and alternative communication (AAC) devices. This article identifies six issues regarding use of language sample collection and analysis in clinical practice with children who use AAC devices. The purpose of this article is to encourage speech-language pathologists practicing in the area of AAC to utilize language sample collection and analysis as part of ongoing AAC assessment.

Analyzing discontinuities in longitudinal count data: A multilevel generalized linear mixed model.

2020 ◽  
Author(s):  
James L. Peugh ◽  
Sarah J. Beal ◽  
Meghan E. McGrady ◽  
Michael D. Toland ◽  
Constance Mara
Keyword(s):  
Count Data ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Generalized Linear Mixed Model ◽  
Longitudinal Count Data

scholarly journals Reducing Aggression by Developing Emotional and Inhibitory Control

2021 ◽  
Vol 18 (10) ◽  
pp. 5263
Author(s):  
Miriam Romero-López ◽  
María Carmen Pichardo ◽  
Ana Justicia-Arráez ◽  
Judit Bembibre-Serrano
Keyword(s):  
Psychosocial Development ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Generalized Linear Mixed Model ◽  
Emotional Control ◽  
Control Group ◽  
Post Intervention ◽  
Mixed Model Analysis ◽  
Intervention Measures ◽  
Experimental Group

The objective of this study is to measure the effectiveness of a program on improving inhibitory and emotional control among children. In addition, it is assessed whether the improvement of these skills has an effect on the reduction of aggressive behavior in pre-school children. The participants were 100 children, 50 belonging to the control group and 50 to the experimental group, aged between 5 and 6 years. Pre-intervention and post-intervention measures of inhibitory and emotional control (BRIEF-P) and aggression (BASC) were taken. A Generalized Linear Mixed Model analysis (GLMM) was performed and found that children in the experimental group scored higher on inhibitory and emotional control compared to their peers in the control group. In addition, these improvements have an effect on the decrease in aggressiveness. In conclusion, preventive research should have among its priorities the design of such program given their implications for psychosocial development.

scholarly journals Changes in Dairy Cow Behavior with and without Assistance at Calving

Agriculture ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 722
Author(s):  
Bethan Cavendish ◽  
John McDonagh ◽  
Georgios Tzimiropoulos ◽  
Kimberley R. Slinger ◽  
Zoë J. Huggett ◽  
...  
Keyword(s):  
Dairy Cows ◽  
Video Surveillance ◽  
Dairy Cow ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Generalized Linear Mixed Model ◽  
Behavioral Data ◽  
Behavioral Patterns ◽  
Holstein Friesian ◽  
Time Periods

The aim of this study was to characterize calving behavior of dairy cows and to compare the duration and frequency of behaviors for assisted and unassisted dairy cows at calving. Behavioral data from nine hours prior to calving were collected for 35 Holstein-Friesian dairy cows. Cows were continuously monitored under 24 h video surveillance. The behaviors of standing, lying, walking, shuffle, eating, drinking and contractions were recorded for each cow until birth. A generalized linear mixed model was used to assess differences in the duration and frequency of behaviors prior to calving for assisted and unassisted cows. The nine hours prior to calving was assessed in three-hour time periods. The study found that the cows spent a large proportion of their time either lying (0.49) or standing (0.35), with a higher frequency of standing (0.36) and shuffle (0.26) bouts than other behaviors during the study. There were no differences in behavior between assisted and unassisted cows. During the three-hours prior to calving, the duration and bouts of lying, including contractions, were higher than during other time periods. While changes in behavior failed to identify an association with calving assistance, the monitoring of behavioral patterns could be used as an alert to the progress of parturition.

scholarly journals Analyzing Child Malnutrition in Bangladesh: Generalized Linear Mixed Model Approach

2016 ◽  
Vol 64 (2) ◽  
pp. 163-167
Author(s):  
Tahmidul Islam ◽  
Md Golam Rabbani ◽  
Wasimul Bari
Keyword(s):  
Mixed Model ◽  
Sampling Design ◽  
Linear Mixed Model ◽  
Generalized Linear Mixed Model ◽  
Child Malnutrition ◽  
Demographic Health Survey ◽  
Factors Affecting ◽  
Mixed Model Approach ◽  
Potential Factors ◽  
Model Approach

Child malnutrition is a serious issue for overall child health and future development. Stunting is a key anthropometric indicator of child malnutrition. Because of the nature of sampling design used in Bangladesh Demographic Health Survey, 2011, responses obtained from children under same family might be correlated. Again, children residing in same cluster may also be correlated. To tackle this problem, generalized linear mixed model (GLMM), instead of usual fixed effect logistic regression model, has been utilized in this paper to find out potential factors affecting child malnutrition. Model performances have also been compared. Dhaka Univ. J. Sci. 64(2): 163-167, 2016 (July)

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