This study employed the Teacher Behavior Rating Scale (TBRS) to investigate two aspects of sociability, likeability and prosocial behavior, in 143 children with developmental language disorder (DLD) and 131 of their typically developing peers. Initially, measurement invariance analysis was performed to determine if teachers evaluated likeability and sociability in a similar manner for both children with DLD and their typically developing peers. Likeability items on the TBRS were invariant, and 4 of the 5 prosociability items were invariant. Subsequent analysis revealed that teachers rated children with DLD lower in both likeability and prosociability in comparison to their typically developing peers. The results of this study suggest that children with DLD are not fully accepted by their peers, nor do they engage in the helpful, comforting behaviors that encourage peer acceptance and build friendships.

The recent advances in deep learning have made it state-of-the-art for many different tasks, making its potential usefulness for analyzing electroencephalography (EEG) data appealing. This study aims at automatic feature extraction and classification of likeability, valence, and arousal elicited by auditory stimuli from music by training deep neural networks (DNNs) on  minimally pre-processed multivariate EEG time series. Two data sets were collected, the first containing 840 samples from 21 subjects, the second containing 400 samples from a single subject. Each sample consists of a 30 second EEG stream which was recorded during music playback. Each subject in the multiple subject data set was played 40 different songs from 8 categories, after which they were asked to self-label their opinion of the song and the emotional response it elicited. Different pre- processing and data augmentation methods were tested on the data before it was fed to the DNNs. Three different network architectures were implemented and tested, including a one-dimensional translation of ResNet18, InceptionTime, and a novel architecture built upon from InceptionTime, dubbed EEGNet. The classification tasks were posed both as a binary and a three-class classification problem. The results from the DNNs were compared to three different methods of handcrafted feature extraction. The handcrafted features were used to train LightGBM models, which were used as a baseline. The experiments showed that the DNNs struggled to extract relevant features to discriminate between the different targets, as the results were close to random guessing. The experiments with the baseline models showed generalizability indications in the data, as all 36 experiments performed better than random guessing. The best results were a classification accuracy of 64 % and an AUC of 0.638 for valence on the multiple subject data set. The background study discovered many flaws and unclarities in the published work on the topic. Therefore, future work should not rely too much on these papers and explore other network architectures that can extract the relevant features to classify likeability and emotion from EEG data. / Djupinlärning har visat sig vara effektivt för många olika uppgifter, vilket gör det möjligt att det även kan användas för att analysera data från elektroencefalografi (EEG). Målet med denna studie är att genom två egeninsamlade dataset försöka klassificera huruvida någon gillar en låt eller inte samt vilka känslor låten väcker genom att träna djupa neurala nätverk (DNN) på minimalt pre-processade EEG-tidsserier.  För det första datasettet samlades 840 dataexempel in från 21 deltagare. Dessa fick lyssna på 30-sekunders snuttar av 40 olika låtar från 8 kategorier varvid de fick svara på frågor angående vad de tyckte om låten samt vilka känslor den väckte. Det andra datasettet samlade in 400 dataexempel från endast en deltagare. Datan blev behandlad med flera olika metoder för att öka antalet träningsexempel innan det blev visat för de neurala nätverken. Tre olika nätverksarkitekturer implementerades och testades; en endimensionell variant av ResNet18, InceptionTime samt en egenbyggd arkitektur som byggde vidare på InceptionTime, döpt till EEGNet. Nätverken tränades både för binär och tre-klass klassificering.  Resultaten från nätverken jämfördes med tre olika metoder för att bygga egna prediktorer från EEG-datan. Dessa prediktorer användes för att träna LightGBM modeller, vars resultat användes som baslinje. Experimenten visade att DNNsen hade svårt att extrahera relevanta prediktorer för att kunna diskriminera mellan de olika klasserna, då resultaten var nära till godtyckligt gissande. Experimenten med LightGBM modellerna och de handgjorda prediktorerna visade dock indikationer på att det finns relevant information i datan för att kunna prediktera ett visst utfall, då alla 36 experiment presterade bättre än godyckligt gissande. Det bästa resultatet var 64 % träffsäkerhet för valens och binär klassificering, med en AUC på 0.638, för datasettet med många deltagare. Bakgrundsstudien upptäckte många oklarheter och fel i flera av de artiklar som är publicerade på ämnet. Framtida arbete bör därför inte förlita sig på denna alltför mycket. Den bör fokusera på att utveckla arkitekturer som klarar att extrahera de relevanta prediktorer som behövs för att kunna prediktera huruvida någon tycker om en låt eller inte samt vilka känslor denna väckte.

This study examines the relationship between syntactic and pragmatic language and reticence, solitary-active passive withdrawal, solitary-passive withdrawal, prosocial skills, and likeability. The Children's Communication Checklist (CCC-2), a language checklist, and Teacher Behavior Rating Scale (TBRS), a behavior checklist, were completed by three 2nd-grade teachers and three 4th-grade teachers about each of their students. Factor analysis was used to determine two composite language measures from the CCC-2 scales. The results of two hierarchal regression analyses indicated that social behaviors were significant predictors of pragmatic language, but not structural language. In particular, solitary-passive withdrawal and reticence were significant predictors of pragmatic language deficits.

The topic of information transparency is becoming increasingly important in the online B2C (business-to-consumer) clothing industry. While both information transparency and perceived brand credibility are already known to be important for the firm, research shows that it is challenging to understand how information transparency influences brand credibility. It is because of the rapid growth of the online clothing and fashion industry that the significance of this research is rooted. Therefore, this paper set out to explore the influence of information transparency on perceived brand credibility in the online B2C clothing industry by performing a qualitative analysis. Furthermore, this research takes inspiration from existing literature that was conducted on information transparency. This research applies the same dimensions of information transparency from the existing literature by Zhou, et al., (2018) – namely product, vendor, and transaction transparency –  to act as a framework for this research and understand how they act to influence the perceptions of brand credibility. Three brand credibility dimensions put forth by Keller (2013) – perceived expertise, trustworthiness, and likeability – were used to understand what brand credibility entails. It was found that product transparency and vendor transparency influence perceptions of brand credibility through the dimensions of trustworthiness and likeability, while transaction transparency influences perceptions of brand credibility through all dimensions of brand credibility as specified by Keller, (2013).

本研究旨在探討不同甄選情境中（視聽組、聽覺組、文字組），「人格印象」、「能力評估」、「喜好程度」及「應對表現」對「口試成績」的影響。 本研究以實驗法進行，採3「甄選情境」× 5「考生」二因子混合設計。其中「甄選情境」分為「視聽組」、「聽覺組」及「文字組」三組，「考生」則分為「甲生」、「乙生」、「丙生」、「丁生」及「戊生」五位。每組均由17位口試委員對5位考生進行評分。由於三組之口試委員不同，各組之評分不會彼此影響，因此「甄選情境」為獨立樣本，即受試者間設計；五位考生均會接受17位口試委員之評分，因此「考生」乃相依樣本，即受試者內設計。 本研究以碩博士班研究生為研究對象，請他們在觀看（聆聽或閱讀）口試錄影帶（謄本）後，以大學推甄口試委員的立場對影片中人物所形成之「人格印象」、「能力評估」、「喜好程度」及「應對表現」予以評分，並評定其「口試成績」。本研究採隨機分配，將研究對象分為「視聽組」、「聽覺組」及「文字組」三組。每組17位口試委員，共計51位。在觀看（聆聽或閱讀）口試錄影帶（謄本）前，受試者有5分鐘時間閱覽考生之書面資料，然後在觀看（聆聽或閱讀）考生之口試錄影帶（謄本）後，填寫「人格印象量表」及「口試評量表」。 本研究以3「甄選情境」× 5「考生」混合設計二因子變異數分析檢定不同的甄選情境在「人格印象」（他人親和取向、個人愉悅取向）、「能力評估」（專業能力、問題處理能力、人際關係處理能力、行政能力、外語能力）、「喜好程度」、「應對表現」及「口試成績」上之差異情形，結果發現處在不同甄選情境中的口試委員在這些變項上（外語能力除外）均可獲得頗為一致的判斷。研究者認為造成此結果的可能原因為：（1）參與推甄口試的考生無論是在課業或是人格特質、能力上均有相當程度的相似性；（2）大學推甄所使用的書面審查資料較職場口試中所使用的豐富，足以提供考生之人格、能力相關訊息；（3）實驗過程中口試委員閱讀書面審查資料的時間和口試時間的間隔太短，考生書面資料造成的初始效應過於強烈。 在不同甄選情境（視聽組、聽覺組、文字組）中，「人格印象」、「能力評估」、「喜好程度」、「應對表現」對「口試成績」之影響方面，研究結果發現：（1）「口試成績」與「應對表現」有高度相關；（2）「個人愉悅取向」與「視聽組」及「文字組」口試成績之間僅具低度相關，但卻與「聽覺組」口試成績具高度相關；（3）「專業能力」與「視聽組」及「文字組」口試成績之間僅具低度相關，但卻與「聽覺組」口試成績之間具高度相關；（4）「問題解決能力」與「視聽組」及「聽覺組」口試成績之間僅具低度相關，但卻與「文字組」口試成績之間具高度相關；（6）「喜好程度」與「視聽組」及「文字組」口試成績僅具低度相關，但與「聽覺組」口試成績則具高度相關；（7）「口試成績」與「他人親和取向」、「人際關係能力」、「行政能力」、「外語能力」僅有低度相關。 研究者認為本研究之所以發現「人格印象」與「視聽組」和「文字組」口試成績之間並沒有很高的相關，其可能的原因如下：（1）有些人格特質是無法在短時間內被覺察到的；（2）有些人格特質是比較容易用聽的方式覺察到的；（3）人格特質的判斷通常是以潛意識的方式在進行的；（4）人格特質是可以經由大學四年的教育慢慢成形的；（5）人格特質對口試的影響主要是在對應試者未來工作表現的預測上，而大學校系並不需預測考生未來工作表現。 綜合本研究發現，在大學推甄口試方面，考生的肢體語言、外表、聲音等對口試委員的影響並不是很重要。此外，人格特質在大學推甄口試上的影響亦非常輕微，因此研究認為大學學系應重新衡量是否應繼續保有口試？抑或改以其他方式進行學生的甄選，以達到既有效又節省的取才方式。 / The purpose of the study is to identify which of the following variables: personality impression, capability judgment, likeability, and interview performance, is actually affecting the outcome of the university enrollment interview with different settings. Subjects were 51 post-graduate students, randomly assigned to three groups – “Audio-visual Group”, “Audio Group”, and “Script Group”. Those in video group watched the video of the university enrollment interview, while those in audio group listened to the audio of the same interview, and those in script group read the transcript of the said interview. Vitae of the applicants were given to the subjects for reference prior to the stimulus. Each subject reviewed 5 applicants’ video (audio/transcript), and filled in the Personality Impression Form and Interview Assessment Form. 2-way ANOVA is used to examine the effect of different settings (audio-visual, audio, or transcript) on personality impression, capability judgment, likeability, and interview content, and it is found that all variable can be judged in coincidence among subjects within different settings. Pearson correlation is used to examine the effect of personality impression, capability judgment, likeability, and interview performance on the decision-making of interview in different settings, and it is found that interview result is (1) highly correlated to interview performance in all settings; (2) highly correlated to personality impression, specialty, and likeability in “audio group”, however, it is loosely correlated in other groups; (3) highly correlated to problem solving skills in “audio group”, however, it is loosely correlated in other groups; (4) loosely correlated to inter-personal skills, administrative skills and foreign language in all settings.