Identifying Careless Responses in Survey Data

Meade, Adam W.; Craig, S. Bartholomew

doi:10.1037/e518362013-127

Cited by 270 publications

(497 citation statements)

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“…Ciri-ciri fungsi dalam kesesuaian item yang diterangkan ini turut dapat diaplikasikan bagi mengenal pasti responden yang cuai semasa menjawab soalan soal selidik. Ini bagi meningkatkan kualiti hasil analisis data penyelidikan sesebuah kajian bagi mendapatkan analisis yang lebih tepat (Meade & Craig 2012 Bagi mengesan responden yang tidak sepadan, analisis kesesuaian responden diperoleh dan disemak melalui jadual kesesuaian responden dalam Jadual 3. Pemeriksaan terhadap nilai min kuasa dua, skor-z dan korelasi dilakukan seperti yang telah dilaksanakan dalam beberapa kajian sebelum ini.…”

Section: Kaedah Analisis Model Pengukuran Raschunclassified

Reliability & Validity of the Construction Delay Questionaire using the Rasch Mesurement Model

Khoiry¹,

Hamzah²,

Badaruzzaman³

et al. 2017

JKUKM

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For purposes of data analysis, this research uses Winstep a software-based Rasch Modal measurement. Beside Cronbach alpha reliability, Rasch Measurement Model can provide the reliability of the item and respondent. Validity of data evaluated from various aspects, including the items and the respondent misfit, principal component analysis and rating scale. A total of 54 of the 64 items were identified contributed to delays in construction projects in Malaysia.Keywords: Validity; Reliability, Construction delay; Rasch analysis; Rating scale PENGENALAN Fenomena kelewatan dalam projek pembinaan menjadi satu perkara biasa dalam industri pembinaan. Di Malaysia kelewatan dalam projek pembinaan tidak asing lagi di kalangan pengamal industri pembinaan terutamanya projek pembinaan awam. Kajian mengenai kelewatan dalam projek pembinaan merupakan sebuah kajian yang telah lama dilakukan telah dilakukan sejak tahun 1985 lagi di Turki, dan berkembang sehingga kini ke negara-negara lain (Sepasgozar, Razkenari & Barati 2015). Walaupun pelbagai kajian kelewatan dilakukan oleh penyelidik daripada seluruh dunia, fenomena ini masih tidak dapat diselesaikan seperti ia tidak berpenghujung.Pelbagai kajian kelewatan dalam projek pembinaan telah dilakukan oleh penyelidik lepas di seluruh dunia, antaranya ialah Kaliba et al. (2009) telah melakukan kajian kelewatan di Republik Zambia, sebuah negara membangun. Kajian tersebut mengenal pasti punca dan kesan peningkatan kos dan kelewatan dalam penjadualan bagi projek pembinaan jalan raya di negara tersebut. Penyelidik tersebut telah mendapati proses pembayaran yang lambat, masalah kewangan dan kesukaran pada pihak kontraktor dan klien, pengubahsuaian kontrak, masalah kewangan, masalah perolehan bahan, perubahan dalam lukisan, masalah kakitangan, ketiadaan peralatan, penyeliaan yang lemah, kesilapan pembinaan, penyelarasan yang lemah di tapak, perubahan dalam spesifikasi bahan dan pertikaian dan masalah buruh telah menjadi punca utama kelewatan penyiapan projek pembinaan jalan raya di negara tersebut.

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Section: Kaedah Analisis Model Pengukuran Raschunclassified

Reliability & Validity of the Construction Delay Questionaire using the Rasch Mesurement Model

Khoiry¹,

Hamzah²,

Badaruzzaman³

et al. 2017

JKUKM

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“…The group of respondents can be verified and collected data are stored into the database. In data quality research, attention is also paid to the lower involvement of the respondent, in the case of a web survey [15]. The problem with the respondent's involvement is also due to the volume of survey.…”

Section: The Analysis Of the Present Situationmentioning

confidence: 99%

Survey as a source of low quality research data

Szyjewski¹,

Fabisiak²

2017

Annals of Computer Science and Information Systems

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Abstract-Survey is the most common way to gather information for research purposes. Gathering information is usually provided in the early stage of research procedure. The information create the basis for further activities which lead to scientific research results. The problem is that information gathered as a result of survey is exposed to the high risk of errors. Following factors generates mostly errors: the way of survey carrying out, the survey content and just by simple human factors. Researchers very rarely pay their attention to the data quality taking as an obvious that gathered information describes properly studied case. In the end of the process they get results, which are becoming basis for the conclusions. Taking that into account the questions is about the quality, of research results, which were based on the surveys. What is important, usually most publications do not contain any information about source data on the basis of which results came out. It disables the independent evaluation of the results. The goal was to investigate the scale of survey data errors, using the scientific experiment. To achieve it, authors used their own survey system, which helped them in instant verification of the respondents' answers.

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“…Finally, we had a reduced sample size for the PLC analyses due to both the delayed administration of the survey and the participants removed from the analysis for incorrect responses to the attention item. However, it is better to have the smaller sample size with greater confidence in the quality of the data (Meade & Craig, 2012).…”

Section: Limitationsmentioning

confidence: 99%

“…The manager sent the survey to all 518 trainees, and 132 responded for an initial response rate of 25.5 percent. We included one attention check item in the survey to detect careless responses (Meade & Craig, 2012). We removed respondents who answered this item incorrectly from further analyses using the PLC measure.…”

Section: Research Setting and Proceduresmentioning

confidence: 99%

How Do Learners Interact with E-learning? Examining Patterns of Learner Control Behaviors

Fisher¹,

Howardson²,

Wasserman³

et al. 2017

THCI

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Abstract:There has been significant debate in the literature on technology-mediated training about the appropriate role of learner control. We define learner control as giving trainees the ability to make choices about how they proceed through the learning environment. We explore two perspectives. First, we consider learners' stated preferences for the extent of control in the learning environment. Second, we analyze the actual online learning behaviors of 518 trainees in a Fortune 500 organization. We compare a measure of learner control preferences to the most commonly used framework of learner control that comprises five dimensions: pace of instruction, sequence of topics, specific content covered, amount of advice/feedback provided, and type of media. We also compare the dimensionality of learner behaviors to this framework and examine the relationship between learner preferences and learner behaviors. Results suggest that fewer dimensions can capture both learner preferences and behaviors than what the literature currently suggests. Specifically, media control aligned with both pace and content control. The relationship between stated learner control preferences and learner control behaviors was relatively weak. However, we found support for the recently identified dimension of scheduling control and suggest a new learner control dimension of performance control, consistent with the importance of practice retrieval for learning.

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Identifying Careless Responses in Survey Data

Cited by 270 publications

References 0 publications

Reliability & Validity of the Construction Delay Questionaire using the Rasch Mesurement Model

Reliability & Validity of the Construction Delay Questionaire using the Rasch Mesurement Model

Survey as a source of low quality research data

How Do Learners Interact with E-learning? Examining Patterns of Learner Control Behaviors

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