How do MIS researchers handle missing data in survey-based research: A content analysis approach

Karanja, Erastus; Zaveri, Jigish; Ahmed, Ashraf Uddin

doi:10.1016/j.ijinfomgt.2013.05.002

Cited by 30 publications

(19 citation statements)

References 64 publications

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“…Some of these are fatigue (e.g., failing to respond the last questions on a long test), carelessness (e.g., forgetting to complete the items on the backside of a test or survey), item difficulty, unwillingness to answer certain items (e.g., what's your recent TOEFL score? ), unclear items/questions and limited test time (Enders, 2010;Karanja et al, 2013;Schafer & Graham, 2002). Taking a more systematic approach, McKnigt et al (2007) highlighted that there are three potential sources of missing data: "missing cases, missing variables and missing occasions" (p. 17).…”

Section: Missing Datamentioning

confidence: 99%

“…Although this issue is virtually guaranteed in quantitative research, little is still known about why data are missing, how they influence the results and how this problem can be properly handled (McKnight, McKnight, Sidani & Figueredo, 2007). While a number of scholars in different fields such as education (Cheema, 2014;Peugh & Enders, 2004;Rousseau, Simon, Bertrand & Hachey, 2012), counseling psychology (Schlomer, Bauman & Card, 2010) and management information systems (Karanja, Zaveri & Ahmed, 2013) have addressed the problem of missing data, there is a paucity of missing data research in the field of second language acquisition (SLA 1 ). Further, considering the recent scholarly work (e.g., Gonulal, 2016, 2018, Gonulal, Loewen & Plonsky, 2017Loewen et al, 2014) on promoting statistical knowledge and statistical practices in the field, examining this issue in second language (L2) research is a logical and timely step.…”

Section: Introductionmentioning

confidence: 99%

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Missing Data Management Practices in L2 Research: The Good, The Bad and The Ugly

Gönülal

2019

Erzincan Üniversitesi Eğitim Fakültesi Dergisi

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Missing data are one of the frequently encountered problems in quantitative research. When neglected or handled improperly, this problem can have adverse impact on research results. However, the issue of missing data in quantitative second language (L2) research has largely been ignored when compared to the other sister disciplines such as education and psychology. The purpose of this methodological synthesis was, therefore, to investigate the issue of missing data in L2 research, with a particular focus on L2 researchers' current missing data management practices. A total of 143 studies published in six leading L2 journals were reviewed in this synthesis. The results indicated that missing data were indeed quite common in L2 research in that 41% of the studies indicated evidence of missing data, but L2 researchers' management and reporting of missing data was often less than optimal. In light of the results, several directed suggestions were made to improve the rigor and quality of L2 research.

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Section: Missing Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Missing Data Management Practices in L2 Research: The Good, The Bad and The Ugly

Gönülal

2019

Erzincan Üniversitesi Eğitim Fakültesi Dergisi

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“…Therefore, a process of missing value replacement may have invalidated the results. Second, missing value replacement with mean substitution should be used only if 10% or fewer of the components of a given scale are missing (Baraldi & Enders, 2010;Karanja, Zaveri, & Ahmed, 2013). Because all scale scores used in this study included only four or five items, even one missing item would have exceeded the number of permissible items omitted.…”

Section: Methodsmentioning

confidence: 99%

The Relationship between Instructor Servant Leadership Behaviors and Satisfaction With Instructors in an Online Setting

Sahawneh

Benuto

2018

OLJ

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Servant leadership has the potential to improve student satisfaction with online learning. However, the relationship between servant leadership and student satisfaction in an online environment had not yet been understood at the level of the individual instructor. The purpose of this quantitative, correlational study was to evaluate the relationship between online students’ perception of their instructor’s servant leadership style and the student’s satisfaction with the online instructor. We selected 155 online students at a major community college in the south-central United States to complete the Servant Leadership Questionnaire and the Student Evaluation of Teaching survey online. We examined the relationships between each of the five facets of perceived servant leadership style (altruistic calling, emotional healing, wisdom, persuasive mapping, and organizational stewardship) and student satisfaction. The results of the Spearman’s correlations showed a strong positive correlation between all servant leadership behaviors and student satisfaction, p < .001. A multiple linear regression analysis showed that the combination of altruistic calling, persuasive mapping, and wisdom strongly predicted student satisfaction with the instructor, F(3, 151) = 83.8, p < .001, R2= .63. The results of this study have filled a gap in the literature on the relationship between online student satisfaction and individual servant leadership behaviors. We recommend future research to investigate servant leadership in relationship to online learning at 4-year public, for-profit, and private institutions.

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“…When formatting the data, we find some missing data entries. We solve this problem by approximating missing values and discovering a relationship between the known and unknown data [21].…”

Section: ) Incomplete Datamentioning

confidence: 99%

Predicting Ayurveda-Based Constituent Balancing in Human Body Using Machine Learning Methods

Madaan

Goyal

2020

IEEE Access

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Human Body constitution (prakriti) defines what is in harmony with human nature and what will cause to move out of balance and experience illness. Tridosha defines the three basic energies or principles that determine the function of our body on the physical and emotional levels. The three energies are known as VATT, PITT and KAPH. Each individual has a unique balance of all three of these energies. Some people will be predominant in one, while others will be a mixture of two or more. Ayurveda-dosha studies have been used for a long time, but the quantitative reliability measurement of these diagnostic methods still lags behind. A careful and appropriate analysis leads to an effective treatment. To collect a meaningful data set, a questionnaire with 28 different characteristics is validated by Ayurveda experts. Authors calculate Cronbach alpha of VATT-Dosha, PITT-Dosha and KAPH-Dosha as 0.94, 0.98 and 0.98, respectively to check the reliability of the questionnaire. Authors analyzed questionnaires of 807 healthy persons aged 20-60 years and found 62.1% men and 37.9% women. The class imbalance problem is resolved with oversampling and the equally distributed data set of randomly selected 405 persons is used for the actual experiment. Using computer algorithms, we randomly divide the data set (8:2) into a training set of 324 persons and a test data set of 81 persons. Model is trained using traditional machine learning techniques for classification analysis as Artificial Neural Network (ANN), K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Naive Bayes (NB) and Decision Tree (DT). System is also implemented using ensemble of several machine learning methods for constitution recognition. Evaluation measures of classification such as root mean square error (RMSE), precision, recall, F-score, and accuracy is calculated and analyzed. On analyzing the results authors find that the data is best trained and tested with CatBoost, which is tuned with hyper parameters and achieves 0.96 precision, 0.95 recall, 0.95 F-score and 0.95 accuracy rate. The experimental result shows that the proposed model based on ensemble learning methods clearly surpasses conventional methods. The results conclude that advances in boosting algorithms could give machine learning a leading future. INDEX TERMS Ayurveda, human body constituents, hyper parameter tuning, KAPH, optimized training model, PITT, VATT.

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How do MIS researchers handle missing data in survey-based research: A content analysis approach

Cited by 30 publications

References 64 publications

Missing Data Management Practices in L2 Research: The Good, The Bad and The Ugly

Missing Data Management Practices in L2 Research: The Good, The Bad and The Ugly

The Relationship between Instructor Servant Leadership Behaviors and Satisfaction With Instructors in an Online Setting

Predicting Ayurveda-Based Constituent Balancing in Human Body Using Machine Learning Methods

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