scholarly journals A Multiprocess Item Response Model for Not-Reached Items due to Time Limits and Quitting

2019 ◽  
Vol 80 (3) ◽  
pp. 522-547
Author(s):  
Esther Ulitzsch ◽  
Matthias von Davier ◽  
Steffi Pohl
Keyword(s):  
Missing Data ◽  
Test Performance ◽  
Missing Values ◽  
Response Times ◽  
Computerized Testing ◽  
Item Response Model ◽  
Time Limits ◽  
Fine Grained ◽  
Test Taking ◽  
The One

So far, modeling approaches for not-reached items have considered one single underlying process. However, missing values at the end of a test can occur for a variety of reasons. On the one hand, examinees may not reach the end of a test due to time limits and lack of working speed. On the other hand, examinees may not attempt all items and quit responding due to, for example, fatigue or lack of motivation. We use response times retrieved from computerized testing to distinguish missing data due to lack of speed from missingness due to quitting. On the basis of this information, we present a new model that allows to disentangle and simultaneously model different missing data mechanisms underlying not-reached items. The model (a) supports a more fine-grained understanding of the processes underlying not-reached items and (b) allows to disentangle different sources describing test performance. In a simulation study, we evaluate estimation of the proposed model. In an empirical study, we show what insights can be gained regarding test-taking behavior using this model.

A Psychometric Examination of the Anagram Persistence Task: More Than Two Unsolvable Anagrams May Not Be Better

Assessment ◽  
2018 ◽  
Vol 27 (6) ◽  
pp. 1198-1212 ◽  
Author(s):  
Gilles E. Gignac ◽  
Ka Ki Wong
Keyword(s):  
Processing Speed ◽  
Test Performance ◽  
Convergent Validity ◽  
Response Times ◽  
Nonlinear Effects ◽  
Intelligence Test ◽  
Factorial Validity ◽  
Test Taking ◽  
Two Factors ◽  
Administration Time

The purpose of this investigation was to examine a single-anagram, a double-anagram, and multi-anagram versions of the Anagram Persistence Task (APT) for factorial validity, reliability, and convergent validity. Additionally, a battery of intelligence tests was administered to examine convergent validity. Based on an unrestricted factor analysis, two factors were uncovered from the 14 anagram (seven very difficult and seven very easy) response times: test-taking persistence and verbal processing speed. The internal consistency reliabilities for the single-anagram, double-anagram, and multi-anagram (seven difficult anagrams) measures were .42, .85, and .86, respectively. Furthermore, all three versions of the APT correlated positively with intelligence test performance ( r ≈ .22). However, the double-anagram and multi-anagram versions also evidenced negative, nonlinear effects with intelligence test performance ( r ≈ −.15), which suggested the possibility of testee adaptation. Taking psychometrics and administration time into consideration, simultaneously, the double-anagram version of the APT may be regarded as preferred.

scholarly journals A Spatial Missing Value Imputation Method for Multi-view Urban Statistical Data

2020 ◽  
Author(s):  
Yongshun Gong ◽  
Zhibin Li ◽  
Jian Zhang ◽  
Wei Liu ◽  
Bei Chen ◽  
...  
Keyword(s):  
Missing Data ◽  
Missing Values ◽  
Statistical Data ◽  
Regional Analysis ◽  
Imputation Method ◽  
Urban Computing ◽  
Missing Data Imputation ◽  
Fine Grained ◽  
Kernel Clustering ◽  
Adaptive Weight

Large volumes of urban statistical data with multiple views imply rich knowledge about the development degree of cities. These data present crucial statistics which play an irreplaceable role in the regional analysis and urban computing. In reality, however, the statistical data divided into fine-grained regions usually suffer from missing data problems. Those missing values hide the useful information that may result in a distorted data analysis. Thus, in this paper, we propose a spatial missing data imputation method for multi-view urban statistical data. To address this problem, we exploit an improved spatial multi-kernel clustering method to guide the imputation process cooperating with an adaptive-weight non-negative matrix factorization strategy. Intensive experiments are conducted with other state-of-the-art approaches on six real-world urban statistical datasets. The results not only show the superiority of our method against other comparative methods on different datasets, but also represent a strong generalizability of our model.

Missing Data - Better "Not to Have Them", but What If You Do? (Part 1)

Marketing ZFP ◽  
2019 ◽  
Vol 41 (4) ◽  
pp. 21-32
Author(s):  
Dirk Temme ◽  
Sarah Jensen
Keyword(s):  
Missing Data ◽  
Statistical Power ◽  
Missing Values ◽  
Graphical Representation ◽  
Marketing Research ◽  
Likelihood Estimation ◽  
Parameter Estimates ◽  
Full Information Maximum Likelihood ◽  
Definition Of ◽  
Traditional Approaches

Missing values are ubiquitous in empirical marketing research. If missing data are not dealt with properly, this can lead to a loss of statistical power and distorted parameter estimates. While traditional approaches for handling missing data (e.g., listwise deletion) are still widely used, researchers can nowadays choose among various advanced techniques such as multiple imputation analysis or full-information maximum likelihood estimation. Due to the available software, using these modern missing data methods does not pose a major obstacle. Still, their application requires a sound understanding of the prerequisites and limitations of these methods as well as a deeper understanding of the processes that have led to missing values in an empirical study. This article is Part 1 and first introduces Rubin’s classical definition of missing data mechanisms and an alternative, variable-based taxonomy, which provides a graphical representation. Secondly, a selection of visualization tools available in different R packages for the description and exploration of missing data structures is presented.

scholarly journals Comparison of Four Commercially Available Point-of-Care Tests to Detect Antibodies against Canine Parvovirus in Dogs

Viruses ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 18
Author(s):  
Michèle Bergmann ◽  
Mike Holzheu ◽  
Yury Zablotski ◽  
Stephanie Speck ◽  
Uwe Truyen ◽  
...  
Keyword(s):  
Test Performance ◽  
Point Of Care ◽  
Reference Method ◽  
Canine Parvovirus ◽  
P Value ◽  
Important Indicator ◽  
Specificity And Sensitivity ◽  
Specific Pathogen ◽  
The One ◽  
Point Of Care Tests

Measuring antibodies to evaluate dogs´ immunity against canine parvovirus (CPV) is useful to avoid unnecessary re-vaccinations. The study aimed to evaluate the quality and practicability of four point-of-care (POC) tests for detection of anti-CPV antibodies. The sera of 198 client-owned and 43 specific pathogen-free (SPF) dogs were included; virus neutralization was the reference method. Specificity, sensitivity, positive and negative predictive value (PPV and NPV), and overall accuracy (OA) were calculated. Specificity was considered to be the most important indicator for POC test performance. Differences between specificity and sensitivity of POC tests in the sera of all dogs were determined by McNemar, agreement by Cohen´s kappa. Prevalence of anti-CPV antibodies in all dogs was 80% (192/241); in the subgroup of client-owned dogs, it was 97% (192/198); and in the subgroup of SPF dogs, it was 0% (0/43). FASTest® and CanTiCheck® were easiest to perform. Specificity was highest in the CanTiCheck® (overall dogs, 98%; client-owned dogs, 83%; SPF dogs, 100%) and the TiterCHEK® (overall dogs, 96%; client-owned dogs, 67%; SPF dogs, 100%); no significant differences in specificity were observed between the ImmunoComb®, the TiterCHEK®, and the CanTiCheck®. Sensitivity was highest in the FASTest® (overall dogs, 95%; client-owned dogs, 95%) and the CanTiCheck® (overall dogs, 80%; client-owned dogs, 80%); sensitivity of the FASTest® was significantly higher compared to the one of the other three tests (McNemars p-value in each comparison: <0.001). CanTiCheck® would be the POC test of choice when considering specificity and practicability. However, differences in the number of false positive results between CanTiCheck®, TiterCHEK®, and ImmunoComb® were minimal.

scholarly journals Dynamic model updating (DMU) approach for statistical learning model building with missing data

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Rahi Jain ◽  
Wei Xu
Keyword(s):  
Missing Data ◽  
Dynamic Model ◽  
Statistical Models ◽  
Missing Values ◽  
Model Building ◽  
Model Updating ◽  
Biological Data ◽  
Bayesian Regression ◽  
Biological Research ◽  
Original Dataset

Abstract Background Developing statistical and machine learning methods on studies with missing information is a ubiquitous challenge in real-world biological research. The strategy in literature relies on either removing the samples with missing values like complete case analysis (CCA) or imputing the information in the samples with missing values like predictive mean matching (PMM) such as MICE. Some limitations of these strategies are information loss and closeness of the imputed values with the missing values. Further, in scenarios with piecemeal medical data, these strategies have to wait to complete the data collection process to provide a complete dataset for statistical models. Method and results This study proposes a dynamic model updating (DMU) approach, a different strategy to develop statistical models with missing data. DMU uses only the information available in the dataset to prepare the statistical models. DMU segments the original dataset into small complete datasets. The study uses hierarchical clustering to segment the original dataset into small complete datasets followed by Bayesian regression on each of the small complete datasets. Predictor estimates are updated using the posterior estimates from each dataset. The performance of DMU is evaluated by using both simulated data and real studies and show better results or at par with other approaches like CCA and PMM. Conclusion DMU approach provides an alternative to the existing approaches of information elimination and imputation in processing the datasets with missing values. While the study applied the approach for continuous cross-sectional data, the approach can be applied to longitudinal, categorical and time-to-event biological data.

scholarly journals Handling Complex Missing Data Using Random Forest Approach for an Air Quality Monitoring Dataset: A Case Study of Kuwait Environmental Data (2012 to 2018)

2021 ◽  
Vol 18 (3) ◽  
pp. 1333
Author(s):  
Ahmad R. Alsaber ◽  
Jiazhu Pan ◽  
Adeeba Al-Hurban 
Keyword(s):  
Air Quality ◽  
Missing Data ◽  
Random Forest ◽  
Missing Values ◽  
Imputation Method ◽  
Environmental Data ◽  
Environmental Research ◽  
Quality Data ◽  
Data Set ◽  
Air Quality Data

In environmental research, missing data are often a challenge for statistical modeling. This paper addressed some advanced techniques to deal with missing values in a data set measuring air quality using a multiple imputation (MI) approach. MCAR, MAR, and NMAR missing data techniques are applied to the data set. Five missing data levels are considered: 5%, 10%, 20%, 30%, and 40%. The imputation method used in this paper is an iterative imputation method, missForest, which is related to the random forest approach. Air quality data sets were gathered from five monitoring stations in Kuwait, aggregated to a daily basis. Logarithm transformation was carried out for all pollutant data, in order to normalize their distributions and to minimize skewness. We found high levels of missing values for NO2 (18.4%), CO (18.5%), PM10 (57.4%), SO2 (19.0%), and O3 (18.2%) data. Climatological data (i.e., air temperature, relative humidity, wind direction, and wind speed) were used as control variables for better estimation. The results show that the MAR technique had the lowest RMSE and MAE. We conclude that MI using the missForest approach has a high level of accuracy in estimating missing values. MissForest had the lowest imputation error (RMSE and MAE) among the other imputation methods and, thus, can be considered to be appropriate for analyzing air quality data.

Halstead-Reitan Test Battery: A Problem of Differential Diagnosis

1980 ◽  
Vol 50 (2) ◽  
pp. 611-630
Author(s):  
Irmingard I. Lenzer
Keyword(s):  
Brain Damage ◽  
Test Performance ◽  
Construct Validation ◽  
Neuropsychological Test ◽  
Summary Score ◽  
Test Battery ◽  
Medical Patients ◽  
Test Taking ◽  
Limited Applicability ◽  
Schizophrenic Patients

The Halstead-Reitan Test Battery is one of the most widely recognized neuropsychological test batteries. Many claims have been made as to its validity. Despite these claims, doubts persist. A critical review of the literature shows that the battery can separate brain-damaged patients from normal patients, general medical patients, and patients with certain psychiatric disorders. However, the battery cannot separate brain-damaged patients as a group from schizophrenics as a group, though in individual cases there may exist pathognomonic signs indicating brain damage. The impairment index, as a summary score of the basic tests, as well as other “methods of inference,” fail at this point. Four alternatives are discussed. First, brain-damaged patients differ from schizophrenic patients not in test performance but in test-taking behavior. Second, the battery is a valid measure of brain damage but has limited applicability. Third, the battery is a measure not of brain damage but of degree of degradation of psychological processes. And fourth, schizophrenics perform poorly on the battery because they have undetected brain damage. Only the third and fourth alternatives appear viable. Both question the validity of the traditional criteria of brain damage. It is argued that future validation studies of the battery should be of construct validation type and not of the criterion-oriented type, as these are defined by Cronbach and Meehl (1955). Possible procedures for construct validation are briefly discussed.

scholarly journals Bootstrap joint prediction regions for sequences of missing values in spatio-temporal datasets

2021 ◽  
Author(s):  
Maria Lucia Parrella ◽  
Giuseppina Albano ◽  
Cira Perna ◽  
Michele La Rocca
Keyword(s):  
Missing Data ◽  
Missing Values ◽  
Sample Selection ◽  
Sample Path ◽  
Temporal Relationships ◽  
Empirical Performance ◽  
Spatio Temporal ◽  
Joint Prediction ◽  
Point Forecast ◽  
Prediction Regions

AbstractMissing data reconstruction is a critical step in the analysis and mining of spatio-temporal data. However, few studies comprehensively consider missing data patterns, sample selection and spatio-temporal relationships. To take into account the uncertainty in the point forecast, some prediction intervals may be of interest. In particular, for (possibly long) missing sequences of consecutive time points, joint prediction regions are desirable. In this paper we propose a bootstrap resampling scheme to construct joint prediction regions that approximately contain missing paths of a time components in a spatio-temporal framework, with global probability $$1-\alpha $$ 1 - α . In many applications, considering the coverage of the whole missing sample-path might appear too restrictive. To perceive more informative inference, we also derive smaller joint prediction regions that only contain all elements of missing paths up to a small number k of them with probability $$1-\alpha $$ 1 - α . A simulation experiment is performed to validate the empirical performance of the proposed joint bootstrap prediction and to compare it with some alternative procedures based on a simple nominal coverage correction, loosely inspired by the Bonferroni approach, which are expected to work well standard scenarios.

scholarly journals Mineralogy, Geochemistry and Provenance of Coastal Sands from Greece: New Insights on the REE Content of Black Coastal Sands from Aggelochori Area, N.-Greece

Minerals ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 693
Author(s):  
Argyrios Papadopoulos ◽  
Stylianos Lazaridis ◽  
Afroditi Kipourou-Panagiotou ◽  
Nikolaos Kantiranis ◽  
Antonios Koroneos ◽  
...  
Keyword(s):  
Major And Trace Elements ◽  
Heavy Minerals ◽  
Sea Waves ◽  
Complex Flow ◽  
Fine Grained ◽  
Microscopy Analysis ◽  
Beach Sands ◽  
Geochemical Analyses ◽  
The One ◽  
Black Color

Beach sands from Aggelochori coast line are investigated for their geochemistry and REE content, mineralogy and their provenance. These fluvial sands bear heavy minerals enriched horizons (containing minerals such as magnetite, zircon, ilmenite, hematite, rutile and titanite) that can be distinguished due to their black color and are formed usually due to the action of sea waves that deposit the heavy minerals and remove the lighter ones. After a suitable processing (washing, sieving, drying and magnetic separation) of the samples, the mineral constituents and their presence (wt.%) were estimated by XRD. Among the samples, the one being simultaneously the more fine grained and the more zircon-enriched (as suggested by XRPD data and optical microscopy analysis) has been selected for further geochemical analyses. The major and trace elements contents were compared to previously studied REE enriched beach sands from Kavala and Sithonia. Beach sands from Aggelochori area appear to have relatively low REE contents. Considering the provenance of these sediments, we suggest that these sands, are a product of the erosion of multi-sources, including the near-by Monopigado granite, as well as metamorphic rocks, as indicated by the presence of rutile and both ilmenite and magnetite in some samples. Therefore, there are indications of a complex flow pattern that existed at the paleo-catchment area of the deposition.

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