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Book part
Publication date: 23 November 2011

Gayaneh Kyureghian, Oral Capps and Rodolfo M. Nayga

The objective of this research is to examine, validate, and recommend techniques for handling the problem of missingness in observational data. We use a rich observational data…

Abstract

The objective of this research is to examine, validate, and recommend techniques for handling the problem of missingness in observational data. We use a rich observational data set, the Nielsen HomeScan data set, which allows us to effectively combine elements from simulated data sets: large numbers of observations, large number of data sets and variables, allowing elements of “design” that typically come with simulated data, and its observational nature. We created random 20% and 50% uniform missingness in our data sets and employed several widely used methods of single imputation, such as mean, regression, and stochastic regression imputations, and multiple imputation methods to fill in the data gaps. We compared these methods by measuring the error of predicting the missing values and the parameter estimates from the subsequent regression analysis using the imputed values. We also compared coverage or the percentages of intervals that covered the true parameter in both cases. Based on our results, the method of single regression or conditional mean imputation provided the best predictions of the missing price values with 28.34 and 28.59 mean absolute percent errors in 20% and 50% missingness settings, respectively. The imputation from conditional distribution method had the best rate of coverage. The parameter estimates based on data sets imputed by conditional mean method were consistently unbiased and had the smallest standard deviations. The multiple imputation methods had the best coverage of both the parameter estimates and predictions of the dependent variable.

Details

Missing Data Methods: Cross-sectional Methods and Applications
Type: Book
ISBN: 978-1-78052-525-9

Keywords

Article
Publication date: 2 April 2021

Tressy Thomas and Enayat Rajabi

The primary aim of this study is to review the studies from different dimensions including type of methods, experimentation setup and evaluation metrics used in the novel…

1340

Abstract

Purpose

The primary aim of this study is to review the studies from different dimensions including type of methods, experimentation setup and evaluation metrics used in the novel approaches proposed for data imputation, particularly in the machine learning (ML) area. This ultimately provides an understanding about how well the proposed framework is evaluated and what type and ratio of missingness are addressed in the proposals. The review questions in this study are (1) what are the ML-based imputation methods studied and proposed during 2010–2020? (2) How the experimentation setup, characteristics of data sets and missingness are employed in these studies? (3) What metrics were used for the evaluation of imputation method?

Design/methodology/approach

The review process went through the standard identification, screening and selection process. The initial search on electronic databases for missing value imputation (MVI) based on ML algorithms returned a large number of papers totaling at 2,883. Most of the papers at this stage were not exactly an MVI technique relevant to this study. The literature reviews are first scanned in the title for relevancy, and 306 literature reviews were identified as appropriate. Upon reviewing the abstract text, 151 literature reviews that are not eligible for this study are dropped. This resulted in 155 research papers suitable for full-text review. From this, 117 papers are used in assessment of the review questions.

Findings

This study shows that clustering- and instance-based algorithms are the most proposed MVI methods. Percentage of correct prediction (PCP) and root mean square error (RMSE) are most used evaluation metrics in these studies. For experimentation, majority of the studies sourced the data sets from publicly available data set repositories. A common approach is that the complete data set is set as baseline to evaluate the effectiveness of imputation on the test data sets with artificially induced missingness. The data set size and missingness ratio varied across the experimentations, while missing datatype and mechanism are pertaining to the capability of imputation. Computational expense is a concern, and experimentation using large data sets appears to be a challenge.

Originality/value

It is understood from the review that there is no single universal solution to missing data problem. Variants of ML approaches work well with the missingness based on the characteristics of the data set. Most of the methods reviewed lack generalization with regard to applicability. Another concern related to applicability is the complexity of the formulation and implementation of the algorithm. Imputations based on k-nearest neighbors (kNN) and clustering algorithms which are simple and easy to implement make it popular across various domains.

Details

Data Technologies and Applications, vol. 55 no. 4
Type: Research Article
ISSN: 2514-9288

Keywords

Article
Publication date: 14 August 2017

Panagiotis Loukopoulos, George Zolkiewski, Ian Bennett, Pericles Pilidis, Fang Duan and David Mba

Centrifugal compressors are integral components in oil industry, thus effective maintenance is required. Condition-based maintenance and prognostics and health management…

353

Abstract

Purpose

Centrifugal compressors are integral components in oil industry, thus effective maintenance is required. Condition-based maintenance and prognostics and health management (CBM/PHM) have been gaining popularity. CBM/PHM can also be performed remotely leading to e-maintenance. Its success depends on the quality of the data used for analysis and decision making. A major issue associated with it is the missing data. Their presence may compromise the information within a set, causing bias or misleading results. Addressing this matter is crucial. The purpose of this paper is to review and compare the most widely used imputation techniques in a case study using condition monitoring measurements from an operational industrial centrifugal compressor.

Design/methodology/approach

Brief overview and comparison of most widely used imputation techniques using a complete set with artificial missing values. They were tested regarding the effects of the amount, the location within the set and the variable containing the missing values.

Findings

Univariate and multivariate imputation techniques were compared, with the latter offering the smallest error levels. They seemed unaffected by the amount or location of the missing data although they were affected by the variable containing them.

Research limitations/implications

During the analysis, it was assumed that at any time only one variable contained missing data. Further research is still required to address this point.

Originality/value

This study can serve as a guide for selecting the appropriate imputation method for missing values in centrifugal compressor condition monitoring data.

Details

Journal of Quality in Maintenance Engineering, vol. 23 no. 3
Type: Research Article
ISSN: 1355-2511

Keywords

Article
Publication date: 9 May 2016

Sanna Sintonen, Anssi Tarkiainen, John W. Cadogan, Olli Kuivalainen, Nick Lee and Sanna Sundqvist

The purpose of this paper is to focus on the case where – by design – one needs to impute cross-country cross-survey (CCCS) data (situation typical for example among multinational…

1463

Abstract

Purpose

The purpose of this paper is to focus on the case where – by design – one needs to impute cross-country cross-survey (CCCS) data (situation typical for example among multinational firms who are confronted with the need to carry out comparative marketing surveys with respondents located in several countries). Importantly, while some work demonstrates approaches for single-item direct measures, no prior research has examined the common situation in international marketing where the researcher needs to use multi-item scales of latent constructs. The paper presents problem areas related to the choices international marketers have to make when doing cross-country/cross-survey research and provides guidance for future research.

Design/methodology/approach

Multi-country sample of real data is used as an example of cross-sample imputation (292 New Zealand exporters and 302 Finnish ones) the international entrepreneurial orientation (IEO) data. Three variations of the input data are tested: first, imputation based on all the data available for the measurement model; second, imputation based on the set of items based on the invariance structure of the joint items shared across the two groups; and third, imputation based both on examination of the invariance structures of the joint items and the performance of the measurement model in the group where the full data was originally available.

Findings

Based on distribution comparisons imputation for New Zealand after completing the measurement model with Finnish data (Model C) gave the most promising results. Consequently, using knowledge on between country measurement qualities may improve the imputation results, but this benefit comes with a downside since it simultaneously reduces the amount of data used for imputation. None of the imputation models leads to the same statistical inferences about covariances between latent constructs than as the original full data, however.

Research limitations/implications

Considering multiple imputation, the present exploratory study suggests that there are several concerns and issues that should be taken into account when planning CCCSs (or split questionnaire or sub-sampling designs). Even if there are several advantages available for well-implemented CCCS designs such as shorter questionnaires and improved response rates, these concerns lead us to question the appropriateness of the CCCS approach in general, due to the need to impute across the samples.

Originality/value

The combination of cross-country and cross-survey approaches is novel to international marketing, and it is not known how the different procedures utilized in imputation affect the results and their validity and reliability. The authors demonstrate the consequences of the various imputation strategy choices taken by using a real example of a two-country sample. The exploration may have significant implications to international marketing researchers and the paper offers stimulus for further research in the area.

Details

International Marketing Review, vol. 33 no. 3
Type: Research Article
ISSN: 0265-1335

Keywords

Book part
Publication date: 8 December 2023

Cassie Mead

Past research has established a relationship between the perceptions of fairness in the division of household labor and relationship satisfaction. Varying according to gender and…

Abstract

Past research has established a relationship between the perceptions of fairness in the division of household labor and relationship satisfaction. Varying according to gender and time, this relationship has been found with differing outcomes, including relationship satisfaction, relationship happiness, divorce, and sexual frequency. Although this relationship has been well studied, little research has focused on how this relationship is moderated by relationship status. According to the Second Demographic Transition Theory (SDT), as societies become more “modern,” cohabitation will become more prevalent, eventually becoming socially and culturally equivalent to marriage. As such, it is vital to ask how cohabitation and marriage differ, or if they differ at all. Therefore, this gap is explored by asking, “How do perceptions of the division of household labor affect married and cohabitating heterosexual couples’ relationship happiness and chance of separation?” In order to answer this question, the National Survey of Families and Households (Wave III) is analyzed, with outcomes focusing on relationship happiness and chance of separation. Results indicate that when married and cohabitating individuals experience similar levels of happiness with their partner’s housework, they also experience similar levels of relationship happiness and chance of separation, with relationship status not affecting the impact happiness with partner’s housework has on these relationship outcomes. This suggests that cohabitation and marriage may continue to become more similar overall.

Details

Cohabitation and the Evolving Nature of Intimate and Family Relationships
Type: Book
ISBN: 978-1-80455-418-0

Keywords

Book part
Publication date: 10 July 2006

Craig Enders, Samantha Dietz, Marjorie Montague and Jennifer Dixon

Missing data are a pervasive problem in special education research. The purpose of this chapter is to provide researchers with an overview of two “modern” alternatives for…

Abstract

Missing data are a pervasive problem in special education research. The purpose of this chapter is to provide researchers with an overview of two “modern” alternatives for handling missing data, full information maximum likelihood (FIML) and multiple imputation (MI). These techniques are currently considered to be the methodological “state of the art”, and generally provide more accurate parameter estimates than the traditional methods that are still common in published educational studies. The chapter begins with an overview of missing data theory, and provides brief descriptions of some traditional missing data techniques and their requisite assumptions. Detailed descriptions of FIML and MI are given, and the chapter concludes with an analytic example from a longitudinal study of depression.

Details

Applications of Research Methodology
Type: Book
ISBN: 978-0-76231-295-5

Article
Publication date: 5 May 2015

Jeremy N.V Miles and Priscillia Hunt

In applied psychology research settings, such as criminal psychology, missing data are to be expected. Missing data can cause problems with both biased estimates and lack of…

Abstract

Purpose

In applied psychology research settings, such as criminal psychology, missing data are to be expected. Missing data can cause problems with both biased estimates and lack of statistical power. The paper aims to discuss these issues.

Design/methodology/approach

Recently, sophisticated methods for appropriately dealing with missing data, so as to minimize bias and to maximize power have been developed. In this paper the authors use an artificial data set to demonstrate the problems that can arise with missing data, and make naïve attempts to handle data sets where some data are missing.

Findings

With the artificial data set, and a data set comprising of the results of a survey investigating prices paid for recreational and medical marijuana, the authors demonstrate the use of multiple imputation and maximum likelihood estimation for obtaining appropriate estimates and standard errors when data are missing.

Originality/value

Missing data are ubiquitous in applied research. This paper demonstrates that techniques for handling missing data are accessible and should be employed by researchers.

Details

Journal of Criminal Psychology, vol. 5 no. 2
Type: Research Article
ISSN: 2009-3829

Keywords

Book part
Publication date: 30 November 2011

Wensheng Kang

A linear interpolation (Lerp) approach, utilizing a common stochastic trend, is explored to impute missing values in nonstationary panel data models. The Lerp algorithm is…

Abstract

A linear interpolation (Lerp) approach, utilizing a common stochastic trend, is explored to impute missing values in nonstationary panel data models. The Lerp algorithm is considerably faster and easier to use than the leading methods recommended in the statistics literature. It shows through a set of simulations that the Lerp works well, whereas other existing methods fail to perform properly, when the panel data contain a high degree of missingness and/or a strong correlation across cross-sectional units. As an illustration, the method is applied to study the cost-of-living-index dataset with missing values. The test on the imputed panel data provides the supporting evidence for the U.S. economy convergence that depends on the state physical spatial proximities and the state industrial development similarities.

Details

Missing Data Methods: Time-Series Methods and Applications
Type: Book
ISBN: 978-1-78052-526-6

Keywords

Article
Publication date: 24 August 2018

Jewoo Kim and Jongho Im

The purpose of this paper is to introduce a new multiple imputation method that can effectively manage missing values in online review data, thereby allowing the online review…

Abstract

Purpose

The purpose of this paper is to introduce a new multiple imputation method that can effectively manage missing values in online review data, thereby allowing the online review analysis to yield valid results by using all available data.

Design/methodology/approach

This study develops a missing data method based on the multivariate imputation chained equation to generate imputed values for online reviews. Sentiment analysis is used to incorporate customers’ textual opinions as the auxiliary information in the imputation procedures. To check the validity of the proposed imputation method, the authors apply this method to missing values of sub-ratings on hotel attributes in both the simulated and real Honolulu hotel review data sets. The estimation results are compared to those of different missing data techniques, namely, listwise deletion and conventional multiple imputation which does not consider text reviews.

Findings

The findings from the simulation analysis show that the imputation method of the authors produces more efficient and less biased estimates compared to the other two missing data techniques when text reviews are possibly associated with the rating scores and response mechanism. When applying the imputation method to the real hotel review data, the findings show that the text sentiment-based propensity score can effectively explain the missingness of sub-ratings on hotel attributes, and the imputation method considering those propensity scores has better estimation results than the other techniques as in the simulation analysis.

Originality/value

This study extends multiple imputation to online data considering its spontaneous and unstructured nature. This new method helps make the fuller use of the observed online data while avoiding potential missing problems.

Details

International Journal of Contemporary Hospitality Management, vol. 30 no. 11
Type: Research Article
ISSN: 0959-6119

Keywords

Article
Publication date: 6 May 2020

Jon Bokrantz, Anders Skoogh, Cecilia Berlin and Johan Stahre

Scholars and practitioners within industrial maintenance management are focused on understanding antecedents, correlates and consequences of the concept of “Smart Maintenance,”…

Abstract

Purpose

Scholars and practitioners within industrial maintenance management are focused on understanding antecedents, correlates and consequences of the concept of “Smart Maintenance,” which consists of the four dimensions, namely, data-driven decision-making, human capital resource, internal integration and external integration. In order to facilitate this understanding, valid and reliable empirical measures need to be developed. Therefore, this paper aims to develop a psychometric instrument that measures the four dimensions of Smart Maintenance.

Design/methodology/approach

The results from two sequential empirical studies are presented, which include generating items to represent the constructs, assessment of content validity, as well as an empirical pilot test. With input from 50 industrial experts, a pool of 80 items that represent the constructs are generated. Thereafter, using data from 42 industrial and academic raters, the content validity of all items is assessed quantitatively. Finally, using data from 59 manufacturing plants, the dimensionality and factor structure of the instrument are tested.

Findings

The authors demonstrate content validity and provide evidence of good model fit and psychometric properties for one-factor models with 8–11 items for each of the four constructs, as well as a combined 24-item four-factor model.

Originality/value

The authors provide recommendations for scholarly use of the instrument in further theory-testing research, as well as its practical use to assess, benchmark and longitudinally evaluate Smart Maintenance within the manufacturing industry.

Details

International Journal of Operations & Production Management, vol. 40 no. 4
Type: Research Article
ISSN: 0144-3577

Keywords

1 – 10 of 130