Big Data,Big Waste? A Reflection on the Environmental Sustainability of Big Data Initiatives

Science and Engineering Ethics - This paper addresses a problem that has so far been neglected by scholars investigating the ethics of Big Data and policy makers: that is the ethical implications...     

Happiness,Faith, Friends,and Fortune—Empirical Evidence from the 1998 US Survey Data

This paper studies both pecuniary and non-pecuniary factors affecting happiness—an issue that has sparked a great deal of interest in the economic literature. Using an ordered Probit model and the 1998 general social survey (GSS) data, the paper empirically demonstrates the extent to which socioeconomic and demographic variables along with faith and emotionally based factors may determine happiness. The 1998 survey was conducted nearly at the conclusion of one of the longest economic expansion—a high income low inflation era in the US history. However, the findings tend to suggest that the absolute value of nominal income insignificantly, but non-pecuniary elements (faith and emotionally based factors including financial security) significantly determine happiness.

The Importance of Defining ‘Data’ in Data Management Policies

What comprises ‘data’ varies from one institution to another based on the information which is deemed important by individual institutions. To effectively and efficiently produce, collect, and retain data, an organization develops specific defining characteristics of data to meet its informational needs. Procedures to maintain and retain knowledge among laboratory members and principal investigators will allow for improved efficiency of data collection. Optimization of communication, maintenance of inventories, record keeping, and updating relevant training programs are all critical to supporting the quality and integrity of a particular organization’s data. Concurrent revisions to such procedures will ensure that the definition of data as well as the means by which it is collected and maintained remain appropriate to the needs of the individual organization.     

Tests of Homoscedasticity,Normality, and Missing Completely at Random for Incomplete Multivariate Data

Test of homogeneity of covariances (or homoscedasticity) among several groups has many applications in statistical analysis. In the context of incomplete data analysis, tests of homoscedasticity among groups of cases with identical missing data patterns have been proposed to test whether data are missing completely at random (MCAR). These tests of MCAR require large sample sizes n and/or large group sample sizes n _i , and they usually fail when applied to nonnormal data. Hawkins (Technometrics 23:105–110, 1981) proposed a test of multivariate normality and homoscedasticity that is an exact test for complete data when n _i are small. This paper proposes a modification of this test for complete data to improve its performance, and extends its application to test of homoscedasticity and MCAR when data are multivariate normal and incomplete. Moreover, it is shown that the statistic used in the Hawkins test in conjunction with a nonparametric k-sample test can be used to obtain a nonparametric test of homoscedasticity that works well for both normal and nonnormal data. It is explained how a combination of the proposed normal-theory Hawkins test and the nonparametric test can be employed to test for homoscedasticity, MCAR, and multivariate normality. Simulation studies show that the newly proposed tests generally outperform their existing competitors in terms of Type I error rejection rates. Also, a power study of the proposed tests indicates good power. The proposed methods use appropriate missing data imputations to impute missing data. Methods of multiple imputation are described and one of the methods is employed to confirm the result of our single imputation methods. Examples are provided where multiple imputation enables one to identify a group or groups whose covariance matrices differ from the majority of other groups.     

Is Semantic Information Meaningful Data?

There is no consensus yet on the definition of semantic information. This paper contributes to the current debate by criticising and revising the Standard Definition of semantic Information (SDI) as meaningful data, in favour of the Dretske-Grice approach: meaningful and well-formed data constitute semantic information only if they also qualify as contingently truthful. After a brief introduction, SDI is criticised for providing necessary but insufficient conditions for the definition of semantic information. SDI is incorrect because truth-values do not supervene on semantic information, and misinformation (that is, false semantic information) is not a type of semantic information, but pseudo-information, that is not semantic information at all. This is shown by arguing that none of the reasons for interpreting misinformation as a type of semantic information is convincing, whilst there are compelling reasons to treat it as pseudo-information. As a consequence, SDI is revised to include a necessary truth-condition. The last section summarises the main results of the paper and indicates some interesting areas of application of the revised definition.     

Data Mining and Privacy of Social Network Sites’ Users: Implications of the Data Mining Problem

This paper explores the potential of data mining as a technique that could be used by malicious data miners to threaten the privacy of social network sites (SNS) users. It applies a data mining algorithm to a real dataset to provide empirically-based evidence of the ease with which characteristics about the SNS users can be discovered and used in a way that could invade their privacy. One major contribution of this article is the use of the decision forest data mining algorithm (SysFor) to the context of SNS, which does not only build a decision tree but rather a forest allowing the exploration of more logic rules from a dataset. One logic rule that SysFor built in this study, for example, revealed that anyone having a profile picture showing just the face or a picture showing a family is less likely to be lonely. Another contribution of this article is the discussion of the implications of the data mining problem for governments, businesses, developers and the SNS users themselves.     

Model Conditioned Data Elasticity in Path Analysis: Assessing the “Confoundability” of Model/Data Characteristics

Much research has been directed at the validity of fit indices in Path Analysis and Structural Equation Modeling (e.g., Browne, MacCallum, Kim, Andersen, &; Glaser, 2002 Browne, M.W., MacCallum, R.C., Kim, C.T., Andersen, B.L., &; Glaser, R. (2002). When fit indices and residuals are incompatible. Psychological Methods, 7, 403–421.[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]; Heene, Hilbert, Draxler, Ziegler, &; Bühner, 2011 Heene, M., Hilbert, S., Draxler, C., Ziegler, M., &; Bühner, M. (2011). Masking misfit in confirmatory factor analysis by increasing unique variances: A cautionary note on the usefulness of cutoff values of fit indices. Psychological Methods, 16(3), 319–336.[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]; Hu &; Bentler, 1999 Hu, L., &; Bentler, P. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, J., 6(1), 1–55.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]; Marsh, Hau, &; Wen, 2004 Marsh, H.W., Hau, K.T., &; Wen, Z. (2004). In search of golden rules: Comment on hypothesis-testing approaches to setting cutoff values for fit indexes and dangers in overgeneralizing Hu and Bentler’s (1999) findings. Structural Equation Modeling, 11(3), 320–341.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]). Recent developments (e.g., Preacher, 2006 Preacher, K.J. (2006). Quantifying parsimony in structural equation modeling. Multivariate Behavioral Research, 41(3), 227–259.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]; Roberts &; Pashler, 2000 Roberts, S., &; Pashler, H. (2000). How persuasive is a good fit? A comment on theory testing. Psychological Review, 107(2), 358–367.[Crossref], [PubMed], [Web of Science ®] , [Google Scholar], 2002 Roberts, S., &; Pashler, H. (2002). Reply to Rodgers and Rowe (2002). Psychological Review, 109(3), 605–607.[Crossref] , [Google Scholar]) have encouraged researchers to investigate other criteria for comparing models, including model complexity. What has not been investigated is the inherent ability of a particular data set to be fitted with a constrained set of randomly generated linear models, which we call Model Conditioned Data Elasticity (DE). In this article we show how DE can be compared with the problem of equivalent models and a more general problem of the “confoundability” of data/model combinations (see MacCallum, Wegener, Uchino, &; Fabrigar, 1993 MacCallum, R.C., Wegener, D.T., Uchino, B.N., &; Fabrigar, L.R. (1993). The problem of equivalent models in applications of covariance structure analysis. Psychological Bulletin, 114(1), 185–199.[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]). Using the DE package in R, we show how DE can be assessed through automated computer searches. Finally, we discuss how DE fits within the controversy surrounding the use of fit statistics.     

When Insurance Companies and Clinicians Pay Attention to Data,Everybody Wins: A Commentary on Koons,O’Rourke,Carter, and Erhardt (2013)

Insurance companies and clinicians may appear to be strange bedfellows, however, both have common objectives. This commentary describes how, using Koons, O’Rourke, Carter, and Erhardt (2013) as a model, CBT clinicians can learn to actively change the system to benefit both the providers and the clients. By doing so, we can increase the availability of evidence-based practices to a larger proportion of the population and not just individuals who can afford to pay out-of-network.     

Omitting Data—Ethical or Strategic Problem?

Omitting experimental data is often considered a violation of scientific integrity. If we consider experimental inquiry as a questioning process, omitting data is seen to be merely an example of tentatively rejecting (‘bracketing’) some of nature’s answers. Such bracketing is not only occasionally permissible; sometimes it is mandated by optimal interrogative strategies. When to omit data is therefore a strategic rather than ethical question. These points are illustrated by reference to Millikan’s oil drop experiment.     

Modeling Concordance Correlation Coefficient for Longitudinal Study Data

Measures of agreement are used in a wide range of behavioral, biomedical, psychosocial, and health-care related research to assess reliability of diagnostic test, psychometric properties of instrument, fidelity of psychosocial intervention, and accuracy of proxy outcome. The concordance correlation coefficient (CCC) is a popular measure of agreement for continuous outcomes. In modern-day applications, data are often clustered, making inference difficult to perform using existing methods. In addition, as longitudinal study designs become increasingly popular, missing data have become a serious issue, and the lack of methods to systematically address this problem has hampered the progress of research in the aforementioned fields. In this paper, we develop a novel approach to tackle the complexities involved in addressing missing data and other related issues for performing CCC analysis within a longitudinal data setting. The approach is illustrated with both real and simulated data.     

How Are Personality States Associated with Smartphone Data?

Smartphones promise great potential for personality science to study people's everyday life behaviours. Even though personality psychologists have become increasingly interested in the study of personality states, associations between smartphone data and personality states have not yet been investigated. This study provides a first step towards understanding how smartphones may be used for behavioural assessment of personality states. We explored the relationships between Big Five personality states and data from smartphone sensors and usage logs. On the basis of the existing literature, we first compiled a set of behavioural and situational indicators, which are potentially related to personality states. We then applied them on an experience sampling data set containing 5748 personality state responses that are self-assessments of 30 minutes timeframes and corresponding smartphone data. We used machine learning analyses to investigate the predictability of personality states from the set of indicators. The results showed that only for extraversion, smartphone data (specifically, ambient noise level) were informative beyond what could be predicted based on time and day of the week alone. The results point to continuing challenges in realizing the potential of smartphone data for psychological research. © 2020 The Authors. European Journal of Personality published by John Wiley & Sons Ltd on behalf of European Association of Personality Psychology     

Inferential Precision in Single-Case Time-Series Data Streams: How Well Does the EM Procedure Perform When Missing Observations Occur in Autocorrelated Data?

The case-based time-series design is a viable methodology for treatment outcome research. However, the literature has not fully addressed the problem of missing observations with such autocorrelated data streams. Mainly, to what extent do missing observations compromise inference when observations are not independent? Do the available missing data replacement procedures preserve inferential integrity? Does the extent of autocorrelation matter? We use Monte Carlo simulation modeling of a single-subject intervention study to address these questions. We find power sensitivity to be within acceptable limits across four proportions of missing observations (10%, 20%, 30%, and 40%) when missing data are replaced using the Expectation-Maximization Algorithm, more commonly known as the EM Procedure (Dempster, Laird, &; Rubin, 1977). This applies to data streams with lag-1 autocorrelation estimates under 0.80. As autocorrelation estimates approach 0.80, the replacement procedure yields an unacceptable power profile. The implications of these findings and directions for future research are discussed.     

What Is Missing in Counseling Research? Reporting Missing Data

Missing data have long been problematic in quantitative research. Despite the statistical and methodological advances made over the past 3 decades, counseling researchers fail to provide adequate information on this phenomenon. Interpreting the complex statistical procedures and esoteric language seems to be a contributing factor. An overview of missing data and remediation procedures is provided. A systematic approach to addressing missing data along with reporting suggestions is presented.     

Further Validational Data on “A Simple Course Evaluation Scale”

Two studies test the hypothesis that a child's scores on two factor analytically derived dimensions of social-emotional functioning are directly related to the frequency and type of his peer interactions. In the first study, three measures of observer rated peer interaction correlated negatively and significantly with high scores on the Apathy-Withdrawal dimension.

In the second study, observation-based frequency counts showed a high frequency of solitary behavior for apathetic-withdrawn children, as well as a number of qualitative differences between the groups in kinds of interactive behavior: angry-defiant children tended to be both verbal and negative-hostile, while apathetic-withdrawn children, when they ddid interact, were unable to cope with aggression directed at them by their peers.     

A Hierarchical Ornstein–Uhlenbeck Model for Continuous Repeated Measurement Data

In this paper, we present a diffusion model for the analysis of continuous-time change in multivariate longitudinal data. The central idea is to model the data from a single person with an Ornstein–Uhlenbeck diffusion process. We extend it hierarchically by allowing the parameters of the diffusion process to vary randomly over different persons. With this approach, both intra and interindividual differences are analyzed simultaneously. Furthermore, the individual difference parameters can be regressed on covariates, thereby providing an explanation of between-person differences. Unstructured and unbalanced data pose no problem for the model to be applied. We demonstrate the method on data from an experience sampling study to investigate changes in the core affect. It can be concluded that different factors from the five factor model of personality are related to features of the trajectories in the core affect space, such as the cross-correlation and variability of the changes.     

Data selection and responsible conduct: Was millikan a fraud?

This paper addresses a problem in reporting scientific research. The problem is how to distinguish between justifiable and unjustifiable data selection. Robert Millikan is notorious for an infamous remark that he used all his data when in fact he had used a selection. On this basis he has been accused of fraud. There is a tension here — historians and his defenders see his selection as understandable and legitimate, while current statements about the Responsible Conduct of Research imply his selection was illegitimate. This paper discusses two main issues that arise in assessing his conduct, whether he was intentionally misleading and whether he actually did mislead the scientific community about some facts of nature. It is argued that he was not intentionally misleading, and that it is unlikely that he misled the scientific community.     

Paper or plastic? Data equivalence in paper and electronic diaries

Concern has been raised about the lack of participant compliance in diary studies that use paper-and-pencil as opposed to electronic formats. Three studies explored the magnitude of compliance problems and their effects on data quality. Study 1 used random signals to elicit diary reports and found close matches to self-reported completion times, matches that could not plausibly have been fabricated. Studies 2 and 3 examined the psychometric and statistical equivalence of data obtained with paper versus electronic formats. With minor exceptions, both methods yielded data that were equivalent psychometrically and in patterns of findings. These results serve to at least partially mollify concern about the validity of paper diary methods.     

The Nonsingularity of Γ in Covariance Structure Analysis of Nonnormal Data

Covariance structure analysis of nonnormal data is important because in practice all data are nonnormal. When applying covariance structure analysis to nonnormal data, it is generally assumed that the asymptotic covariance matrix Γ for the nonredundant terms in the sample covariance matrix S is nonsingular. It is shown this need not be the case, which raises a question of how restrictive this assumption may be and how difficult it may be to verify it. It is shown that Γ is nonsingular whenever sampling is from a nonsingular distribution, including any distribution defined by a density function. In the discrete case necessary and sufficient conditions are given for the nonsingularity of Γ, and it is shown how to demonstrate Γ is nonsingular with high probability. Thus, the nonsingularity of Γ assumption is mild and one should feel comfortable about making it. These observations also apply to the asymptotic covariance matrix Γ that arises in structural equation modeling.