Intimacy,rapport, and exceptional disclosure: a comparison of in-person and mediated interview contexts

While qualitative researchers increasingly accept online video interviews as a reliable method, many maintain concerns about rapport and data quality. Drawing on two separate interview projects conducted in private in-person settings, public in-person settings, and privately via Skype, we compare interview contexts with regard to rapport, suitability to sensitive topics, interview duration, and scheduling concerns raised by prior research. Analytical comparison of these two corpuses of data suggest, largely in contrast to previous literature, that (1) interviews conducted in private settings (either in-person or via Skype) result in more sharing of deeply personal experiences, and there is little difference in this exceptional disclosure between Skype and in-person private interviews; (2) interviewing via Skype produces neither reduction nor inappropriate excesses of rapport; and (3) Skype interviews are a popular choice among participants, did not result in shorter interview duration, and were not subject to greater rescheduling or cancellation.     

Indigenous data,indigenous methodologies and indigenous data sovereignty

The field of Indigenous methodologies has grown strongly since Tuhiwai Smith’s 1999 groundbreaking book Decolonizing Indigenous Methodologies. For the most part however, there has been a marked absence of quantitative methodologies with the methods aligned with Indigenous methodologies predominantly qualitative. This article proposes that the absence of an Indigenous presence from Indigenous data production has resulted in an overwhelming statistical narrative of deficit for dispossessed Indigenous peoples around the globe. Using the theoretical concept of Indigenous Lifeworlds this article builds on the core premises of Walter and Andersen’s 2013 book Indigenous quantitative methodologies. Arguing for a fundamental disturbance of the Western logics of statistical data the article details recent developments in the field including the emergence of the Indigenous Data Sovereignty movement. The article also explores Indigenous quantitative methodologies in practice using the case study of a Tribal Epidemiology Centre in New Mexico.     

What influences the size of child maintenance payments? A register‐based study of maintenance payments in Finland

The aim of this study was to explore how particular economic and demographic factors contribute to the level of the child maintenance payment (CMP) paid by the non‐resident parent. For this study, we used 5‐year longitudinal panel data from years 2009 to 2013 consisting of over 80,000 non‐resident parents from the Finnish Tax Administration and The Finnish Population Register Centre. Results from regression models indicate that the single biggest factor affecting the size of CMPs is the number of dependent children. We found that the non‐resident parent's higher income is associated with higher CMPs and that non‐resident fathers pay on average larger CMPs than non‐resident mothers, even after accounting for differences in income. Unexpectedly, we found that the age of the dependent child did not predict changes in CMPs. This suggests that once formal CMP‐contracts are determined between the parents, they are seldom changed. Our results suggest that some degree of mandatory periodic review for maintenance contracts is worth considering.     

Risk factors for child maltreatment in South Korea: An investigation of a nationally representative sample

This study investigated the risk factors associated with the occurrence of child physical and psychological abuse in South Korea based on the ecological theory of child maltreatment. A subsample of 3‐ to 18‐year‐old children from “A Study on the Current State of Child Abuse and Neglect,” a nationally representative study on child abuse and neglect, was utilized for secondary data analysis. The sample was divided into two age groups (third graders in elementary school and below and fourth graders and above). We utilized hierarchical logistic regression for each age group separately to analyze the data. The variables at each level of the system (i.e., ontogenic development, microsystem, and exosystem) were entered into the model in sequential order. The results showed the common risk factors for both age groups are the caregiver's experience of abuse in childhood, the child's problem behavior, exposure to domestic violence, community size, and informal social control. The child's age was also significant in both groups but in the opposite direction. Social support was a significant predictor for the younger age group only, while the caregiver's level of education was significant for the older age group only. Implications for future research and practice are discussed based on the study results.     

Hypothesis Testing of CNA Perceptions of Organizational Culture in Long Term Care

This commentary for the special issue on research that went wrong describes a study that explored factors that contribute to variability within Certified Nursing Assistants (CNAs) on organizational safety culture. We know from previous research that CNAs provide most direct care in nursing homes and that direct care workers often experience agency culture differently from agency management (Wolf et al., 2014). We were looking for factors that nursing homes could alter to improve the culture for CNAs, and thus, residents. We conducted a secondary analysis of data collected via a multi-component paper survey of CNAs employed in long term care. We used results from the Nursing Home Survey on Patient Safety Culture and primary shift, type of unit, and years as a CNA to identify modifiable characteristics that would explain variability in the perceptions of patient safety culture. The final sample included n = 106 from three nursing homes. Dimension scores were compared using bivariate tests appropriate to the scale and ordinal logistic regression. Despite support in the literature for the hypothesis, we found few significant differences on the total scale within groups. Differences in perceptions have implications for quality of care and the experiences of residents within nursing homes.     

大数据驱动下的影视文本创作 和生产模式变革

大数据正以其对规律和趋势的分析预测功能,对人们生活和影视内容生产产生日益深刻的影响。数据化思维是贯穿当下影视生产创作的重要思维形态,延承的正是传统数据化思维的基本认知过程。大数据驱动下的互动参与生产模式主要体现在,借助于受众行为数据分析,可以对导演、故事、表演、影像、剪辑和声音等影视元素进行选择,并在此基础上进行生产或再生产。这些将推动影视内容生产在技术和观念上不断变革。     

Zero-inflated sum of Conway-Maxwell-Poissons (ZISCMP) regression

While excess zeros are often thought to cause data over-dispersion (i.e. when the variance exceeds the mean), this implication is not absolute. One should instead consider a flexible class of distributions that can address data dispersion along with excess zeros. This work develops a zero-inflated sum-of-Conway-Maxwell-Poissons (ZISCMP) regression as a flexible analysis tool to model count data that express significant data dispersion and contain excess zeros. This class of models contains several special case zero-inflated regressions, including zero-inflated Poisson (ZIP), zero-inflated negative binomial (ZINB), zero-inflated binomial (ZIB), and the zero-inflated Conway-Maxwell-Poisson (ZICMP). Through simulated and real data examples, we demonstrate class flexibility and usefulness. We further utilize it to analyze shark species data from Australia's Great Barrier Reef to assess the environmental impact of human action on the number of various species of sharks.     

The nested joint clustering via Dirichlet process mixture model

This article focuses on the clustering problem based on Dirichlet process (DP) mixtures. To model both time invariant and temporal patterns, different from other existing clustering methods, the proposed semi-parametric model is flexible in that both the common and unique patterns are taken into account simultaneously. Furthermore, by jointly clustering subjects and the associated variables, the intrinsic complex shared patterns among subjects and among variables are expected to be captured. The number of clusters and cluster assignments are directly inferred with the use of DP. Simulation studies illustrate the effectiveness of the proposed method. An application to wheal size data is discussed with an aim of identifying novel temporal patterns among allergens within subject clusters.     

Robust inference for estimating equations with nonignorably missing data based on SIR algorithm

Nonresponse is a very common phenomenon in survey sampling. Nonignorable nonresponse – that is, a response mechanism that depends on the values of the variable having nonresponse – is the most difficult type of nonresponse to handle. This article develops a robust estimation approach to estimating equations (EEs) by incorporating the modelling of nonignorably missing data, the generalized method of moments (GMM) method and the imputation of EEs via the observed data rather than the imputed missing values when some responses are subject to nonignorably missingness. Based on a particular semiparametric logistic model for nonignorable missing response, this paper proposes the modified EEs to calculate the conditional expectation under nonignorably missing data. We can apply the GMM to infer the parameters. The advantage of our method is that it replaces the non-parametric kernel-smoothing with a parametric sampling importance resampling (SIR) procedure to avoid nonparametric kernel-smoothing problems with high dimensional covariates. The proposed method is shown to be more robust than some current approaches by the simulations.     

Quantile regression for large-scale data via sparse exponential transform method

In recent decades, quantile regression has received much more attention from academics and practitioners. However, most of existing computational algorithms are only effective for small or moderate size problems. They cannot solve quantile regression with large-scale data reliably and efficiently. To this end, we propose a new algorithm to implement quantile regression on large-scale data using the sparse exponential transform (SET) method. This algorithm mainly constructs a well-conditioned basis and a sampling matrix to reduce the number of observations. It then solves a quantile regression problem on this reduced matrix and obtains an approximate solution. Through simulation studies and empirical analysis of a 5% sample of the US 2000 Census data, we demonstrate efficiency of the SET-based algorithm. Numerical results indicate that our new algorithm is effective in terms of computation time and performs well for large-scale quantile regression.