As a direct reference tool to reflect the operational status and development level of national industry, industrial statistics hold significant value for numerous systematic studies. Nevertheless, it ...is crucial to recognize that the quality of these statistics can be compromised by the common occurrence of missing value. This issue poses a substantial challenge for analyzing and utilizing industrial statistics, impeding progress in tasks reliant upon them. Given the severity of the missing value problem in industrial statistical databases and the limitations of existing literatures on missing value imputation in terms of research objects and modeling approaches, this paper proposes a novel missing value imputation modeling approach for single-indicator panels of industrial statistics based on the idea of data-characteristic-driven (DCD). Accordingly, taking the inter-provincial “monthly power generation” data from China as an example, the imputation model was constructed and its validity was tested under different imputed objects (Jiangsu and Jilin), different missing types (continuous and discrete), and different missing rates (5%, 10% and 20%) respectively. The results indicate that the proposed DCD modeling approach in this paper exhibits excellent efficacy. The imputation model, constructed based on the data characteristic of the imputed object, demonstrates clear advantages in handling missing value with different missing types and rates. This is evident in its superior consideration of numerical accuracy, directional accuracy, and imputation stability, resulting in an outstanding comprehensive imputation effect.
•A novel DCD modeling approach for missing value imputation of industrial statistics is proposed.•A data characteristic recognition path of single-indicator panel data is constructed.•The match between data characteristic and model mechanisms improves imputation performance.
•A thorough review of AIB control charting along with the underlying assumptions.•Derivation of ARL expression for AIB control chart with shifted auxiliary variable.•A modified AIB method that is ...robust to shifts in auxiliary variable.•An application of the proposed methodology in industrial process.
During the last decade, auxiliary information based (AIB) control charts have gained a lot of popularity for process monitoring. These charts are proved efficient for early detection of shifts in process parameters like location and dispersion. A recent study has pointed out few concerns related to AIB control charts which were already highlighted in earlier studies one way or the other. In reply to that, we reiterate the initial classification of processes by Hawkins (1993) namely non-cascading processes where the change in one variable may not affect the other variable. Under the non-cascading situations, AIB control charts are designed with the assumption that auxiliary variable remains stable even if there is a shift in study variable. This assumption is true for some processes. On the other hand, in many non-cascading processes where the auxiliary variable can experience a shift, the performance of AIB control charts is highly affected specially when the auxiliary variable(s) and study variable are strongly correlated. This study proposes a modified AIB control charting structure that safeguards the detection ability and the run length properties in case auxiliary variable experiences a shift. In addition, the usual AIB and classical Shewhart charts become a special case of the proposed chart. For several values of design parameter, the in control and out of control performance of proposed chart is evaluated in terms of average run length. It is observed that the proposed modified AIB control chart is capable of increasing its robustness against any shift in the auxiliary variable through a design parameter. An extensive comparative analysis revealed that the newly proposed modified AIB control chart has capability to absorb a shift of any size in auxiliary variable, maintaining the gain in terms of efficiency. An illustrative example is also included to support the finding of our study.
The authenticity and quality of industrial statistical data directly affects all types of systematic research based on it. Considering the limitations of extant data quality evaluation literature on ...research objects and evaluation methods, we constructed a new data quality comprehensive inspection and evaluation model based on Benford's Law (BL) and the technique for order of preference by similarity to ideal solution (TOPSIS), selected coal-related industries as the research object, and conducted an empirical test along the research path of “Industry→Province→Indicator”. The results showed that, at industry level, the quality of statistical data for China's coal-related industries from 2001 to 2016 was generally poor. Among the eight sample industries selected, the data quality for five industries (including coal, electricity, and steel) was assessed as poor or slightly poor. Furthermore, at the provincial level, there is significant spatial heterogeneity in the quality of statistical data for various industries affected by factors such as economic structure, marketization level, and industrial diversity. Compared with other types of statistical indicators, industry financial indicators are more prone to data quality problems at the indicator level, and the suspicious indicators of different industries show certain common characteristics and some industry differences. To improve the quality of industrial statistical data and reduce the possible adverse impacts of data quality problems, based on the research findings, we propose targeted countermeasures and suggestions on how to prevent data fraud and effectively identify and rationally use suspicious data.
•A novel model for evaluating the quality of industrial statistical data is proposed.•The quality of statistical data for China's coal and its downstream industries is generally poor.•There is significant spatial heterogeneity in the level of statistical data quality between provinces.•The suspiciousness indicators not only show certain industry common features, but some industry differences.
The authors conducted a content analysis
of all articles published in the
Journal of Applied
Psychology
and
Personnel
Psychology
from January 1963 to May 2007
(
N
= 5,780) to identify the relative
...attention devoted to each of 15 broad topical areas and 50
more specific subareas in the field of industrial and
organizational (I-O) psychology. Results revealed
that (a) some areas have become more (or less) popular over
time, whereas others have not changed much, and (b) there
are some lagged relationships between important societal
issues that involve people and work settings (i.e.,
human-capital trends) and I-O psychology research
that addresses them. Also, much I-O psychology
research does not address human-capital trends.
Extrapolating results from the past 45 years to the next
decade suggests that the field of I-O psychology
is not likely to become more visible or more relevant to
society at large or to achieve the lofty goals it has set
for itself unless researchers, practitioners, universities,
and professional organizations implement significant
changes. In the aggregate, the changes address the broad
challenge of how to narrow the
academic-practitioner divide.
STAN: OECD Structural Analysis Statistics 2014 provides analysts and researchers with a comprehensive tool for analysing industrial performance across countries. The publication includes following ...annual measures: production, value added (at current and constant prices), gross fixed capital formation, number engaged and labour compensation. Data are presented in national currency for current price data, in terms of the current price value in the reference year (usually 2005) for volume data and in number of persons for employment data. Coverage is provided for 15 OECD countries and for multiple sectors, with extended coverage of service sectors according to ISIC Revision 4 classification.
