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Constructing and evaluating behavioral science models is a complex process. Decisions must be made about which variables to include, which variables are related to each other, the functional forms of the relationships, and so on. The last 10 years have seen a substantial extension of the range of statistical tools available for use in the construction process. The progress in tool development has been accompanied by the publication of handbooks that introduce the methods in general terms (Arminger et al., 1995; Tinsley & Brown, 2000a). Each chapter in these handbooks cites a wide range of books and articles on specific analysis topics.

Statistical methods are important for meaningful analysis, critique and interpretation of results. The current study aims to investigate the use of statistical methods used in LIS research articles produced by Pakistani authors during 2001–2016.

Content analysis method with both the qualitative and quantitative components was used. LIS articles published by Pakistani authors in national and international journals from 2001 to 2016 were selected. The descriptive and inferential statistics were used to analyze the usage of statistical techniques.

The findings show that use of descriptive statistics remained higher as compared to inferential statistics in the LIS research produced by Pakistani authors. However, a visible growth trend in the use of inferential statistical techniques is found. Males are two times more likely to use inferential statistics as compared to female authors. Articles published in foreign journals and impact factor journals used more inferential statistics as compared to local and nonimpact factor journals. Parametric inferential statistics is more popular among Pakistani authors as compared to nonparametric. Faculty was more inclined toward using parametric statistic. The percentage of collaboration was higher in the papers using parametric statistics. Few articles reported the tests to fulfill the assumptions of parametric and nonparametric statistics.

This study can be used to better understand the trends of statistical techniques used in LIS research and authors' orientation in this regard. It will be helpful for future researchers in the selection of appropriate statistical techniques to be used.

The recent “reverse”¹ discrimination decisions by the Supreme Court involving the admissions decision-making policies at the University of Michigan² illustrate the underlying need for private and public entities to justify the need to reach or maintain diversity within an organization. Clearly, the equality of the decision-making methodology and criteria used to obtain and maintain diversity was an issue, but perhaps more pressing was the question of whether such programs were necessary. The issue of parity is at the very center of these cases. If the normal admissions process would have resulted in obtaining the predicted number of minority admissions then there may no longer be a need for such programs. While the university cases have been most publicized recently, matters involving affirmative action plans and governmental programs to enhance diversity (such as minority contractor set-asides) face similar questions of parity.

This chapter provides a presentation about Chapter 1 of The Balance of the National Economy, 1923–24, edited by Pavel Illich Popov. The Balance was issued in June 1926 by the Central Statistical Administration (CSU or TsSU) of the USSR, which Popov had headed from July 1918 to January 1926. In the first part of our chapter, we show how Popov’s work on the balance of the national economy was rooted in the specific scientific and political culture of zemstvo statisticians inherited from the Tsar. Statistical inquiry was considered an objective scientific process based on international standards. Furthermore, like zemstvo statisticians, CSU statisticians developed great autonomous political power. The balance of the national economy was built according to these principles, which met harsh criticism from revolutionaries and Bolsheviks. In the second part, we analyze the contents of Popov’s Chapter 1, especially the theoretical foundations of the balance and its connection with Soviet planning. In the third part, we discuss the balance’s significance in the years 1926–1929, years which ended the NEP and launched the first Five-Year Plan, so as to understand how CSU’s balance didn’t become a standard Soviet statistical instrument and was discarded as a “bourgeois” device.

This study reviews the use of meta-analysis in accounting research. We categorize the meta-analytic research into five topics: financial reporting, auditing, corporate governance and accounting quality, management accounting, and miscellaneous topics. Further, we classify the studies by the meta-analysis technique employed: Hunter et al. (1982), Hunter and Schmidt (2000), Lipsey and Wilson (2001), and Stouffer’s approach. We identify 27 meta-analytical studies over the period 1985–2014 with financial reporting (auditing) topics representing seven (six) of these studies. Our review highlights that meta-analytic methods are being applied and accepted, more frequently, to answer complex questions concerning the moderating effects of country-level variables, such as national culture, economic conditions, and institutional characteristics, on various associations of interest.

Quantitative methods, especially statistical methods, play an increasingly important role in research of library and information science (LIS). For different journals, the uses of statistical methods vary substantially due to different journal scopes and aims. The purpose of this paper is to explore the characteristics of statistical methodology uses in six major scholarly journals in LIS.

Research papers that used statistical methods from the six major journals were selected and investigated. Content analysis method, descriptive statistical analysis method, and temporal analysis method were used to compare and analyze statistical method uses in research papers of the investigated journals.

The findings of this study show that there was a clear growth trend of statistical method uses in five of the investigated journals; statistical methods were used most in The Journal of the Association for Information Science and Technology and Information Processing & Management; and the top three most frequently used statistical methods were t-test, ANOVA test, and χ2-test.

The findings can be used to better understand the application areas, patterns, and trends of statistical methods among the investigated journals and their statistical methodology orientations in research studies of LIS.

MIS researchers have consistently adopted survey‐based research method while investigating MIS and related phenomenon, making survey‐based research method one of the widely used research method in MIS research. This study seeks to revisit some of the inherent characteristics of survey‐based research method with the aim of improving the quality, replication, and validation of results in MIS survey‐based studies. Additionally, this study provides information on the most prevalent analytical and statistical tools used in MIS survey research studies.

In this research, the authors adopt the content analysis technique. The choice of content analysis is premised on the desire to investigate the sources of survey data, units of analysis, research methods, and statistical tools used in MIS research with the aim of improving empirical research in the MIS discipline.

The results show the prevalent sources of data, the dominant units of analysis, the most commonly used analytical research methods, and the statistical tools adopted by many MIS researchers. The results indicate that many MIS researchers get their data from US sources, although researchers are increasingly acquiring data from other countries. Also, the results reveal that most MIS survey researchers are using SEM, LISREL, and PLS statistical methods and tools.

The paper concludes with recommendations and implications on how to inform and retool upcoming and existing researchers on the current and future MIS research tools and methods. Editors should ensure that MIS researchers provide as much information as possible about the sources of data, the dominant units of analysis, the analytical research methods used, and the statistical tools adopted; these will demonstrate the rigor of the research process and enable replication, validation, and extension of the research works.

The paper presents the results of a content analysis of 749 survey‐based research articles published between 1990 and 2010 in nine mainstream MIS Journals. Prior studies have broadly addressed aspects of MIS research methodologies like investigating MIS research methods, ranking them, and generated a taxonomy of MIS research methodology. The results of this study make a case for the reporting of, both, the analytical method(s) and statistical tools used by MIS researchers to aid in replicating, validating, and extending the resultant findings of their survey‐based research.

Single subject research has long been employed to evaluate intervention effectiveness with students with learning or behavioral disabilities. Typically, the results of single subject research are presented on graphic displays and analyzed by a method of visual inspection, in which analysts simultaneously consider such data elements as level change, slope change, and variability in baseline and treatment data. However, over the years several concerns regarding visual inspection have emerged, including relatively low inter-rater reliabilities. This chapter reviews the arguments in favor of visual inspection as an analytic tool, and also summarizes the arguments favoring statistical analysis of single case data. The use of randomization tests is recommended, and an example is provided of its use in research with students with learning and behavioral disorders.

The statistical method plays an extremely important role in quantitative research studies in library and information science (LIS). The purpose of this paper is to investigate the status of statistical methods used in the field, their application areas and the temporal change patterns during a recent 15-year period.

The research papers in six major scholarly journals from 1999 to 2013 in LIS were examined. Factors including statistical methods, application areas and time period were analyzed using quantitative research methods including content analysis and temporal analysis methods.

The research studies using statistical methods in LIS have increased steadily. Statistical methods were more frequently used to solve problems in the information retrieval area than in other areas, and inferential statistical methods were used more often than predictive statistical methods and other statistical methods. Anomaly analysis on statistical method uses was conducted and four types of anomaly were specified.

The findings of this study can help educators, graduates and researchers in the field of LIS better understand the patterns and trends of the applications of statistical methods in this field, depict an overall picture of quantitative research studies in LIS from the perspective of statistical methods and discover the change patterns of statistical method applications in LIS between 1999 and 2013.

In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing.

In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing.

In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing.

In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing.