Panel data are sets of data in which the behaviour of each entity (individuals, firms, etc.) is observed over time. Panel data are also known as longitudinal or cross- sectional time-series data. Panel data contains observations of the same set of entities obtained over multiple time periods. Time-series and cross-sectional data can be thought of as special cases of panel data. Time series and cross-sectional
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data represent two sets of information, which have only one dimension, and can be derived from panel data. The cross-sectional component reflects the differences observed between entities, while the time series component reflects the differences observed for one entity across multiple time periods. Panel data sets come in two forms, balanced and unbalanced. In the case of balanced panel data, every cross- sectional entity is observed for the same time period. In the unbalanced panel case, the cross-sectional entities are observed for different time periods; in other words, some observations for some time periods are missing.
To support a thorough examination of the research question, a comprehensive dataset was prepared. Therefore, a panel regression approach was the preferred analytic model, and the panel approach requires data for individual company variables, cross-sectional data, and the same variables for the same companies over a number of years (the time-series component).
The dataset compiled for the study covers the majority of the publicly traded companies listed on China’s domestic stock exchanges (i.e., the Shanghai and Shenzhen Stock Exchanges). A panel set is often considered to be efficient in handling econometric data since it captures two-dimensional aspects of observations by including data for X cross-sections and Y time periods. To examine the impact of EM level on agency costs for listed Chinese companies from 1999 to 2014, a panel data set is appropriate. The panel data set applied in this study is unbalanced because of missing values for some companies over some years.
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The data collected combines two separate data sets; CSMAR6 and DataStream7. CSMAR and DataStream use different codes for the same company, although each database has a distinct code for each company. In order to merge the CSMAR and DataStream data, this study combines the CSMAR company code and DataStream company code via the unique full name of the company. This is the first study, as far as I am aware, that has developed a unique code that matches companies listed in both CSMAR and DataStream. Specifically, accounting and CG data (including total assets, total sales. total liability, leverage ratio, non-operating income, the number of directors, and the number of independent directors) were collected from CSMAR, while the industry type and stock exchange type data were collected from DataStream. CSMAR specializes in China stock market data and there are fewer missing values relative to other databases. Where necessary, any additional data were collected from DataStream or individual company websites.
Next, the research paradigm employed in this study is briefly discussed. In this study, the criteria used in the selection of the sample of companies are as follows. First, all the companies included in the sample must be listed companies on Shanghai Stock Exchange or Shenzhen Stock Exchange. Second, financial firms and banks are excluded from the sample. Third, the data or information for all firms included in the sample must be available in CSMAR and DataStream.
Research involves scientific practices that are “based on people’s philosophies and assumptions about the world and the nature of knowledge; in this context, about
6 The China Stock Market &Accounting Research (CSMAR) Database provides high-quality data on China’s stock markets and the financial statements of China’s listed companies. The CSMAR is jointly produced by GTA Information Technology Co. Ltd (the leading global provider of Chinese financial market data, Chinese industries and economic data) to cater for the needs of Chinese economic analysis and research by scholars from universities and financial institutions.
7 DataStream is a global financial and macroeconomic data platform covering equities, stock market indices, currencies, company fundamentals, fixed income securities and key economic indicators for 175 countries and 60 markets.
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how research should be conducted” (Collis & Hussey, 2013, p. 46). Therefore, the research paradigm (i.e., the way researchers design research, collect data and analyze data) is based on researchers’ basic beliefs about the world.
Bryman and Bell (2011, p. 24) comment that “[a] research paradigm is a cluster of beliefs and dictates which for scientists in a particular discipline influence what should be studied, how research should be done, and how results should be interpreted”. The basic research paradigm for this study was developed after examining two research approaches; qualitative and quantitative (Burrell & Morgan, 2007). The two fundamental research paradigms (or research philosophies) are given different names in different circumstances (Collis & Hussey, 2013). The alternative terms for the quantitative paradigm are positivist, objectivist, scientific, experimentalist, and traditionalist. The alternative terms for the qualitative paradigm include phenomenological, subjectivist, humanistic, and interpretivist. Based on the researcher’s subjective or objective view of social reality, research paradigms are classified as either qualitative or quantitative. Given their significant influence on the entire research process, it is important to identify research paradigms. Each research paradigm has assumptions based on fundamental aspects of ontology, epistemology, axiology, rhetoric, and methodology.
Quantitative and qualitative research paradigms vary in many aspects (Collis & Hussey, 2013; Johnson & Onwuegbuzie, 2004). The first relates to data and sampling. In a quantitative approach, quantitative data that is highly specific and precise are generated, while in a qualitative approach, data are more subjective, narrative, and may contain more literal descriptions instead of continuous numeric data. To pursue a quantitative approach, large samples are often required, as statistics generated from a larger population tend to be more reliable. In a qualitative approach, researchers tend to focus on a small sample in order to study the emerging
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phenomena in depth. The second area of difference is in the method applied. A quantitative study usually concerned with hypothesis testing, while a qualitative study is concerned with theory generation. In quantitative studies, an econometric and statistical method is commonly applied to generalize from sample to population. With the help of econometric techniques, researchers are able to eliminate the confounding influences of many variables and only use the variables of interest to assess the underlying cause and effect relationship more credibly. In contrast, a qualitative approach relies less on econometrics and focuses more on providing understandings and descriptions of people’s personal experiences, or the participants’ own categories of meaning. Thus, the qualitative approach is often used to describe complex phenomena. The third difference lies in the reporting of results. The results of quantitative approach are relatively independent of the researcher, and primarily based on statistical significance calculated using an econometric model. However, in reporting the outcomes of qualitative research, researchers can describe phenomena in rich detail using narrative.
Although the quantitative and qualitative approaches are different in many ways, they are not actually as discrete as they appear. There is an emerging approach named the mixed method, which combines quantitative and qualitative approaches. The belief is that combining the two approaches can provide a more complete understanding of the research problem than either the quantitative or qualitative approach alone.