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The purpose of this paper is to provide evidence that the U‐shaped relationship between intellectual property rights (IPRs) and per capita gross domestic product (GDP) observed in the past literature using a panel of data is not a consequence of longitudinal forces, as has been previously postulated, but instead a consequence of cross‐sectional influences.

Differences in the longitudinal and cross‐sectional relationship between IPRs and per capita GDP are analyzed through a variety of methods, including pooled regression analysis that isolates the regional differences that are critical in making an accurate longitudinal analysis from the panel data.

Analyzing the country data reveals that a longitudinal U‐shaped relationship is counterfactual, as countries generally do not weaken their IPRs once they are in place, barring a regime change or other alteration in their political economy. The significant U‐shape link between IPRs and per capita GDP empirically observed in preliminary analysis of the panel data is instead a result of cross‐sectional influences.

Making the distinction between the cross‐sectional and longitudinal relationship between IPRs and per capita GDP provides a more accurate insight about how IPRs change in a country as it develops.

One crucial but sometimes overlooked fact regarding the difference between observation in the cross-section and observation over time must be stated before proceeding further. Tempting though it is to draw conclusions about the dynamics of a process from cross-sectional observations taken as a snapshot of that process, it is a fallacious practice except under a very precise condition that is highly unlikely to obtain in processes of interest to the social scientist. That condition is known as ergodicity.

The purpose of this research is to examine the way uncertainty plays a role in built land prices. This paper provides basic real option pricing models of land prices on the demand side in central Tokyo. The model in this research analyzes micro land prices covering individual lot data provided by the Land Price Index. Since land prices are determined by both macro economic environment and micro lot‐specific attributes, this paper utilizes both time‐series economic data and cross‐sectional lot‐specific data. The model incorporates both time‐series (macro) and cross‐sectional (micro) data including uncertainty terms. In addition to the total uncertainty in asset prices over years, this research also gives some ideas of cross‐sectional uncertainty in land price variations by utilizing cross‐sectional amenity variables. These cross‐sectional and time‐series variables including the two uncertainty variables are arithmetically combined and the OLS method is conducted. The data set consists of 4,368 land price data from 1985 through 2000. The results from the option‐based models favor the application of the real option theory in land prices. The total uncertainty with respect to built asset return has a substantial effect on increasing land prices, which implies that an increase in uncertainty leads to an increase in land prices.

Strategy researchers typically avoid using data more than a few years old for estimation of cross-sectional models. However, problems that might be caused by older data generally reflect more basic weaknesses in research design. This chapter develops criteria for evaluating the importance of the age of data used in cross-sectional research and indicates ways that better research design may be more effective than the substitution of newer data sets.

Longitudinal regression analysis is conducted to clarify causal relations and control for unwanted influences from actor heterogeneity and state dependence on theoretically important coefficient estimates. Because strategic management contains theory on how firms differ and how firm actions are influenced by their current strategic position and recent experiences, consistency of theory and methodology often requires use of longitudinal methods. We describe the theoretical motivation for longitudinal methods and outline some common methods. Based on a survey of recent articles in strategic management, we argue that longitudinal methods are now used more frequently than before, but the use is still inconsistent and insufficiently justified by theoretical or empirical considerations. In particular, strategic management researchers should use dynamic models more often, and should test for the presence of actor effects, autocorrelation, and heteroscedasticity before applying corrections.

Longitudinal investigations are often suggested but rarely used in operations and supply chain management (OSCM), mainly due to the difficulty of obtaining data. There is a silver lining in the form of existing large-scale and planned repeated cross-sectional (RCS) data sets, an approach commonly used in sociology and political sciences. This study aims to review all relevant RCS surveys with a focus on OSCM, as well as data and methods to motivate longitudinal research and to study trends at the plant, industry and geographic levels.

A comparison of RCS, panel and hybrid surveys is presented. Existing RCS data sets in the OSCM discipline and their features are discussed. In total, 30 years of Global Manufacturing Research Group data are used to explore the applicability of analytical methods at the plant and aggregate level and in the form of multilevel modeling.

RCS analysis is a viable alternative to overcome the confines associated with panel data. The structure of the existing data sets restricts quantitative analysis due to survey and sampling issues. Opportunities surrounding RCS analysis are illustrated, and survey design recommendations are provided.

The longitudinal aspect of RCS surveys can answer new and untested research questions through repeated random sampling in focused topic areas. Planned RCS surveys can benefit from the provided recommendations.

RCS research designs are generally overlooked in OSCM. This study provides an analysis of RCS data sets and future survey recommendations.

Empirical accounting research frequently makes use of data sets with a time‐series and a cross‐sectional dimension ‐ a panel of data. The literature review indicates that South African researchers infrequently allow for heterogeneity between firms when using panel data and the empirical example shows that regression results that allow for firm heterogeneity are materially different from regression results that assume homogeneity among firms. The econometric analysis of panel data has advanced significantly in recent years and accounting researchers should benefit from those improvements.

Purpose – Information collected from police crash reports has long been the primary source of data for the analysis of factors that determine the likelihood of a crash (crash frequency) and its resulting severity (measured in terms of the extent of injuries to vehicle occupants). Proper cross-sectional analyses techniques, covered in this chapter, are important for guiding safety policy and countermeasures.

Methodology – This chapter provides an overview of some of the more commonly used cross-sectional statistical and econometric methods, and discusses the nuances and their limitations with regard to how they are applied to typical crash-report data.

Findings – The wide variety of analytic methods available to safety researchers makes the selection of appropriate methods critical. This chapter provides important guidance for safety researchers in their choice of methodological approach.

Implications – Understanding the importance of proper model specification, unobserved heterogeneity, endogeneity and other factors covered in this chapter is extremely important in analysing safety data and must be given full consideration before any results are finalised.

This article aims to provide prospective authors guidelines that will hopefully enable them to submit more competitive manuscripts to journals publishing careers research.

Based on their experience as an author, reviewer and editorial team member, the authors identify the main criteria that a quantitative study must meet to be considered for publication in international peer-reviewed journals covering career-related topics. They emphasize the importance of contributing to the careers literature and of designing the study in accordance with the research question.

Manuscripts are rejected because they are insufficiently innovative, and/or because sample, instruments and design are not appropriate to answer the research question at hand. Cross-sectional designs cannot be used to answer questions of mediation but should not be discarded automatically since they can be used to address other types of questions, including questions about nesting, clustering of individuals into subgroups, and to some extent, even causality.

The manuscript provides an insight into the decision-making process of reviewers and editorial board members and includes recommendations on the use of cross-sectional data.

This article reviews the extensive history of dynamic performance research, with the goal of providing a clear picture of where the field has been, where it is now, and where it needs to go. Past research has established that job performance does indeed change, but the implications of this dynamism and the predictability of performance trends remain unresolved. Theories are available to help explain dynamic performance, and although far from providing an unambiguous understanding of the phenomenon, they offer direction for future theoretical development. Dynamic performance research does suffer from a number of methodological difficulties, but new techniques have emerged that present even more opportunities to advance knowledge in this area. From this review, I propose research questions to bridge the theoretical and methodological gaps of this area. Answering these questions can advance both research involving job performance prediction and our understanding of the effects of human resource interventions.