User Spatial Content in Social Research: Approaches, Opportunities, and Challenges

With the development of the Internet, especially Web 2.0, user-generated contents such as “Volunteered Geographic Information (VGI)” have gained attention and generated a lot of information over the Web. This chapter introduces the basic information about VGI and presents that VGI (or crowd-sourcing) applications have proven useful for gathering and sharing spatial information. However, one major problem of VGI is the existence of the semantic heterogeneity problem. A semantic-empowered analysis tool, which is able to automatically integrate and analyze a broad variety of spatial data contents, is needed to provide more utilities for VGI. The framework of an interoperable online VGI system based on Geospatial Semantic Web technologies was proposed. Some challenges or issues faced by VGI were discussed in this chapter.

Missing data are nearly always a problem in research, and missing values represent a serious threat to the validity of inferences drawn from findings. Increasingly, social science researchers are turning to multiple imputation to handle missing data. Multiple imputation, in which missing values are replaced by values repeatedly drawn from conditional probability distributions, is an appropriate method for handling missing data when values are not missing completely at random. However, use of this method requires developing an imputation model from the observed data. This is typically a rigorous and time-consuming process. To encourage wider adoption of multiple imputation in social work research, a simple framework for designing imputation models is presented. The framework and its ability to generate unbiased estimates are demonstrated in a simulation study. KEY WORDS: missing data; multiple imputation; nonresponse ********** Missing data are ubiquitous in social research, and missingness or nonresponse can represent a threat to the validity of inferences because of undue effects on efficiency, power, and parameter bias (Shadish, Cook, & Campbell, 2002). Social work researchers are now addressing missing data in a more rigorous manner. Recently, Saunders et al. (2006) and Choi, Golder, Gillmore, and Morrison (2005) described important data imputation methods and dispelled misunderstandings regarding popular imputation methods, such as mean substitution. Recent advances in analytic methods, such as multiple imputation (MI), are taking hold in social work research. With MI, missing values are replaced with values repeatedly drawn from simulated conditional probability distributions (Schafer, 1997), thus creating multiple versions of the data set. Each version of the data set is analyzed according to the data analysis model, and the multiple results are combined into point estimates (Rubin, 1996). A critical task in MI is to devise an imputation model (Allison, 2002) or missing data model (Graham, Olchowski, & Gilreath, 2007), which involves specifying the measures that are putatively associated with the missing values. Although this process adds additional steps, the specification of an imputation model and the creation of multiple data sets can produce less-biased estimates in the presence of missing data across a wide variety of data analysis techniques (Schafer, 1997). Besides MI, there are many other methods for addressing missing data (Schafer, 1999; Schafer & Graham, 2002). An equally rigorous method known as direct or full information maximum likelihood (FIML) estimation can produce unbiased estimates and correct standard errors in the presence of missing data. When the number of imputations is sufficiently large, identical missing data models will produce the same estimates under MI and FIML (Graham et al., 2007). Unlike MI, FIML is limited to maximum likelihood analytic techniques and the missing data model must be included in the analysis model. Although we focus on MI, the steps we describe for developing an imputation model are equally appropriate for use in the missing data model for FIML. On the basis of the MI literature, this article describes a framework for developing an imputation model for use with any free or commercial software package that performs MI. BRIEF REVIEW OF MISSING DATA CONCEPTS Generally, both MI and a broad range of missing data issues have received ample attention in the applied literature (Graham et al., 2007). We briefly discuss the three types of distributions that describe the randomness of nonresponse given that this property has consequences for the development of an imputation model. For a discussion of general missing data concepts that are not critical to understanding our discussion of imputation model development, we refer readers to Schafer and Graham (2002). Distribution of Nonresponse The probability distribution of nonresponse--more frequently referred to as the missing data or nonresponse mechanism (Rubin, 1976)--is both an important factor in the decision to impute with MI and a context for the development of an imputation model. …

The use of online media has led to an increase in digital footprints of human behavior, which has resulted in a growing interest in the collection and analysis of such data in non-intrusive ways. One such approach is digital data donation, which involves requesting participants to share data that they have requested from digital platforms with researchers. While this approach promises to provide an unprecedented level of detail for computational communication research, it also raises concerns about the representativeness and validity of the data. This study investigates the issue of potential selection bias and non-response bias in data donation samples. It aims to identify subparts of the population that might be underrepresented (or even absent) in data donation sampling methods, which might lead to inaccurate and biased research conclusions. Utilizing a survey with a sample frame of 1178 and 289 participants from the Netherlands, the study investigates the relation between demographics, knowledge, privacy and trust factors, and donation behavior. The results show biases in donation behavior in terms of age and digital and algorithmic efficacy, while privacy and trust factors are not related to it. It suggests that individuals decide not to donate to academic research due to skills and not due to concerns or lack of trust. This offers possibilities for improvement in study design so that all willing individuals are equally able to participate.

Social science research has been attacked by neoliberal thinkers who allege that such research lacks economic objectives. In the face of neoliberal and positivist criteria for evaluating the social impact of social science inquiry, social science researchers are developing qualitative evaluation methodologies through which we can have direct contact with citizens. These qualitative methodologies declare our social responsibility as social researchers in addressing relevant problems, especially those affecting the most vulnerable people. From these qualitative methodologies, the most vulnerable groups are included in the assessment of the social impacts of social research. Some examples of people who have participated in this qualitative evaluation include women, youth, immigrants, and Roma organizations. Participants perceived social science researchers as being far from their social reality, but in this research, they began to overcome their skepticism that social science research can help to solve those problems affecting their everyday lives.

The ever-growing availability of Earth Observation (EO) data is demonstrating a wide range of potential applications in the realm of land management. On the other hand, large volumes of data need to be handled and analysed to extract meaningful information and Geomatics coupled with new approaches such as Artificial Intelligence (AI) and Machine Learning (AI) will play a pivotal role in the years to come. Training datasets need to be developed to use these new models and Volunteered Geographic Information can be one of the promising sources for EO processing. Among the various applications, agriculture may benefit from the large dataset availability and AI processing. However, several issues remain unsolved and further steps should be taken in the near future by researchers and policy makers.

This book guides social researchers and instructors in the practice of interpretive research. Interpretivism has an intuitive appeal to many social researchers, but they often feel ill-equipped to do it. Other social researchers tend to believe that interpretivism is a specialized niche with little relevance to their research practice. What has been lost is the awareness that interpretation is an explanatory logic and form of inquiry that is at the root of all social science research, including quantitative research. This book enables readers to better understand, reflect on, and improve the experience of doing interpretive research. The book’s unique angle is the centrality of experience in learning and teaching interpretive research. It pays special attention to the role of emotions in the learning process, and the way that negative emotions, such as doubt and anxiety, can impart learning. The book provides a novel approach to methods teaching by offering a set of heuristics and open-ended strategies of inquiry and discovery for improving the practice of research. It shows how emotions can be leveraged in the learning process. The book will benefit social researchers who are doing, or are interested in, interpretivism to turn their research into an enjoyable, productive, and imaginative experience.

One strand of criticism of the ‘cultural turn’ in geography and other disciplines is that it produces research that is of limited ‘usefulness’ and has disarmed academics. This paper argues that elements of the critique of the cultural turn are overstated. It then argues that criticism of the ‘usefulness’ of cultural research rests on simplistic assumptions concerning the relationship of the social research to users such as policy makers. The problem is depicted as largely related to the nature of the information flowing to ‘users’. Such assumptions are critiqued through discussing the concept of ‘use’, influences on the use of research, and models of relationships between ‘users’ and researchers. Finally, the paper argues that a key issue in the relationship between policy making and social sciences is the users’ expectations. A recent example from research in natural resource management (NRM) policy making shows that ‘users’ of social science research can have a questionable foundation from which to assess social research. This example also points to clear roles for cultural research in NRM. The problem of connecting with policy makers is multidimensional. It is one for social researchers as a whole and it includes the norms and practices regarding nature and natural resources of potential ‘users’.

Introduction Part One: Issues in social research Perspectives on social scientific research Social theory and social research Values and ethics in the research process Part Two: Methods of social research Official statistics: Topics and resources Social surveys: Design to analysis Interviewing: Methods and process Participant observation: Perspectives and practice Documentary research: Excavations and evidence Case study research Comparative research: Potential and problems Part Three: Research in practice Reflections on Research in practice

Contributions of M. N. Srinivas to the understanding of changing nature of caste, village and politics have been well documented and continue to be under focus several decades after his scholarly formulations on them. This article is an attempt to focus on the nature of changes that are occurring in the realm of his contributions to the field of social science research methods—especially to the tradition of intensive field research whether in rural or urban locales. Not only have the field sites’ amenability to research has undergone a major change, the institutional context in which social research is practised have radically transformed. This article is an attempt to track some such changes in both the contexts and to reflect upon the emerging challenges to social science research in general and the idea of ‘field’ research in particular. One of the questions it raises and attempts to answer is: Is sociology and social anthropology becoming more dependent on secondary data, and the source becoming a web than the field?

In this survey the author links the social science research on sports with the historical development of sports in Finland and with society at large. Apart from the basic task of discerning truths and facts based on the empirical reality of sports, social science research also has a critical and evaluative role in exploring problematic or neglected issues in physical culture. Is also challenges traditional wisdom in the mainstreams of sports, and contributes to a new policy and the planning of sports for man in society.

This special issue represents a rather erratic dialogue within sociological research. While social inequality has been an enduring focus in social research, it has commanded rather limited intellectual and political resources within the specialty that has come to be known as environmental sociology. Despite a history of research since the early 1970s in this arena, it is curious how little we have analytically integrated environmentalism and equity. This volume outlines some of the social consequences of this disattention by both social researchers and environmental movement adherents. From my own perspective, I find this disattention a puzzling paradox. It is comforting for us to attribute this strategic disregard as simply due to the ignorance of social scientists, or due to competition for attention posed by other changing social or ecological factors. These might include the rise of an unprecedently large voluntary social movement labeled as "the environmental movement", or the scientific rediscovery of pollution and its health effects in the 1960s and 1970s. Perhaps others currently working within environmental sociology itself, or in other social scientific research, can accept this interpretation. But I cannot, for the simple reason that I have been calling for attentiveness to this issue for over two decades. In 1973, I concluded my very first published paper, Politics, participation and pollution: The 'environmental movement,' with the following statement (Schnaiberg, 1973: 623): Therefore, any proposal for environmental action must clearly incorporate the elements of social justice, and there must be a commitment of the middle-class proponents to explicitly build in such considerations. This is a political necessity, as well as a moral prerequisite, since the coalition of rich and poor is sufficient to

Addressing the policy challenges and opportunities of “Big data”

Interpretivism is a research paradigm that is associated with qualitative research.A tradition of Interpretivism can be linked to the ethnographies of non-western peoples in the 19th century and continues to be a popular way to explain human behaviour and social phenomenon today.The interpretivist view invites the researcher to investigate meaning behind the understanding of human behaviour, interactions and society.This involves the researcher attempting to develop an in-depth subjective understanding of people's lives.The difference between the natural sciences and social sciences is that the natural sciences requires the researcher to observe and interpret natural processes but the social sciences requires the researcher to observe a phenomenon and understand the interpretations that individuals have already made.Social work research aims to explain human behavior and social phenomenon and not to focus on isolating and objectively measuring variables or on developing generalizations.Social work research requires a focus on gaining an empathic understanding of an individual's feelings and the meaning that they give to everyday life with the goal to gaining greater understanding of the individual's behavior.For this reason, interpretive research is well suited to social work research.In this introductory paper, the authors present the validity of interpretive research and the value of using it in social work research.

Systematic reviews and meta-analyses are used to combine results across studies to determine an overall effect. Meta-analysis is especially useful for combining evidence to inform social policy, but meta-analyses of applied social science research may encounter practical issues arising from the nature of the research domain. The current paper identifies potential resolutions to four issues that may be encountered in systematic reviews and meta-analyses in social research. The four issues are: scoping and targeting research questions appropriate for meta-analysis; selecting eligibility criteria where primary studies vary in research design and choice of outcome measures; dealing with inconsistent reporting in primary studies; and identifying sources of heterogeneity with multiple confounded moderators. The paper presents an overview of each issue with a review of potential resolutions, identified from similar issues encountered in meta-analysis in medical and biological sciences. The discussion aims to share and improve methodology in systematic reviews and meta-analysis by promoting cross-disciplinary communication, that is, to encourage ‘viewing through different lenses’.

This article explores the proposition that the appropriate use of suitable forms of graphic communication can improve the formulation and presentation of hypotheses in quantitative social science research. The creative nature of scientific diagrams is discussed and the technological advances in computer graphic media are seen as part of a ‘visual revolution’ which is markedly changing not only the way we see things but also the way we think and do things today. Brief historical views on the use of hypotheses and diagrammatic languages in science are given. The restricted use of graphic communication tools in social research academic documents is discussed and the importance of using well-designed data graphics in the production and transmission of scientific knowledge is highlighted. Hypotheses are conceptualised and their importance within social research is emphasised. A methodological approach for formulating hypotheses graphically is proposed based on the use of three types of language: notation, statement (ordinary language) and diagram. Some criteria are suggested for the selection of diagram type dependent on the related variables. Several examples are given covering the different models proposed.