Social Research: Issues, Methods and Process.

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. …

Contents: Introduction Part I Perspectives on Social Work Research: Cutting edge issues in social work research Is social work distinctive? Ways of knowing in social work Rereading The Jack Roller: hidden histories in sociology and social work William J. Reid: an appreciation. Part II Evaluation: Social work and the human services Evidence from qualitative evaluation Seeing the trees for the wood: the politics of evaluating in practice Evaluation for a learning organization? Part III Qualitative Research: Qualitative social work: a room with a view Qualitative research and outcomes in health, social work and education Ethics and the practice of qualitative research Constructing causal accounts in social work The consequences of qualitative social work research Unbroken voices: children, young people and qualitative methods. Part IV Research, Theory and Practice: The quality of mercy: the management of quality in the personal social services Game plans, buzzes and sheer luck: doing well in social work Practitioner evaluation at work Evaluating in practice Culture and the indigenisation of quality in Third World social research. Part V Service Users and Research: Consumer opinion and social policy: a research review Consumer evaluations of the personal social services Engaging the user: participation, empowerment and the rhetoric of quality Just inquiry? Research and evaluation for service users Users and practitioners' research Indexes.

Social informatics: Beyond emergence

Over the years the research endeavours in social sciences have been directed towards producing profound discoveries about humankind and the society they live in. Now-a-days qualitative research has been flourishing in many fields of the social sciences. Rapid social change and the resulting diversification of life scenarios are increasingly confronting social researchers with new social contexts and perspectives. There is a specific relevance for qualitative research to the study of social contexts and life situations. This has lead to the development of several social theories and social research methods which have still continued to develop in order to understand the diverse social problems. The challenge for social scientists and researchers worldwide is to continue in the search for a unified consensus on what methodology is to be adopted for research for meeting the emerging diverse needs of humankind and the society. This thought has led to the proliferation of pluralism or multi-method research in which the researcher synthesizes multiple research methodologies in order to study social problems in a holistic way. This concept of pluralism in research has to a greater extend subverted methodological tribalism which had existed till the recent past. This paper focuses on the philosophical assumptions as guidelines to research paradigms have influenced the development of social research theory and social research methods towards pluralism. This paper intends to explore pluralism in qualitative Social Science researches as it may be applied methodologically and philosophically. In this context, the paper heeds a thought on refocusing pluralistic qualitative research in terms of its needs and demands based on the level and depth of the study undertaken. Key Terms: Qualitative research, methodological pluralism, research paradigms, philosophical assumptions

For decades, traditional correlation analysis and regression models have been used in social science research. However, the development of machine learning algorithms makes it possible to apply machine learning techniques for social science research and social issues, which may outperform standard regression methods in some cases. Under the circumstances, this article proposes a methodological workflow for data analysis by machine learning techniques that have the possibility to be widely applied in social issues. Specifically, the workflow tries to uncover the natural mechanisms behind the social issues through a data-driven perspective from feature selection to model building. The advantage of data-driven techniques in feature selection is that the workflow can be built without so much restriction of related knowledge and theory in social science. The advantage of using machine learning techniques in modelling is to uncover non-linear and complex relationships behind social issues. The main purpose of our methodological workflow is to find important fields relevant to the target and provide appropriate predictions. However, to explain the result still needs theory and knowledge from social science. In this paper, we trained a methodological workflow with left-behind children as the social issue case, and all steps and full results are included.

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.

Addressing the policy challenges and opportunities of “Big data”

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’.

This statement on social work practice research highlights the contributions of scholars, practitioners, and conference participants in the Fourth International Conference on Practice Research (ICPR) in 2017, hosted by The Hong Kong Polytechnic University in May 2017. It focuses on the contexts and challenges of carrying out practice research in the Far East and beyond as well as raises pertinent questions about the development of practice research. It begins with a brief description of the context of social work practice research in the Far East. The second part explores the organizational and community contexts and challenges of practice research with special attention to the perspectives of practitioners. It concludes with reviewing some of the continuing challenges that will guide the program planning for the Fifth ICPR in 2020 in Melbourne, Australia, located at the crossroads between East and West.

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

The purpose of this article is to present some of the particularities, scope and limits of privacy, confidentiality and anonymity in the field of social sciences research ethics. The article is organized in three sections. Firstly, I define and distinguish the concepts of privacy and confidentiality, and their unique characteristics in social research. Secondly, I analyze some current debates and criticisms of anonymity. Finally, I examine some problems about the protection of information and the challenges involving new social research environments.

Greetings to readers as we start a new year for Qualitative Social Work. Last year saw an increase in papers to the Journal and as our readership continues to grow, the value of qualitative research for social work is confirmed. However, there is no room for complacency and if we are to ensure that the Journal continues to thrive, it is important to remain abreast of changes in the world of qualitative research and social research more generally. So, this brings me to consider the issue of innovation in this new year editorial. For readers familiar with writing or reviewing grant applications, claims by academics that their work is novel or innovative are all very familiar. As Taylor and Coffey described in 2009, pressures stemming from funding councils and publishers encourage academics to state and indeed over-state the cutting edge nature of their work. Writing in 2015, it seems that claims to innovation are now endemic and if we examine the mission of the UK’s major social science research funding council – the Economic and Social Research Council (ESRC) – the word innovation is never far from the Council’s funding calls, strategy statements or impact case studies. In the Council’s research funding guide, applicants must pay attention to impact, innovation and interdisciplinarity. The Council’s conceptualisation of innovation is broad, to include: ‘innovative or even untested methods’, through to research which challenges ‘existing paradigms in respect of research ethics’ (ESRC, 2015). So what exactly is meant by innovation and to what extent is new invention better than old? In response to the latter question, critics differ in their opinion. For example, there are those who consider that innovative methods simply represent the latest fads, leaving unresolved the long-standing challenges in qualitative research (Travers, 2009). The problem of fad is that where we fetishise the novel we may inadvertently recast tried and tested methodologies as dated, or at worse obsolete. Traditional ethnography and conventional methods of qualitative interviewing have been so vital to our understanding of the social world, we must resist casting such methods as old hat (Wiles et al., 2011). Critics have also argued that claims to innovation are frequently overstated and can amount to little more than Qualitative Social Work 2016, Vol. 15(1) 3–10 ! The Author(s) 2016 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/1473325015619822 qsw.sagepub.com