Data Analysis Sociology
Data analysis in sociology involves the systematic examination of social data to identify patterns, trends, and relationships within a given population or social group. It encompasses various quantitative and qualitative methods to interpret and make sense of social phenomena, such as surveys, interviews, and statistical analysis. The goal is to gain insights into social behavior and structures.
6 Key excerpts on "Data Analysis Sociology"
eBook - ePub- Dominique Moyse Steinberg(Author)
- 2015(Publication Date)
- Routledge(Publisher)
...The most generic reference to these types of analysis is content analysis. Quantitative data analysis is usually associated with large-scale descriptive, experimental, or correlational studies and consists of understanding numbers that describe your findings or those that tell you about the capacity to generalize. To carry out qualitative analysis is to expect a lot of work during and after implementation. To carry out quantitative analysis is to spend a lot of time in designing or choosing an appropriate standardized instrument, which means front-heavy labor. Analysis, description, and interpretation of qualitative data all revolve around the purpose of a study, which provides the context for making meaning, usually to the end of theory building. To make meaning of quantitative data is to use numbers to summarize, organize, describe, and interpret your findings. Both qualitative and quantitative data have value in social work research, and, more often than not, studies seek both types of data in order to capture as full a “picture” of and tell as comprehensive a “story” as possible for the study sample and, in some cases, for the population from which it was drawn. Exercise Get directions So by now your study should be taking shape...
- Bruce B. Frey(Author)
- 2018(Publication Date)
- SAGE Publications, Inc(Publisher)
...Data analysis should begin as soon as any data are collected and should be continued as long as any significant questions remain about the meaning and implications of the data. Although the relative emphasis on the different aspects of the research process varies over time, they are not chronologically separated components of a linear series. History Although the term qualitative research is a more recent development, its actual practice has a long history, extending back at least to the 19th-century work of anthropologists and the study of social problems by Charles Booth, Jane Addams, and others and to later community studies such as Middletown and Yankee City. Despite this, the analysis of qualitative data has, until relatively recently, received little theoretical attention. This is in striking contrast to quantitative research, which has a well-developed theory, statistics, which informs quantitative analysis. The first widely recognized, named, and systemically developed method for analyzing qualitative data was analytic induction (AI). It was created by the sociologist Florian Znaniecki in the 1930s, during his research with W. I. Thomas for their classic work The Polish Peasant in Europe and America, and was further developed by Alfred Lindesmith in his research on opiate addiction in the 1940s. In contrast to quantitative research, which typically collects and analyzes data in order to test previously developed theories, AI proceeds inductively to generate categories, concepts, and theories from the data. These inductively developed theories specify the necessary preconditions for a type of case (e.g., of people who embezzle money from a firm to deal with unexpected personal financial problems); the theory is tested by seeking negative instances, and revising the theory, or limiting its scope, until no negative cases are found. The goal of AI was to develop explanatory theories about the phenomena studied...
eBook - ePubField Research
A Sourcebook and Field Manual
- Robert G. Burgess(Author)
- 2003(Publication Date)
- Routledge(Publisher)
...Lofland (1974a) has outlined a variety of circumstances in which no sociological analysis takes place. First, there are reports based on a ‘moral style’, where the researcher shows empathy and sympathy for the group studied. Secondly, there are reports based on ‘the “then they do this” style’, which present a detailed chronological record of what occurred. Finally, he identifies a ‘vacillating style’, in which sociological concepts are haphazardly applied to the data collected. In an earlier account Lofland argues that the process of data analysis involves an appreciation of the way in which participants order and analyse their world. He maintains that ‘the qualitative analyst seeks to provide an explicit rendering of the structure, order, and patterns found among a set of participants’ (Lofland, 1971, p. 7). The field researcher, therefore, needs to describe and explain that which has been observed and to indicate further areas that require detailed study. The material obtained from a single setting may be used in a variety of analyses, as data can be selected and combined to illustrate numerous social structures and social processes. Indeed, Schatzman and Strauss (1973) have indicated that data can be used to provide a straight description that links into classes of accepted theory, an analytic description whereby an organisational scheme may be developed from the data and substantive theory which is present in any descripti ve account. Some of the basic issues involved in the analysis of field data are outlined in the paper by Becker and Geer (Chapter 32), who examine the way in which sequential analysis is done in field research and the way in which conclusions are reported. While they indicate that there are no rules for doing data analysis, there have been several suggestions about how data can be examined. In a classic paper of qualitative data analysis, Barton and Lazarsfeld (1955) indicate ways in which researchers can begin to analyse data...
eBook - ePubGetting Started in Your Educational Research
Design, Data Production and Analysis
- Clive Opie, Desma Brown(Authors)
- 2019(Publication Date)
- SAGE Publications Ltd(Publisher)
...The task of analysis involves you having to make decisions about how the data might be categorised or organised, in order to cast some understanding about the research area under investigation. You may find the following approaches to the analysis of qualitative data useful, but by gaining some understanding of their function you will be able to make judgements about how to analyse your own data. We begin the chapter with a discussion by Hannah Scaife of the process of analysing and interpreting textual data in Content Analysis, followed by a consideration of the exploration of themes within data through Thematic Analysis, the tool which she used in the completion of her Master’s degree in psychology (Scaife, 2016). Then Desma Brown examines what is understood by the term Discourse Analysis, exploring the research for her own doctorate (Brown, 2016) as an example of its usage. This is then followed by an insight into Critical Discourse Analysis and Conversation Analysis and how they might be useful should you decide to examine discourse as part of your study. Content Analysis What is Content Analysis and how is it used? Content Analysis is a method for analysing and interpreting textual data, using coding and categorisation to identify the presence and frequency of trends and patterns (Ahuvia, 2001; Bauer, 2000; Gbrich, 2007; Mayring, 2000; Pope et al., 2006). Textual data can be viewed according to one or more of five distinctions (Krippendorff, 2013): Physical – mechanisms used to structure a physical medium, e.g. books, newspapers, photographs, interview transcripts, letters. Syntactical – ‘naturally’ occurring blocks of data, e.g. chapters in a book, articles in a magazine, scenes in a film, individual words within a transcript. Categorical – data relevant to specific classifications or categories, e.g...
eBook - ePub- (Author)
- 2011(Publication Date)
- Jossey-Bass(Publisher)
...6 DATA ANALYSIS R. M. Cooper and Mack C. Shelley, II After data have been collected, they need to be analyzed. In quantitative studies, this usually means the use of statistical techniques. Which techniques are most appropriate for analyzing data for an audience of nonstatisticians while maintaining the integrity of the study? In qualitative assessments, how does one make sense of the data? How does one ensure rigor in data analysis? These issues and others will be considered in this chapter. Examples are provided throughout the chapter to illustrate how data can be used to answer questions related to student affairs practice. The Quantitative Path The primary example employed throughout this discussion is undergraduate student retention through the use of learning communities, which use linked courses, residential housing proximity, and heightened contact between students and faculty to enhance student satisfaction, achievement, and, ultimately, retention and graduation. What Do We Do with the Data? To pursue your research on a student affairs issue, you have collected quantitative data from a survey, institutional records, or some other sources. What do you do next? With any luck, your data will have come to you already in machine-readable form—which means it would be in the form of an Excel spreadsheet or a dataset in a statistical package such as SPSS (Statistical Package for the Social Sciences) or SAS (the Statistical Analysis System). However, if the data have not yet been entered into a computer software package (for example, Excel, SPSS), that would be the next logical thing to do. Unless you are really good at data entry yourself, this is an opportunity to hire students or use existing staff to do that for you. Whether the data came to you ready-made for computerized analysis or had to be entered into the computer directly from their raw format, you will have to make sure the data have been cleaned and coded properly...
eBook - ePubTeachers Investigate Their Work
An Introduction to Action Research across the Professions
- Allan Feldman, Herbert Altrichter, Peter Posch, Bridget Somekh(Authors)
- 2018(Publication Date)
- Routledge(Publisher)
...Through analysis, data and experiences are restructured and practical theories elaborated. In this sense, analysis, theorizing, and restructuring are the same. But, can this be called research? Is the teacher, nurse, social worker, or other professional practitioner who does the analysis a researcher ? A sharp line cannot be drawn between analysis in research and everyday analysis. The more systematically an analysis is carried out (based on theoretical and methodological knowledge), the more critical the process (tested against conflicting data and interpretations), and the more communicative it is (the process and the results made public), the more it deserves to be called research. The results of the process of analysis are preliminary and hypothetical, and require further testing through reflection and examination in practice. Results are the interpretations, practical theories, and conclusions that we draw from our data. We use data analysis methods both to construct and critique our findings. Part of analysis is the process of breaking a complex topic into smaller parts in order to gain a better understanding of it. Another important part is synthesis, which is “the act of combining different ideas or things to make a whole that is new and different from the items considered separately” (Cambridge English Dictionary, 2016). We collect and analyze data so that we can get a better handle on a complex system, like teaching and learning in schools. However, as we go about doing action research, we want something new, better, or improved to come out of the process. As part of this we need to engage in synthesis. In an organic and flowing activity like doing action research on professional practice, the breaking down of complex topics into smaller parts and the construction of new concepts occur together as we develop and critique our findings. The remainder of this chapter is divided into two large sections...
