case study method in sociology pdf

• Corpus ID: 168373848

The Case Study as a Research Method

• Susan K. Soy
• Published 2015
• Sociology, Education

213 Citations

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Case Study: Types, Advantages And Disadvantages

  Case Study: Types, Advantages And Disadvantages 

Case study is both method and tool for research. Case study is the intensive study of a phenomenon, but it gives subjective information rather than objective. It gives detailed knowledge about the phenomena and is not able to generalize beyond the knowledge.

Case studies aim to analyze specific issues within the boundaries of a specific environment, situation or organization. According to its design, case study research method can be divided into three categories: explanatory, descriptive and exploratory.

Explanatory case studies aim to answer ‘how’ or ‘why’ questions with little control on behalf of the researcher over occurrence of events. This type of case study focuses on phenomena within the contexts of real-life situations.

Descriptive case studies aim to analyze the sequence of interpersonal events after a certain amount of time has passed. Case studies belonging to this category usually describe culture or sub-culture, and they attempt to discover the key phenomena.

Exploratory case studies aim to find answers to the questions of ‘what’ or ‘who’. Exploratory case study data collection method is often accompanied by additional data collection method(s) such as interviews, questionnaires, experiments etc.

DEFINITION OF CASE STUDY

The case study or case history method is not a newer thing, but it is a linear descendent of very ancient methods of sociological description and generalization namely, the ‘parable’, the ‘allegory’, the ‘story’ and the ‘novel’.

According to P.V. Young . “A fairly exhaustive study of a person or group is called a life of case history.”

Thus, the case study is more intensive in nature; the field of study is comparatively limited but has more depth in it.

TYPES OF CASE STUDY

Six types of case studies are conducted which are as follows:

Community Studies: The community study is a careful description and analysis of a group of people living together in a particular geographic location in a corporative way. The community study deals with such elements of the community as location, appearance, prevailing economic activity, climate and natural sources, historical development, how the people live, the social structure, goals and life values, an evaluation of the social institutions within the community that meet the human needs etc. Such studies are case studies, with the community serving as the case under investigation.

Casual Comparative Studies: Another type of study seeks to find the answers to the problems through the analysis of casual relationships. What factors seem to be associated with certain occurrences, conditions or types of behaviour? By the methodology of descriptive research, the relative importance of these factors may be investigated.

Activity Analysis: The analysis of the activities or processes that an individual is called upon to perform is important, both in industry and in various types of social agencies. This process of analysis is appropriate in any field of work and at all levels of responsibility. In social system, the roles of superintendent, the principal, the teacher and the custodian have been carefully analyzed to discover what these individuals do and need to be able to do.

Content or Document Analysis: Content analysis, sometimes known as document analysis. Deals with the systematic examination of current records or documents as sources of data. In documentary analysis, the following may be used as sources of data: official records and reports, printed forms, text-books, reference books, letters, autobiographies diaries, pictures, films and cartoons etc . But in using documentary sources, one must bear in mind the fact that data appearing in print is not necessarily trustworthy. This content or document analysis should serve a useful purpose in research, adding important knowledge to a field to study or yielding information that is helpful in evaluating and improving social or educational practices.

A Follow-up Study: A follow-up study investigates individuals who have left an institution after having completed programme, a treatment or a course of study, to know what has been the impact of the institutions and its programme upon them. By examining their status or seeking their opinions, one may get some idea of the adequacy or inadequacy of the institutes programme. Studies of this type enable an institution to evaluate various aspects of its programme in the light of actual results.

Trend Studies: The trend or predictive study is an interesting application of the descriptive method. In essence, it is based upon a longitudinal consideration of recorded data, indicating what has been happening in the past, what does the present situation reveal and on the basis of these data, what will be likely to happen in the future.

Whatever type of case study is to conduct, it’s important to first identify the purpose, goals, and approach for conducting methodologically sound research.

ADVANTAGES OF CASE STUDY

The main points of advantages of case study are given below:

Formation of valid hypothesis: Case study helps in formulating valid hypothesis. Once the various cases are extensively studied and analyze, the researcher can deduce various generalizations, which may be developed into useful hypotheses. It is admitted by all that the study of relevant literature and case study form the only potent sources of hypothesis.

  Useful in framing questionnaires and schedules: Case study is of great help in framing questionnaires, schedules or other forms. When a questionnaire is prepared after thorough case study the peculiarities of the group as well as individual units, become known also the type of response likely to be available, liking and aversions of the people. This helps in getting prompt response.

Sampling: Case study is of help in the stratification of the sample. By studying the individual units the researcher can put them in definite classes or types and thereby facilitate the perfect stratification of the sample.

Location of deviant cases: The case study makes it possible to locate deviant cases. There exists a general tendency to ignore them, but for scientific analysis, they are very important. The analysis of such cases is of valuable help in clarifying the theory itself.

Study of process: In cases where the problem under study constitutes a process and not one incident e.g. courtship process, clique formation etc., case study is the appropriate method as the case data is essential for valid study of such problems.

Enlarges experience: The range of personal experience of the researcher is enlarged by the case study on the other hand in statistical methods a narrow range of topics is selected, and the researcher’s knowledge is restricted to the particular aspect only.

Qualitative analysis in actual situation: Case study enables the establishment of the significance of the recorded data when the individual is alive and later on within the life of the classes of individuals. The researcher has the opportunity to come into contact with different classes of people and he is in a position to watch their life and hear their experiences. This provides him with an opportunity to acquire experiences of such life situations which he is never expected to lead.

This discussion highlights the advantages of the case data in social research. Social scientists developed the techniques to make it more perfect and remove the chances of bias.

LIMITATIONS/DISADVANTAGES OF CASE STUDY METHOD

Subjective bias: Research subjectivity in collecting data for supporting or refuting a particular explanation, personal view of investigation influences the findings and conclusion of the study.

Problem of objectivity: Due to excessive association with the social unit under investigation the researcher may develop self-justificatory data which are far from being factual.

Difficulty in comparison: Because of wide variations among human beings in terms of their response and behaviour, attitudes and values, social setting and circumstances, etc., the researcher actually finds it difficult to trace out two social units which are identical in all respects. This hinders proper comparison of cases.

A time, energy and money consuming method: The preparation of a case history involves a lot of time and expenditure of human energy, therefore, there is every possibility that most of the cases may get stray. Due to such difficulties, only a few researchers can afford to case study method.

Time span: Long time span may be another factor that is likely to distort the information provided by the social unit to the researcher.

Unreliable source material: The two major sources of case study are: Personal documents and life history. But in both these cases, the records or the own experience of the social units may not present a true picture. On the contrary, the social unit may try to suppress his unpleasant facts or add colour to them. As a result, the conclusions drawn do not give a true picture and dependable findings.

Scope for wrong conclusions: The case study is laden with inaccurate observation, wrong inferences, faulty reporting, memory failure, repression or omission of unpleasant facts in an unconscious manner, dramatization of facts, more imaginary description, and difficulty in choosing a case typical of the group. All these problems provide the researcher with every possibility of drawing wrong conclusions and errors.

Case studies are complex because they generally involve multiple sources of data, may include multiple cases within a study and produce large amounts of data for analysis. Researchers from many disciplines use the case study method to build upon theory, to produce new theory, to dispute or challenge theory, to explain a situation, to provide a basis to apply solutions to situations, to explore, or to describe an object or phenomenon. The advantages of the case study method are its applicability to real-life, contemporary, human situations and its public accessibility through written reports. Case study results relate directly to the common readers everyday experience and facilitate an understanding of complex real-life situations.

__________________________________________________________________________

Research Methodology Methods and Techniques~C. R. Kothari (p.113) - Link

Fundamental of Research Methodology and Statistics~Yogesh Kumar Singh (Chapter–10: Case Study Method p. 147) - Link

Social Research Methods: Qualitative and Quantitative Approaches~W. Lawrence Neuman (p.42) - Link

The Basics of Social Research~Earl Babbie (p.280) - Link

Social Science Research Principles, Methods, and Practices~Anol Bhattacherjee (93) - Link

PREPARING A CASE STUDY: A Guide for Designing and Conducting a Case Study for Evaluation Input - Link

A Case in Case Study Methodology - Link

Case Study Method - Link1 & Link 2

Unit-4 Case Study - Link

Case study as a research method - Link

Case_Study~Tanya Sammut-Bonnici and John McGee - Link

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A DEM upscaling method with integrating valley lines based on HASM

• Research Article
• Published: 04 July 2024

Cite this article

• Mingwei Zhao 1 , 5 , 6 , 7 ,
• Xiaoxiao Ju 2 ,
• Na Zhao 3 , 4 ,
• Chun Wang 1 , 5 , 6 , 7 ,
• Yan Xu 1 , 5 , 6 , 7 ,
• Xiaoran Wu 3 , 4 &
• Weitao Li 1 , 5 , 6 , 7  

A new digital elevation model (DEM) upscaling method based on high accuracy surface modeling (HASM) is proposed by combining the elevation information of DEM and the valley lines extracted from DEM with different flow accumulation thresholds. The proposed method has several advantages over traditional DEM upscaling methods. First, the HASM ensures the smoothness of the upscaled DEM. Secondly, several DEMs with different topographic details can be obtained using the same DEM grid size by incorporating the valley lines with different flow accumulation thresholds. The Jiuyuangou watershed in China’s Loess Plateau was used as a case study. A DEM with a grid size of 5 m obtained from the local surveying and mapping department was used to verify the proposed DEM upscaling method. We established the surface complexity index to describe the complexity of the topographic surface and quantified the differences in the topographic features obtained from different upscaling results. The results show that topography becomes more generalized as grid size and flow accumulation threshold increase. At a large DEM grid size, an increase in the flow accumulation threshold increases the difference in elevation values in different grids, increasing the surface complexity index. This study provides a new DEM upscaling method suitable for quantifying topography.

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Aguilar F J, Aguera F, Aguilar M A, Carvajal F (2005). Effects of terrain morphology, sampling density, and interpolation methods on grid DEM accuracy. Photogramm Eng Remote Sensing, 71(7): 805–816

Article   Google Scholar  

Ai T, Li J (2010). A DEM generalization by minor valley branch detection and grid filling. ISPRS J Photogramm Remote Sens, 65(2): 198–207

Ai T, Liu Y, Huang Y (2007). The hierarchical watershed partitioning and generalization of river network. Acta Geodetica Cartograph Sin, 36(2): 231–236,243

Google Scholar  

Burrough P A, McDonnell R A, Lloyd C D (1988). Principles of Geographical Information Systems. New York: Oxford University Press

Chen C F, Yue T X, Li Y Y (2012). A high speed method of SMTS. Comput Geosci, 41: 64–71

Chen C, Li Y (2013). An orthogonal least-square-based method for DEM generalization. Int J Geogr Inf Sci, 27(1): 154–167

Chen Y, Ma T, Chen X, Chen Z, Yang C, Lin C, Shan L (2016). A new DEM generalization method based on watershed and tree structure. PLoS One, 11(8): e0159798

Chen Z, Ma X, Yu W, Wu L (2020). An integrated graph Laplacian downsample (IGLD)-based method for DEM generalization. Earth Sci Inform, 13(4): 973–987

Fisher P F (1991). Firest experiments in viewshed uncertainty-The accuracy of the viewshed area. Photogramm Eng Remote Sensing, 57(10): 1321–1327

Horritt M S, Bates P D (2001). Effects of spatial resolution on a raster based model of flood flow. J Hydrol (Amst), 253(1–4): 239–249

Jenson S K, Domingue J O (1988). Extracting topographic structure from digital elevation data for geographic information-system analysis. Photogramm Eng Remote Sensing, 54(11): 1593–1600

Kawabata D, Bandibas J (2009). Landslide susceptibility mapping using geological data, a DEM from ASTER images and an Artificial Neural Network (ANN). Geomorphology, 113(1–2): 97–109

Li J H, Chen W J (2005). A rule-based method for mapping Canada’s wetlands using optical, radar and DEM data. Int J Remote Sens, 26(22): 5051–5069

Lin W T, Chou W C, Lin C Y, Huang P H, Tsai J S (2006). Automated suitable drainage network extraction from digital elevation models in Taiwan’s upstream watersheds. Hydrol Processes, 20(2): 289–306

Liu X (2008). Airborne LiDAR for DEM generation: some critical issues. Prog Phys Geogr, 32(1): 31–49

Ma T, Chen Y, Hua Y, Chen Z, Chen X, Lin C, Yang C (2017). DEM generalization with profile simplification in four directions. Earth Sci Inform, 10(1): 29–39

Murphy P N C, Ogilvie J, Meng F R, Arp P (2008). Stream network modelling using lidar and photogrammetric digital elevation models: a comparison and field verification. Hydrol Processes, 22(12): 1747–1754

O’Callaghan J F, Mark D M (1984). The extraction of drainage networks from digital elevation data. Comput Vis Graph Image Process, 28(3): 323–344

Raposo P (2020). Variable DEM generalization using local entropy for terrain representation through scale. Intern J Cartograph, 6(1): 99–120

Schoorl J M, Sonneveld M P W, Veldkamp A (2000). Three-dimensional landscape process modelling: the effect of DEM resolution. Earth Surf Process Landf, 25(9): 1025–1034

Tang G (2014). Progress of DEM and digital terrain analysis in China. Acta Geogr Sin, 69(9): 1305–1325

Tarboton D G (1997). A new method for the determination of flow directions and upslope areas in grid digital elevation models. Water Resour Res, 33(2): 309–319

Tarboton D G, Bras R L, Rodriguez-Iturbe I (1991). On the extraction of channel networks from digital elevation data. Hydrol Processes, 5(1): 81–100

Tribe A (1992). Automated recognition of valley lines and drainage networks from grid digital electation models–a review and a new method. J Hydrol (Amst), 139(1–4): 263–293

Turcotte R, Fortin J P, Rousseau A N, Massicotte S, Villeneuve J P (2001). Determination of the drainage structure of a watershed using a digital elevation model and a digital river and lake network. J Hydrol (Amst), 240(3–4): 225–242

Vaze J, Teng J, Spencer G (2010). Impact of DEM accuracy and resolution on topographic indices. Environ Model Softw, 25(10): 1086–1098

Weibel R (1992). Models and experiments for adaptive computer-assisted terrain generalization. Cartogr Geogr Inf Syst, 19(3): 133–153

Wolock D M, McCabe G J (2000). Differences in topographic characteristics computed from 100- and 1000-m resolution digital elevation model data. Hydrol Processes, 14(6): 987–1002

Wu Q, Chen Y, Wilson J P, Liu X, Li H (2019). An effective parallelization algorithm for DEM generalization based on CUDA. Environ Model Softw, 114: 64–74

Xiong L Y, Li S J, Hu G H, Wang K, Chen M, Zhu A X, Tang G A (2023). Past rainfall-driven erosion on the Chinese loess plateau inferred from archaeological evidence from Wucheng City, Shanxi. Commun Earth Environ, 4(1): 4

Xiong L, Li S, Tang G, Strobl J (2022). Geomorphometry and terrain analysis: data, methods, platforms and applications. Earth Sci Rev, 233: 104191

Xiong L, Tang G, Yang X, Li F (2021). Geomorphology-oriented digital terrain analysis: progress and perspectives. J Geogr Sci, 31(3): 456–476

Yue T X (2011). Surface Modeling: High Accuracy and High Speed Methods. Calabasas: CRC Press

Book   Google Scholar  

Yue T X, Chen C F, Li B L (2010). An adaptive method of high accuracy surface modeling and its application to simulating elevation surface. Trans GIS, 14(5): 615–630

Yue T X, Du Z P, Song D J, Gong Y (2007). A new method of surface modeling and its application to DEM construction. Geomorphology, 91(1–2): 161–172

Yue T X, Liu Y, Zhao M W, Du Z P, Zhao N (2016). A fundamental theorem of Earth’s surface modelling. Environ Earth Sci, 75(9): 751

Yue T X, Zhao N, Liu Y, Wang Y, Zhang B, Du Z P, Fan Z, Shi W, Chen C, Zhao M, Song D, Wang S, Song Y, Yan C, Li Q, Sun X, Zhang L, Tian Y, Wang W, Wang Y, Ma S, Huang H, Lu Y, Wang Q, Wang C, Wang Y, Lu M, Zhou W, Liu Y, Yin X, Wang Z, Bao Z, Zhao M, Zhao Y, Jiao Y, Naseer U, Fan B, Li S, Yang Y, Wilson J P (2020). A fundamental theorem for eco-environmental surface modelling and its applications. Sci China Earth Sci, 63(8): 1092–1112

Zhang R, Bian S, Li H (2021). RSPCN: super-resolution of digital elevation model based on recursive sub-pixel convolutional neural networks. ISPRS Int J Geoinf, 10(8): 501

Zhao N, Yue T X, Zhao M W, Du Z P, Fan Z M, Chen C F (2014). Sensitivity studies of a high accuracy surface modeling method. Sci China Earth Sci, 57(10): 2386–2396

Zhou A, Chen Y, Wilson J P, Su H, Xiong Z, Cheng Q (2021). An enhanced double-filter deep residual neural network for generating super resolution DEMs. Remote Sens (Basel), 13(16): 3089

Zhou Q, Chen Y (2011). Generalization of DEM for terrain analysis using a compound method. ISPRS J Photogramm Remote Sens, 66(1): 38–45

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Acknowledgments

We are thankful for all of the helpful comments provided by the reviewers. This research was supported by Anhui Province Universities Outstanding Talented Person Support Project (No. gxyq2022097); Major Project of Natural Science Research of Anhui Provincial Department of Education (Nos. 2022AH040150, KJ2021ZD0130, KJ2021ZD0131); the National Natural Science Foundation of China (Grant No. 42071374); the guiding plan project of Chuzhou Science and Technology Bureau (No. 2021ZD008); “113” Industry Innovation Team of Chuzhou City in Anhui Province.

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Geographic Information and Tourism College, Chuzhou University, Chuzhou, 239000, China

Mingwei Zhao, Chun Wang, Yan Xu & Weitao Li

College of Resource, Environment and Tourism, Capital Normal University, Beijing, 100048, China

Xiaoxiao Ju

State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China

Na Zhao & Xiaoran Wu

College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100101, China

Anhui Engineering Laboratory of Geo-information Smart Sensing and Services, Chuzhou, 239000, China

Anhui Center for Collaborative Innovation in Geographical Information Integration and Application, Chuzhou, 239000, China

School of Geographic Information and Tourism, Chuzhou University, Chuzhou, 239000, China

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Zhao, M., Ju, X., Zhao, N. et al. A DEM upscaling method with integrating valley lines based on HASM. Front. Earth Sci. (2024). https://doi.org/10.1007/s11707-022-1068-0

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Received : 21 January 2023

Accepted : 12 May 2023

Published : 04 July 2024

DOI : https://doi.org/10.1007/s11707-022-1068-0

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