Data collection methods in educational research are used to gather information that is then analyzed and interpreted. As such, data collection is a very important step in conducting research and can influence results significantly. Once the research question and sources of data are identified, appropriate methods of data collection are determined.
Data collection includes a broad range of more specific techniques. This involves using instruments, scales, Tests , and structured observation and interviewing.
By the mid- to late twentieth centuries, other disciplines such as anthropology and sociology began to influence educational researchers. As contemporary educational researchers also draw from fields such as business, political science, and medicine, data collection in education has become a multidisciplinary phenomenon.
Because data collection is such a broad topic, General Overviews that attempt to cover all or most techniques tend to offer introductory treatments. Few texts, however, provide comprehensive coverage of every data collection technique.
Instead, some cover techniques appropriate for either quantitative or qualitative research approaches. Even more focus on one or two data collection methods within those two research contexts. Consequently, after presenting general overviews, this entry is categorized by data collection appropriate for quantitative and Qualitative Data Collection.
These sections, in turn, are subdivided into the major types of quantitative and qualitative data collection techniques. While there are some data collection techniques specific to mixed method research design, which implies a combination of qualitative and quantitative research methodologies, these specific procedures are not emphasized in the present article—readers are referred to the Oxford Bibliography article Mixed Methods Research by Nancy Leech for a comprehensive treatment of mixed method data collection techniques.
To locate sources for this article, extensive searches were performed using general-use Internet search engines and educational, psychological, and social science research databases. These searches included keywords around data collection and research methods, as well as specific data collection techniques such as surveys, Tests , Focus Groups , and observation.
Frequently cited texts and articles, most recent editions at the time, and sources specific to educational research were given priority. Once these sources were identified, their suggested readings and reference lists were mined for other potential sources. Works or scholars found in multiple reference lists were investigated. When applicable, book reviews in peer-reviewed journals were located and taken into account when curating sources.
Sources that demonstrated a high level of impact or offered unique coverage of the topic were included. General educational research overviews typically include several chapters on data collection, organized into qualitative and quantitative approaches. As a rule they are updated frequently so that they offer timely discussions of methodological trends. This makes the researchers frame the data on the assumed results. This technique is rarely used. The case studies are the historical moments which tell about the cause of the certain events regardless of all the good and bad.
Through this type of data collection and research, things are studied in depth considering various factors and the final lists are framed. This is similar to the utilization of case studies, here the contents in the newspaper, magazines, social networks, etc… are analyzed deeply to conclude for the required point. Your email address will not be published.
You may use these HTML tags and attributes: Home Uncategorized Different types of data collection methodologies. The below paragraphs lists the some of the data collection methodologies: Leave a Reply Cancel reply Your email address will not be published. These methods are needed when what is of interest is how the respond- ent chooses to allocate behavior in real time and across continuously available alternatives. Such empirical methods have long been used, but they can gen- erate very subtle and difficult problems in experimental design and subsequent analysis.
As theories of allocative behavior of all sorts become more sophisti- cated and precise, the experimental requirements become more demanding, so the need to better understand and solve this range of design issues is an outstanding challenge to methodological ingenuity. The second issue arises in repeated-trial designs when the behavior on suc- cessive trials, even if it does not exhibit a secular trend such as a learning curve , is markedly influenced by what has happened in the preceding trial or trials.
The more naturalistic the experiment and the more sensitive the meas-. But such sequential dependencies in observations cause a number of important concep- tual and technical problems in summarizing the data and in testing analytical models, which are not yet completely understood. In the absence of clear solutions, such effects are sometimes ignored by investigators, simplifying the data analysis but leaving residues of skepticism about the reliability and sig- nificance of the experimental results.
With continuing development of sensitive measures in repeated-trial designs, there is a growing need for more advanced concepts and methods for dealing with experimental results that may be influ- enced by sequential dependencies. Randomized Field Experiments The state of the art in randomized field experiments, in which different policies or procedures are tested in controlled trials under real conditions, has advanced dramatically over the past two decades. Problems that were once considered major methodological obstacles such as implementing random- ized field assignment to treatment and control groups and protecting the ran- domization procedure from corruption have been largely overcome.
While state-of-the-art standards are not achieved in every field experiment, the com- mitment to reaching them is rising steadily, not only among researchers but also among customer agencies and sponsors. The health insurance experiment described in Chapter 2 is an example of a major randomized field experiment that has had and will continue to have important policy reverberations in the design of health care financing.
Field experiments with the negative income tax guaranteed minimum income con- ducted in the s were significant in policy debates, even before their com- pletion, and provided the most solid evidence available on how tax-based income support programs and marginal tax rates can affect the work incentives and family structures of the poor. Important field experiments have also been carried out on alternative strategies for the prevention of delinquency and other criminal behavior, reform of court procedures, rehabilitative programs in men- tal health, family planning, and special educational programs, among other areas.
In planning field experiments, much hinges on the definition and design of the experimental cells, the particular combinations needed of treatment and control conditions for each set of demographic or other client sample charac- teristics, including specification of the minimum number of cases needed in each cell to test for the presence of effects.
Considerations of statistical power, client availability, and the theoretical structure of the inquiry enter into such specifications. Current important methodological thresholds are to find better ways of predicting recruitment and attrition patterns in the sample, of designing experiments that will be statistically robust in the face of problematic sample. Also of major significance are improvements in integrating detailed process and outcome measurements in field experiments.
Relatively unintrusive, inexpensive, and effective im- plementation measures are of great interest. There is, in parallel, a growing emphasis on designing experiments to evaluate distinct program components in contrast to summary measures of net program effects. Finally, there is an important opportunity now for further theoretical work to model organizational processes in social settings and to design and select outcome variables that, in the relatively short time of most field experiments, can predict longer-term effects: For example, in job-training programs, what are the effects on the community role models, morale, referral networks or on individual skills, motives, or knowledge levels that are likely to translate into sustained changes in career paths and income levels?
Survey Designs Many people have opinions about how societal mores, economic conditions, and social programs shape lives and encourage or discourage various kinds of behavior. People generalize from their own cases, and from the groups to which they belong, about such matters as how much it costs to raise a child, the extent to which unemployment contributes to divorce, and so on.
In fact, however, effects vary so much from one group to another that homespun generalizations are of little use. Fortunately, behavioral and social scientists have been able to bridge the gaps between personal perspectives and collective realities by means of survey research.
In particular, governmental information systems include volumes of extremely valuable survey data, and the facility of modern com- puters to store, disseminate, and analyze such data has significantly improved empirical tests and led to new understandings of social processes. Within this category of research designs, two major types are distinguished: In addition, and cross-cutting these types, there is a major effort under way to improve and refine the quality of survey data by investigating features of human memory and of question formation that affect survey response.
Repeated cross-sectional designs can either attempt to measure an entire population as does the oldest U. The general principle is to take independent samples at two or more times, measuring the variables of interest, such as income levels, housing plans, or opinions about public affairs, in the same way.
One methodological question of particular salience in such data is how to adjust for nonresponses and "don't know" responses.
Another is how to deal with self-selection bias. For example, to compare the earnings of women and men in the labor force, it would be mistaken to first assume that the two samples of labor-force participants are randomly selected from the larger populations of men and women; instead, one has to consider and incorporate in the analysis the factors that determine who is in the labor force.
In longitudinal panels, a sample is drawn at one point in time and the relevant variables are measured at this and subsequent times for the same people. In more complex versions, some fraction of each panel may be replaced or added to periodically, such as expanding the sample to include households formed by the children of the original sample. Comparing the fertility or income of different people in different circum- stances at the same time to kind correlations always leaves a large proportion of the variability unexplained, but common sense suggests that much of the unexplained variability is actually explicable.
There are systematic reasons for individual outcomes in each person's past achievements, in parental models, upbringing, and earlier sequences of experiences. Unfortunately, asking people about the past is not particularly helpful: In contrast, generation-long longitudinal data allow readings on the sequence of past circumstances uncolored by later outcomes. Such data are uniquely useful for studying the causes and consequences of naturally occur- ring decisions and transitions.
Thus, as longitudinal studies continue, quant,i- tative analysis is becoming feasible about such questions as: How are the de- cisions of individuals affected by parental experience? Which aspects of early decisions constrain later opportunities? And how does detailed background experience leave its imprint? Studies like the two-decade-long PSID are bring- ing within grasp a complete generational cycle of detailed data on fertility, work life, household structure, and income.
Advances in Longitudinal Designs Large-scale longitudinal data collection projects are uniquely valuable as vehicles for testing and improving survey research methodology. In ways that lie beyond the scope of a cross-sectional survey, longitudinal studies can some- times be designed without significant detriment to their substantive inter- ests to facilitate the evaluation and upgrading of data quality; the analysis of relative costs and effectiveness of alternative techniques of inquiry; and the standardization or coordination of solutions to problems of method, concept, and measurement across different research domains.
It should be especially noted that incorporating improvements in method- ology and data quality has been and will no doubt continue to be crucial to the growing success of longitudinal studies. Panel designs are intrinsically more vulnerable than other designs to statistical biases due to cumulative item non- response, sample attrition, time-in-sample effects, and error margins in re- peated measures, all of which may produce exaggerated estimates of change.
Over time, a panel that was initially representative may become much less representative of a population, not only because of attrition in the sample, but also because of changes in immigration patterns, age structure, and the like.
Longitudinal studies are also subject to changes in scientific and societal con- texts that may create uncontrolled drifts over time in the meaning of nominally stable questions or concepts as well as in the underlying behavior.
Also, a natural tendency to expand over time the range of topics and thus the interview lengths, which increases the burdens on respondents, may lead to deterioration of data quality or relevance. Careful methodological research to understand and overcome these problems has been done, and continued work as a com- ponent of new longitudinal studies is certain to advance the overall state of the art.
Longitudinal studies are sometimes pressed for evidence they are not de- signed to produce: By using research designs that combine field experiments with randomized assignment to program and control con- ditions and longitudinal surveys, one can capitalize on the strongest merits of each: Coupling experiments to ongoing longitudinal studies is not often feasible, given the multiple constraints of not disrupting the survey, developing all the complicated arrangements that go into a large-scale field experiment, and having the populations of interest over- lap in useful ways.
Yet opportunities to join field experiments to surveys are. A pattern of divergence and similarity has begun to emerge in coupled studies; additional cases are needed to understand why some naturally occurring social processes and longitudinal design features seem to approxi- mate formal random allocation and others do not.
The methodological impli- cations of such new knowledge go well beyond program evaluation and survey research. These findings bear directly on the confidence scientists and oth- ers can have in conclusions from observational studies of complex behavioral and social processes, particularly ones that cannot be controlled or simulated within the confines of a laboratory environment.
Memory and the Framing of questions A very important opportunity to improve survey methods lies in the reduc- tion of nonsampling error due to questionnaire context, phrasing of questions, and, generally, the semantic and social-psychological aspects of surveys.
Survey data are particularly affected by the fallibility of human memory and the sen- sitivity of respondents to the framework in which a question is asked. This sensitivity is especially strong for certain types of attitudinal and opinion ques- tions.
Efforts are now being made to bring survey specialists into closer contact with researchers working on memory function, knowledge representation, and language in order to uncover and reduce this kind of error. In many cases in which data are based on recollection, improvements can be achieved by shifting to techniques of structured interviewing and calibrated forms of memory elic- itation, such as specifying recent, brief time periods for example, in the last seven days within which respondents recall certain types of events with ac- ceptable accuracy.
Experiments on individual decision making show that the way a question is framed predictably alters the responses. Analysts of survey data find that some small changes in the wording of certain kinds of questions can produce large differences in the answers, although other wording changes have little effect. Even simply changing the order in which some questions are presented can produce large differences, although for other questions the order of presenta- tion does not matter.
For example, the following questions were among those asked in one wave of the General Social Survey: Would you say that your marriage is very happy, pretty happy, or not too happy? The explanations for and implications of such order effects on the many kinds of questions and sequences that can be used are not simple matters.
Further experimentation on the design of survey instruments promises not only to improve the accuracy and reliability of survey research, but also to advance understanding of how people think about and evaluate their behavior from day to day. Comparative Designs Both experiments and surveys involve interventions or questions by the scientist, who then records and analyzes the responses.
In contrast, many bodies of social and behavioral data of considerable value are originally derived from records or collections that have accumulated for various nonscientific reasons, quite often administrative in nature, in firms, churches, military or- ganizations, and governments at all levels.
Data of this kind can sometimes be subjected to careful scrutiny, summary, and inquiry by historians and social scientists, and statistical methods have increasingly been used to develop and evaluate inferences drawn from such data. Some of the main comparative approaches are'. Among the more striking problems facing the scientist using such data are the vast differences in what has been recorded by different agencies whose behavior is being compared this is especially true for parallel agencies in different nations , the highly unrepresentative or idiosyncratic sampling that can occur in the collection of such data, and the selective preservation and destruction of records.
Means to overcome these problems form a substantial methodological research agenda in comparative research. An example of the method of cross-national aggregative comparisons is found in investigations by political scientists and sociologists of the factors that underlie differences in the vitality of institutions of political democracy in different societies.
Some investigators have stressed the existence of a large middle class, others the level of education of a population, and still others the development of systems of mass communication.
In cross-national aggregate comparisons, a large number of nations are arrayed according to some measures of political democracy and then attempts are made to ascertain the strength of correlations between these and the other variables. In this line of analysis it is possible to use a variety of statistical cluster and regression techniques to isolate and assess the possible impact of certain variables on the institutions under study.
While this kind of research is cross-sectional in character, statements about historical processes are often invoked to explain the correlations. Why did democracy develop in such different ways in America, France, and England?
Why did northeastern Europe develop rational bourgeois capitalism, in contrast to the Mediterranean and Asian nations? Modern scholars have turned their attention to explaining, for example, differences among types of fascism between the two World Wars, and similarities and differences among modern state welfare systems, using these comparisons to unravel the salient causes.
The questions asked in these instances are inevitably historical ones. Historical case studies involve only one nation or region, and so they may not be geographically comparative. However, insofar as they involve tracing the transformation of a society's major institutions and the role of its main shaping events, they involve a comparison of different periods of a nation's or a region's history.
The goal of such comparisons is to give a systematic account of the relevant differences. Sometimes, particularly with respect to the ancient societies, the historical record is very sparse, and the methods of history and archaeology mesh in the reconstruction of complex social arrangements and patterns of change on the basis of few fragments.
Like all research designs, comparative ones have distinctive vulnerabilities and advantages: One of the main advantages of using comparative designs is that they greatly expand the range of data, as well as the amount of variation in those data, for study. Consequently, they allow for more encompassing explanations and theories that can relate highly divergent outcomes to one another in the same framework.
They also contribute to reducing any cultural biases or tendencies toward parochialism among scientists studying common human phenomena. One main vulnerability in such designs arises from the problem of achieving comparability. For example, a vote in a Western democracy is different from a vote in an Eastern bloc country, and a voluntary vote in the United States means something different from a compulsory vote in Australia. These circumstances make for interpretive difficulties in comparing aggregate rates of voter turnout in different countries.
The problem of achieving comparability appears in historical analysis as well. For example, changes in laws and enforcement and recording procedures over time change the definition of what is and what is not a crime, and for that reason it is difficult to compare the crime rates over time. Comparative re- searchers struggle with this problem continually, working to fashion equivalent measures; some have suggested the use of different measures voting, letters to the editor, street demonstration in different societies for common variables.
A second vulnerability is controlling variation. Traditional experiments make conscious and elaborate efforts to control the variation of some factors and thereby assess the causal significance of others. In surveys as well as experi- ments, statistical methods are used to control sources of variation and assess suspected causal significance. In comparative and historical designs, this kind of control is often difficult to attain because the sources of variation are many and the number of cases few.
Scientists have made efforts to approximate such control in these cases of "many variables, small N. Another method is to select, for comparative purposes, a sample of societies that resemble one another in certain critical ways, such as size, common language, and common level of development, thus attempting to hold these factors roughly constant, and then seeking explanations among other factors in which the sampled societies differ from one another.
Ethnographic Designs Traditionally identified with anthropology, ethnographic research designs are playing increasingly significant roles in most of the behavioral and social sciences. The core of this methodology is participant-observation, in which a researcher spends an extended period of time with the group under study, ideally mastering the local language, dialect, or special vocabulary, and partic- ipating in as many activities of the group as possible.
This kind of participant- observation is normally coupled with extensive open-ended interviewing, in which people are asked to explain in depth the rules, norms, practices, and beliefs through which from their point of view they conduct their lives.
A principal aim of ethnographic study is to discover the premises on which those rules, norms, practices, and beliefs are built. The use of ethnographic designs by anthropologists has contributed signif- icantly to the building of knowledge about social and cultural variation. One major trend concerns its scale. Ethnographic methods were originally developed largely for studying small-scale groupings known variously as village, folk, primitive, preliterate, or simple societies.
Over the decades, these methods have increasingly been applied to the study of small. The typical subjects of ethnographic study in modern society are small groups or relatively small social networks, such as outpatient clinics, medical schools, religious cults and churches, ethn- ically distinctive urban neighborhoods, corporate offices and factories, and government bureaus and legislatures.
As anthropologists moved into the study of modern societies, researchers in other disciplines particularly sociology, psychology, and political science- began using ethnographic methods to enrich and focus their own insights and findings.
At the same time, studies of large-scale structures and processes have been aided by the use of ethnographic methods, since most large-scale changes work their way into the fabric of community, neighborhood, and family, af- fecting the daily lives of people. Ethnographers have studied, for example, the impact of new industry and new forms of labor in "backward" regions; the impact of state-level birth control policies on ethnic groups; and the impact on residents in a region of building a dam or establishing a nuclear waste dump.
Advances in structured interviewing see above have proven especially pow- erful in the study of culture. Techniques for understanding kinship systems, concepts of disease, color terminologies, ethnobotany, and ethnozoology have been radically transformed and strengthened by coupling new interviewing methods with modem measurement and scaling techniques see below. These techniques have made possible more precise comparisons among cultures and identification of the most competent and expert persons within a culture.
The next step is to extend these methods to study the ways in which networks of propositions such as boys like sports, girls like babies are organized to form belief systems. Much evidence suggests that people typically represent the world around them by means of relatively complex cognitive models that in- volve interlocking propositions.
The techniques of scaling have been used to develop models of how people categorize objects, and they have great potential for further development, to analyze data pertaining to cultural propositions. Ideological Systems Perhaps the most fruitful area for the application of ethnographic methods in recent years has been the systematic study of ideologies in modern society.
Earlier studies of ideology were in small-scale societies that were rather ho- mogeneous. In these studies researchers could report on a single culture, a uniform system of beliefs and values for the society as a whole. Modern societies are much more diverse both in origins and number of subcultures, related to. Yet these sub- cultures and ideologies share certain underlying assumptions or at least must find some accommodation with the dominant value and belief systems in the society.
The challenge is to incorporate this greater complexity of structure and process into systematic descriptions and interpretations. One line of work carried out by researchers has tried to track the ways in which ideologies are created, transmitted, and shared among large populations that have tradition- ally lacked the social mobility and communications technologies of the West.
This work has concentrated on large-scale civilizations such as China, India, and Central America. How are the ideological doctrines and cultural values of the urban elites, the great traditions, transmitted to local communities?
How are the little traditions, the ideas from the more isolated, less literate, and politically weaker groups in society, transmitted to the elites? India and southern Asia have been fruitful areas for ethnographic research on these questions. The great Hindu tradition was present in virtually all local contexts through the presence of high-caste individuals in every community.
It operated as a pervasive standard of value for all members of society, even in the face of strong little traditions. The situation is surprisingly akin to that of modern, industrialized societies. The central research questions are the degree and the nature of penetration of dominant ideology, even in groups that appear marginal and subordinate and have no strong interest in sharing the dominant value system. Historical Reconstruction Another current trend in ethnographic methods is its convergence with archival methods.
One joining point is the application of descriptive and in- terpretative procedures used by ethnographers to reconstruct the cultures that created historical documents, diaries, and other records, to interview history, so to speak. For example, a revealing study showed how the Inquisition in the Italian countryside between the s and s gradually worked subtle changes in an ancient fertility cult in peasant communities; the peasant beliefs and rituals assimilated many elements of witchcraft after learning them from their persecutors.
A good deal of social history particularly that of the fam- ily has drawn on discoveries made in the ethnographic study of primitive societies. As described in Chapter 4, this particular line of inquiry rests on a marriage of ethnographic, archival, and demographic approaches. A strikingly successful example in this kind of effort is a study of head-hunting. By combining an interpretation of local oral tradition with the fragmentary observations that were made by outside observers such as missionaries, traders, colonial officials , historical fluctuations in the rate and significance of head-hunting were shown to be partly in response to such international forces as the great depression and World War II.
Researchers are also investigating the ways in which various groups in contemporary societies invent versions of traditions that may or may not reflect the actual history of the group.
This process has been observed among elites seeking political and cultural legitimation and among hard-pressed minorities for example, the Basque in Spain, the Welsh in Great Britain seeking roots and political mo-. Ethnography is a powerful method to record, describe, and interpret the system of meanings held by groups and to discover how those meanings affect the lives of group members.
It is a method well adapted to the study of situations in which people interact with one another and the researcher can interact with them as well, so that information about meanings can be evoked and observed. By the same token, experimental, survey, and comparative methods frequently yield connections, the meaning of which is unknown; ethnographic methods are a valuable way to determine them. Scientists continuously try to describe possible structures and ask whether the data can, with allowance for errors of measurement, be described adequately in terms of them.
Over a long time, various families of structures have recurred throughout many fields of science; these structures have become objects of study in their own right, principally by statisticians, other methodological specialists, applied mathematicians, and philosophers of logic and science. Methods have evolved to evaluate the adequacy of particular structures to account for particular types of data.
In the interest of clarity we discuss these structures in this section and the analytical methods used for estimation and evaluation of them in the next section, although in practice they are closely intertwined. A good deal of mathematical and statistical modeling attempts to describe the relations, both structural and dynamic, that hold among variables that are presumed to be representable by numbers.
Such models are applicable in the behavioral and social sciences only to the extent that appropriate numerical. In many studies the phenomena in question and the raw data obtained are not intrinsically nu- merical, but qualitative, such as ethnic group identifications. The identifying numbers used to code such questionnaire categories for computers are no more than labels, which could just as well be letters or colors. One key question is whether there is some natural way to move from the qualitative aspects of such data to a structural representation that involves one of the well-understood numerical or geometric models or whether such an attempt would be inherently inappropriate for the data in question.
The decision as to whether or not particular empirical data can be represented in particular numerical or more complex structures is seldom simple, and strong intuitive biases or a priori assumptions about what can and cannot be done may be misleading. Recent decades have seen rapid and extensive development and application of analytical methods attuned to the nature and complexity of social science data.
Data Collection is an important aspect of any type of research study. Inaccurate data collection can impact the results of a study and ultimately lead to invalid results. Data collection methods for impact evaluation vary along a continuum.
DATA COLLECTION Research methodology A brief and succinct account on what the techniques for collecting data are, how to apply them, where to Magister “Civilisation: find data of any type, and the way to keep records for language and Cultural an optimal management of cost, time and effort. Studies.
Chapter 9-METHODS OF DATA COLLECTION 1. METHODS OF DATA COLLECTION 2. What is data collection? The process by which the researcher collects the information needed to answer the research . Data Collection Techniques. Information you gather can come from a range of sources. Likewise, there are a variety of techniques to use when gathering primary data. Responses can be analyzed with quantitative methods by assigning numerical values to Likert-type scales; Consists of examining existing data in the form of databases.
Types of Data Collection Methods for Research Type Lister Types of Human Behavior 0 Research can be defined as the process of gathering facts and information is a structured manner to understand a subject matter in more depth. Methods of data collection 1. The task of data collection begins after a research problem has been defined and research design /plan chalked out. 3. TYPES OF DATA1) PRIMARY DATA: Are those which are collected a fresh and for the first time and thus happen to be original in character and known as Primary data.2) SECONDARY DATA: Are those.