Ways to analyze data in research
A research g the proposal - data your research proposal, you will also discuss how you will conduct an analysis of your data. By the time you get to the analysis of your data, most of the really difficult work has been done. It's much more difficult to define the research problem, develop and implement a sampling plan, develop a design structure, and determine your measures. If you have done this work well, the analysis of the data is usually a fairly straightforward you look at the various ways of analyzing and discussing data, you need to review the differences between qualitative research/quantitative research and qualitative data/quantitative do i have to analyze data? The analysis, regardless of whether the data is qualitative or quantitative, may:Describe and summarize the fy relationships between fy the difference between r, you distinguished between qualitative and quantitative research. It is highly unlikely that your research will be purely one or the other – it will probably be a mixture of the two example, you may have decided to ethnographic research, which is qualitative. In your first step, you may have taken a small sample (normally associated with qualitative research) but then conducted a structured interview or used a questionnaire (normally associated with quantitative research) to determine people’s attitudes to a particular phenomenon (qualitative research). Source of confusion for many people is the belief that qualitative research generates just qualitative data (text, words, opinions, etc) and that quantitative research generates just quantitative data (numbers). Sometimes this is the case, but both types of data can be generated by each approach. For instance, a questionnaire (quantitative research) will often gather factual information like age, salary, length of service (quantitative data) – but may also collect opinions and attitudes (qualitative data). It comes to data analysis, some believe that statistical techniques are only applicable for quantitative data. There are many statistical techniques that can be applied to qualitative data, such as ratings scales, that has been generated by a quantitative research approach. Even if a qualitative study uses no quantitative data, there are many ways of analyzing qualitative data. For example, having conducted an interview, transcription and organization of data are the first stages of analysis.
How to analyze data in research
Manchester metropolitan university (department of information and communications) and learn higher offer a clear introductory tutorial to qualitative and quantitative data analysis through their analyze this!!! In additional to teaching about strategies for both approaches to data analysis, the tutorial is peppered with short quizzes to test your understanding. The site also links out to further te this tutorial and use your new knowledge to complete your planning guide for your data are many computer- and technology-related resources available to assist you in your data general ing research (lots of examples of studies, and lots of good background, especially for qualitative studies). Data tative data analysis rice virtual lab in statistics also houses an online textbook, hyperstat. The site also includes a really useful section of case studies, which use real life examples to illustrate various statistical sure which statistical test to use with your data? The diagram is housed within another good introduction to data statistical analysis and data management computer-aided qualitative data analysis are many computer packages that can support your qualitative data analysis. The following site offers a comprehensive overview of many of them: online r package that allows you analyze textual, graphical, audio and video data. No free demo, but there is a student has add-ons which allow you to analyze vocabulary and carry out content analysis. Use these questions and explanations for ideas as you complete your planning guide for this common worries amongst researchers are:Will the research i’ve done stand up to outside scrutiny? Questions are addressed by researchers by assessing the data collection method (the research instrument) for its reliability and its ility is the extent to which the same finding will be obtained if the research was repeated at another time by another researcher. The following questions are typical of those asked to assess validity issues:Has the researcher gained full access to the knowledge and meanings of data? Procedure is perfectly reliable, but if a data collection procedure is unreliable then it is also invalid. The other problem is that even if it is reliable, then that does not mean it is necessarily ulation is crosschecking of data using multiple data sources or using two or more methods of data collection. The many sources of non-sampling errors include the following:Researcher error – unclear definitions; reliability and validity issues; data analysis problems, for example, missing iewer error – general approach; personal interview techniques; recording dent error – inability to answer; unwilling; cheating; not available; low response section was discussed in elements of the proposal, where there are many online resources, and you have reflective journal entries that will support you as you develop your ideas for reliability and validity in your planning guide.
How to do data analysis in research
In addition this writing tutorial specifically addresses the ways in which this can be explained in your research to writing the proposal - different a skilltable of taking actiontroubleshooting guide. Collecting and using archival tool box needs your contribution can help change n training teaching core how to collect your data and analyze it, figuring out what it means, so that you can use it to draw some conclusions about your do we mean by collecting data? Previous sections of this chapter, we’ve discussed studying the issue, deciding on a research design, and creating an observational system for gathering information for your evaluation. Now it’s time to collect your data and analyze it – figuring out what it means – so that you can use it to draw some conclusions about your work. In this section, we’ll examine how to do just do we mean by collecting data? You’ll have to record the observations in appropriate ways and organize them so they’re optimally ing and organizing data may take different forms, depending on the kind of information you’re collecting. The way you collect your data should relate to how you’re planning to analyze and use it. Regardless of what method you decide to use, recording should be done concurrent with data collection if possible, or soon afterwards, so that nothing gets lost and memory doesn’t of the things you might do with the information you collect include:Gathering together information from all sources and photocopies of all recording forms, records, audio or video recordings, and any other collected materials, to guard against loss, accidental erasure, or other ng narratives, numbers, and other information into a computer program, where they can be arranged and/or worked on in various ming any mathematical or similar operations needed to get quantitative information ready for analysis. These might, for instance, include entering numerical observations into a chart, table, or spreadsheet, or figuring the mean (average), median (midpoint), and/or mode (most frequently occurring) of a set of ribing (making an exact, word-for-word text version of) the contents of audio or video data (translating data, particularly qualitative data that isn’t expressed in numbers, into a form that allows it to be processed by a specific software program or subjected to statistical analysis). You might also want to group observations in several different ways, so that you can study interactions among different are two kinds of variables in research. An independent variable (the intervention) is a condition implemented by the researcher or community to see if it will create change and improvement. A smoking cessation program, for example, is an independent variable that may change group members’ smoking behavior, the primary dependent do we mean by analyzing data? The point, in terms of your evaluation, is to get an accurate assessment in order to better understand your work and its effects on those you’re concerned with, or in order to better understand the overall are two kinds of data you’re apt to be working with, although not all evaluations will necessarily include both. Quantitative data refer to the information that is collected as, or can be translated into, numbers, which can then be displayed and analyzed mathematically.
As you might expect, quantitative and qualitative information needs to be analyzed tative data are typically collected directly as numbers. Can also be collected in forms other than numbers, and turned into quantitative data for analysis. Researchers can count the number of times an event is documented in interviews or records, for instance, or assign numbers to the levels of intensity of an observed event or behavior. Whether or not this kind of translation is necessary or useful depends on the nature of what you’re observing and on the kinds of questions your evaluation is meant to tative data is usually subjected to statistical procedures such as calculating the mean or average number of times an event or behavior occurs (per day, month, year). These operations, because numbers are “hard” data and not interpretation, can give definitive, or nearly definitive, answers to different questions. And they can identify relationships among different variables, which may or may not mean that one causes numbers or “hard data,” qualitative information tends to be “soft,” meaning it can’t always be reduced to something definite. And that interpretation may be far more valuable in helping that student succeed than knowing her grade or numerical score on the ative data can sometimes be changed into numbers, usually by counting the number of times specific things occur in the course of observations or interviews, or by assigning numbers or ratings to dimensions (e. Where one person might see a change in program he considers important another may omit it due to perceived ative data can sometimes tell you things that quantitative data can’t. It may also show you patterns – in behavior, physical or social environment, or other factors – that the numbers in your quantitative data don’t, and occasionally even identify variables that researchers weren’t aware is often helpful to collect both quantitative and qualitative tative analysis is considered to be objective – without any human bias attached to it – because it depends on the comparison of numbers according to mathematical computations. Analysis of qualitative data is generally accomplished by methods more subjective – dependent on people’s opinions, knowledge, assumptions, and inferences (and therefore biases) – than that of quantitative data. The identification of patterns, the interpretation of people’s statements or other communication, the spotting of trends – all of these can be influenced by the way the researcher sees the world. What the researcher chooses to measure, the accuracy of the observations, and the way the research is structured to ask only particular questions can all influence the results, as can the researcher’s understanding and interpretation of the subsequent should you collect and analyze data for your evaluation? This data collection and sensemaking is critical to an initiative and its future success, and has a number of data can show whether there was any significant change in the dependent variable(s) you hoped to influence. Collecting and analyzing data helps you see whether your intervention brought about the desired term “significance” has a specific meaning when you’re discussing statistics.
Generally, researchers don’t consider a result significant unless it shows at least a 95% certainty that it’s correct (called the . The level of significance is built into the statistical formulas: once you get a mathematical result, a table (or the software you’re using) will tell you the level of , if data analysis finds that the independent variable (the intervention) influenced the dependent variable at the . Data analyses may help discover unexpected influences; for instance, that the effort was twice as large for those participants who also were a part of a support group. Some types of statistical procedures look for connections (“correlations” is the research term) among variables. The effect of cultural issues, how well methods are used, the appropriateness of your approach for the population – these as well as other factors that influence success can be highlighted by careful data collection and analysis. Being a good trustee or steward of community investment includes regular review of data regarding progress and can show the field what you’re learning, and thus pave the way for others to implement successful methods and approaches. In that way, you’ll be helping to improve community efforts and, ultimately, quality of life for people who and by whom should data be collected and analyzed? Far as data collection goes, the “when” part of this question is relatively simple: data collection should start no later than when you begin your work – or before you begin in order to establish a baseline or starting point – and continue throughout. Ideally, you should collect data for a period of time before you start your program or intervention in order to determine if there are any trends in the data before the onset of the intervention. Additionally, in order to gauge your program’s longer-term effects, you should collect follow-up data for a period of time following the conclusion of the timing of analysis can be looked at in at least two ways: one is that it’s best to analyze your information when you’ve collected all of it, so you can look at it as a whole. The other is that if you analyze it as you go along, you’ll be able to adjust your thinking about what information you actually need, and to adjust your program to respond to the information you’re getting. Both approaches are legitimate, but ongoing data collection and review can particularly lead to improvements in your “who” question can be more complex. That’s not the case, you have some choices:You can hire or find a volunteer outside evaluator, such as from a nearby college or university, to take care of data collection and/or analysis for can conduct a less formal evaluation. Can collect the data and then send it off to someone – a university program, a friendly statistician or researcher, or someone you hire – to process it for can collect and rely largely on qualitative data.
You wouldn’t want to conduct a formal evaluation of effectiveness of a new medication using only qualitative data, but you might be able to draw some reasonable conclusions about use or compliance patterns from qualitative possible, use a randomized or closely matched control group for comparison. By the same token, if 72% of your students passed and 70% of the control group did as well, it seems pretty clear that your instruction had essentially no effect, if the groups were starting from approximately the same should actually collect and analyze data also depends on the form of your evaluation. If you’re doing a participatory evaluation, much of the data collection - and analyzing - will be done by community members or program participants themselves. If you’re conducting an evaluation in which the observation is specialized, the data collectors may be staff members, professionals, highly trained volunteers, or others with specific skills or training (graduate students, for example). Another way analysis can be accomplished is by professionals or other trained individuals, depending upon the nature of the data to be analyzed, the methods of analysis, and the level of sophistication aimed at in the do you collect and analyze data? Your evaluation includes formal or informal research procedures, you’ll still have to collect and analyze data, and there are some basic steps you can take to do ent your measurement 've previously discussed designing an observational system to gather information. The definition and description should be clear enough to enable observers to agree on what they’re observing and reliably record data in the same and train observers. This may include reviewing archival material; conducting interviews, surveys, or focus groups; engaging in direct observation; data in the agreed-upon ways. Audio or video, journals, ze the data you’ve you do this depends on what you’re planning to do with it, and on what you’re interested any necessary data into the computer. Into a word processing program, or entering various kinds of information (possibly including audio and video) into a database, spreadsheet, a gis (geographic information systems) program, or some other type of software or ribe any audio- or videotapes. This makes them easier to work with and copy, and allows the opportunity to clarify any hard-to-understand passages of any tests and record the scores your information in ways appropriate to your interest. This may include sorting by category of observation, by event, by place, by individual, by group, by the time of observation, or by a combination or some other possible, necessary, and appropriate, transform qualitative into quantitative data. This might involve, for example, counting the number of times specific issues were mentioned in interviews, or how often certain behaviors were t data graphing, visual inspection, statistical analysis, or other operations on the data as ’ve referred several times to statistical procedures that you can apply to quantitative data. Journals can be particularly revealing in this area because they record people’s experiences and reflections over g patterns in qualitative data.
One measure of success might be meeting a goal for planning or program implementation, for note of any significant or interesting ing on the nature of your research, results may be statistically significant (the 95% or better certainty that we discussed earlier), or simply important or unusual. In both quantitative and qualitative information, patterns often emerge: certain health conditions seem to cluster in particular geographical areas; people from a particular group behave in similar ways; etc. Whether as a result of statistical analysis, or of examination of your data and application of logic, some findings may stand out. It might be obvious from your data collection, for instance, that, while violence or roadway injuries may not be seen as a problem citywide, they are much higher in one or more particular areas, or that the rates of diabetes are markedly higher for particular groups or those living in areas with greater disparities of income. If you have the resources, it’s wise to look at the results of your research in a number of different ways, both to find out how to improve your program, and to learn what else you might do to affect the ret the you’ve organized your results and run them through whatever statistical or other analysis you’ve planned for, it’s time to figure out what they mean for your evaluation. Probably the most common question that evaluation research is directed toward is whether the program being evaluated works or makes a difference. In research terms, that often translates to “what were the effects of the independent variable (the program, intervention, etc. As with programs with positive effects, these might be positive, neutral, or negative; single or multiple; or consistent or your analysis gives you a clear indication that what you’re doing is accomplishing your purposes, interpretation is relatively simple: you should keep doing it, while trying out ways to make it even more effective, or while aiming at other related issues as we discuss elsewhere in the community tool box, good programs are dynamic -- constantly striving to improve, rather than assuming that what they’re doing is as good as it can your analysis shows that your program is ineffective or negative, however – or, for that matter, if a positive analysis leaves you wondering how to make your successful efforts still more successful – interpretation becomes more complex. Correlations may also indicate patterns in your data, or may lead to an unexpected way of looking at the issue you’re can often use qualitative data to understand the meaning of an intervention, and people’s reactions to the observation that participants are continually suffering from a variety of health problems may be traced, through qualitative data, to nutrition problems (due either to poverty or ignorance) or to lack of access to health services, or to cultural restrictions (some muslim women may be unwilling – or unable because of family prohibition – to accept care and treatment from male doctors, for example). You have organized your data, both statistical results and anything that can’t be analyzed statistically need to be analyzed logically. The numbers can tell you whether there is change, but they can’t always tell you what causes it or why (although they sometimes can), or why some people benefit while others don’t. Those are often matters for logical analysis, or critical ing and interpreting the data you’ve collected brings you, in a sense, back to the beginning. You have to keep up the process to ensure that you’re doing the best work you can and encouraging changes in individuals, systems, and policies that make for a better and healthier have to become a cultural detective to understand your initiative, and, in some ways, every evaluation is an anthropological heart of evaluation research is gathering information about the program or intervention you’re evaluating and analyzing it to determine what it tells you about the effectiveness of what you’re doing, as well as about how you can maintain and improve that ting quantitative data – information expressed in numbers – and subjecting it to a visual inspection or formal statistical analysis can tell you whether your work is having the desired effect, and may be able to tell you why or why not as well. It can also highlight connections (correlations) among variables, and call attention to factors you may not have ting and analyzing qualitative data – interviews, descriptions of environmental factors, or events, and circumstances – can provide insight into how participants experience the issue you’re addressing, what barriers and advantages they experience, and what you might change or add to improve what you you’ve gained the knowledge that your information provides, it’s time to start the process again.
Use what you’ve learned to continue to evaluate what you do by collecting and analyzing data, and continually improve your environmental education evaluation resource assistant (meera) provides extensive information on how to analyze data. Pell institute offers user-friendly information on how to analyze qualitative data as a part of their evaluation toolkit. The site provides a simple explanation of qualitative data with a step-by-step process to collecting and analyzing h the evaluation toolkit, the pell institute has compiled a user-friendly guide to easily and efficiently analyze quantitative data. In addition to explaining the basis of quantitative analysis, the site also provides information on data tabulation, descriptives, disaggregating data, and moderate and advanced analytical ’s analyzing qualitative data for evaluation provides how-to guidance for analyzing qualitative ’s analyzing quantitative data for evaluation provides steps to planning and conducting quantitative analysis, as well as the advantages and disadvantages of using quantitative and graphs to communicate research findings, from the model systems knowledge translation center (msktc), will provide guidance on which chart types are best suited for which types of data and for which purposes, shows examples of preferred practices and practical tips for each chart type, and provides cautions and examples of misuse and poor use of each chart type and how to make ting and analyzing evaluation data, 2nd edition, provided by the national library of medicine, provides information on collecting and analyzing qualitative and quantitative data. This booklet contains examples of commonly used methods, as well as a toolkit on using mixed methods in ed for the adolescent and school health sector of the cdc, data collection and analysis methods is an extensive list of articles pertaining to the collection of various forms of data including questionnaires, focus groups, observation, document analysis, and statistics is a guide to free and open source software for statistical analysis that includes a comparison, explaining what operations each program can ed by the u. Department of health and human services, this hrsa toolkit offers advice on successfully collecting and analyzing data. An extensive list of both for collecting and analyzing data and on computerized disease registries is human development index map is a valuable tool from measure of america: a project of the social science research council. You should still be able to navigate through these materials but selftest questions will not 9 : introduction to 1: introduction to 2 research and the voluntary and community 3 primary and secondary 4 research 5 quantitative 6 qualitative 7 ethics and data 8 presenting and using research findings. Analysing qualitative research analysis of qualitative research involves aiming to uncover and / or understand the big picture - by using the data to describe the phenomenon and what this means. Both qualitative and quantitative analysis involves labelling and coding all of the data in order that similarities and differences can be recognised. The qualitative researcher, however, has no system for pre-coding, therefore a method of identifying and labelling or coding data needs to be developed that is bespoke for each research. Which is called content t analysis can be used when qualitative data has been collected through:Content analysis is '... Procedure for the categorisation of verbal or behavioural data, for purposes of classification, summarisation and tabulation. Content can be analysed on two levels:Basic level or the manifest level: a descriptive account of the data i.
This is what was said, but no comments or theories as to why or level or latent level of analysis: a more interpretive analysis that is concerned with the response as well as what may have been inferred or t analysis involves coding and classifying data, also referred to as categorising and indexing and the aim of context analysis is to make sense of the data collected and to highlight the important messages, features or with wimba ped by authenticity consulting, ing, interpreting and reporting basic research results. Copyright carter mcnamara, mba, phd, authenticity consulting,Adapted from the field guide to nonprofit program design, evaluation and field guide to consulting and organizational ns of this topic ing and interpreting should carry out the research? Of a research report -- an pitfalls to l information and and conducting d library see the library's blogs related to analyzing research addition to the articles on this current page, see the following blogs posts related to analyzing research results. Also see the section "recent blog posts" in r of the blog or click on "next" near the bottom of a post ing and interpreting ing quantitative and qualitative data is often the advanced research and evaluation methods courses. However,There are certain basics which can help to make sense of start with your research analyzing data (whether from questionnaires, interviews,Focus groups, or whatever), always start from review of your , i. This will help you organize your data and focus your example, if you wanted to improve a program by strengths and weaknesses, you can organize data into ths, weaknesses and suggestions to improve the you wanted to fully understand how your program works, organize data in the chronological order in which clients go through your program. If you are conducting a ement study, you can categorize data according to each ated with each overall performance result, e. Ratings, rankings,Make copies of your data and store the master copy the copy for making edits, cutting and pasting, te the information, i. Translate recommendations to action plans, is going to do what about the research results and by s / bankers will likely require a report that executive summary (this is a summary of conclusions and recommendations,Not a listing of what sections of information are in the report. Under evaluation; the research goals, methods, and analysis procedures; conclusions and recommendations; and any relevant attachments,E. Or, the funder to review the report sure to record the research plans and activities in ch plan which can be referenced when a similar is needed in the should carry out the research? Then a research expert helps the determine what the research methods should be, and how ing data will be analyzed and reported back to the an organization can afford any outside help at all, it for identifying the appropriate research methods and how can be collected. Conduct interviews, and analyze results of questionnaires, no outside help can be obtained, the organization can a great deal by applying the methods and analyzing lves. However, there is a strong chance that data strengths and weaknesses of a product, service or not be interpreted fairly if the data are analyzed by responsible for ensuring the product, service or a good one.
Therefore, if at all possible, have someone other than responsible for the product, service or program to and determine research ts of a research report -- an your research plan is documented so that you can efficiently carry out your research activities. In your plan,Record enough information so that someone outside of the understand what you're researching and how. Summary (one-page, concise overview of e of the report (what type of research was conducted,What decisions are being aided by the findings of the research. About organization and product/service/ is being zation description/t/service/program description (that is being researched). Evaluation goals (eg, what questions are being of data/information that were data/information were collected (what instruments data/information were tions of the evaluation (eg, cautions about findings/ how to use the findings/conclusions, etc. Instruments used to collect data/, eg, in tabular format, onials, comments made by users of the product/service/ studies of users of the product/service/ related pitfalls to 't balk at research because it seems far too "scientific. It's far more do something than to wait until every last detail has hard to include some interviews in your research onnaires don't capture "the story," and the usually the most powerful depiction of the benefits of ts, services, programs, 't interview just the successes. You'll learn a by understanding its failures, dropouts, 't throw away research results once a report has ted. Results don't take up much room, and they can us information later when trying to understand the product, service or the category of business research:To round out your knowledge of this library topic, you to review some related topics, available from the link of the related topics includes free, online , scan the recommended books listed below. You can the title of the book in that bubble to get more information, guide to nonprofit program design, marketing and carter mcnamara, published by authenticity consulting, -developed nonprofit programs are based on research to verify unmet community needs, how those needs can be met through programs,How clients can best access the programs, what clients and others are willing to pay for the programs, and itors and collaborators. Funders expect to see this level of research from nonprofits applying for book provides step-by-step guidelines for conducting relevant -- and especially realistic and flexible -- market es many online forms that can be are the most popular topics; to see more, use index and search box, to the ising & ts & ty bldg (nonprofit). Nable ng/learning & strategic management library, © copyright authenticity consulting, llc ® ; all rights analysis – analyzing data in qualitative is data analysis? Is more than coding, sorting and gh some researchers suggest that disassembling, coding, and then sorting and sifting through your data, is the primary path to analyzing data / data analysis. But as other rightly caution, intensive data coding, disassembly, sorting, and sifting, is neither the only way to analyze your data nor is it necessarily the most appropriate strategy.
It has been argued that they also fit the notice, collect, and think process invariably also belonging to the data analysis the thinking process the researcher examines the things that have been collected. The goals of the data analysis are:To make some type of sense out of each data look for patterns and relationships both within a collection, and also across collections, make general discoveries about the phenomena you are use an analogy: after sorting the pieces of a jigsaw puzzle into groups, it is important to inspect individual pieces to determine how they fit together and form smaller parts of the picture (e. When analyzing data, one compares and contrasts each of the things that have been noticed in order to discover similarities and differences, build typologies, or find sequences and patterns. In the process one might also stumble across both “wholes” and, quite literally, holes in the the jigsaw puzzle approach to analyzing data is frequently productive and fruitful, it also entails some risks and problems that also translate to qualitative data analysis. Experienced qualitative social scientists have always been aware of the potential problems, and organize their work to minimize the adverse effects. For example, when coding data, the simple act of breaking down data into its constituent parts can distort and mislead the analyst and distort the final data analysis. A serious problem is sometimes created by the very fact of organizing the material through coding or breaking it up into segments in that this destroys the totality of philosophy as expressed by the interviewee-which is closely related to the major goal of the study that informs the data analysis. A proper data analysis acknowledges this problem and, in fact, takes precautions already when first analyzing ative data analysis software.
