Data collection and analysis procedures

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? 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). 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?

Data collection and analysis plan

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. 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. Various kinds of quantitative analysis can indicate changes in a dependent variable related to – frequency, duration, timing (when particular things happen), intensity, level, etc. 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.

Methodology for data collection and analysis

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. Be aware, however, that quantitative analysis is influenced by a number of subjective factors as well. 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.

Data collection and data analysis

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. By combining quantitative and qualitative analysis, you can often determine not only what worked or didn’t, but why. 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.

Data collection and analysis

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. Both approaches are legitimate, but ongoing data collection and review can particularly lead to improvements in your “who” question can be more complex. If you’re reasonably familiar with statistics and statistical procedures, and you have the resources in time, money, and personnel, it’s likely that you’ll do a somewhat formal study, using standard statistical tests. 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. Your results may not be as sophisticated as if you subjected them to rigorous statistical procedures, but they can still tell you a lot about your program. 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.

Data collection analysis and interpretation

Again, these results won’t be as reliable as if the comparison were made using statistical procedures, but they can point you in the right direction. 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). Even where complicated statistical procedures are necessary, participants and/or community members might be involved in sorting out what those results actually mean once the math is done and the results are in. 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.

Quantitative data collection and analysis

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 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. If you have the right numbers, you can find out a great deal about whether your program is causing or contributing to change and improvement, what that change is, whether there are any expected or unexpected connections among variables, how your group compares to another you’re measuring, are other excellent possibilities for analysis besides statistical procedures, however. Journals can be particularly revealing in this area because they record people’s experiences and reflections over g patterns in qualitative data. With or without a control or comparison group, many statistical procedures can tell you whether changes in dependent variables are truly significant (or not likely due to chance).

Qualitative data collection and analysis

In some cases, you may need to subject them to statistical procedures (regression analysis, for example) to see if, in fact, they’re random, or if they constitute actual s important findings. 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. Statistics or other analysis showed clear positive effects at a high level of significance for the people in your program and – if you used a multiple-group design – none, or far fewer, of the same effects for a similar control group and/or for a group that received a different intervention with the same purpose. 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.

Data collection and analysis techniques

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. Within their guide, they answer various questions such as: what type of analysis do i need? 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. New york, ny: guilford ch degree ch degree ones in your ew of a research ion - first e meeting, research induction plan & statement of approval & responsible ch proposal & confirmation of collection & submission & sibilities, roles & & your sity research es, regulations & ch degree graduate rships & as davey research ch training program (rtp). Ic integrity & g research g your research ology & data icating g your thesis or hing & measuring ping your ntly asked the purposes of compliance with ethics and data es, 'data' means 'original information which is collected,Stored, accessed, used or disposed of during the course of ch, and the final report of the research findings'. Research methods may include the collection ation (data) which can be interpreted or analysed to s to your research questions or increase knowledge research topic. Different collection methods will require different types of tative ative g with your tative cal or quantitative information is obtained ch methods such as surveys of populations or ed experimental procedures. When recording it is important to include detailed information (eg dates of collection, methods of measurement, units ement) to minimise confusion.

Numerical data are ed on printed datasheets, then stored in some cases, data may initially be recorded by ers or specialised data recorders which can later aded to more secure devices. Data recorders can often up to record data remotely, without the requirement chers be present. If you are an external re student, contact your supervisor to arrangements for data analysis. The transcripts may also be treated as texts information may be recorded as photographic plates,Slides, computerised files or hand-drawn re and university has purchased qsr nvivo software licences for research degree students in the divisions of business; education,Arts and social sciences; and health software helps you access, manage, shape e detailed textual and/or multimedia data by removing manual classifying, sorting and arranging e virtually any qualitative or ation, from in-depth interview and focus group transcripts nts, field or case used for a wide range ing network and organisational analysis, action or ch, discourse analysis, grounded theory, conversation analysis,Ethnography, literature reviews, phenomenology and mixed methods you a student from a division which has re and you wish to use it to analyse your data, apply to have it installed on free of charge. Your division may offer training opportunities in g with your data or information you initially collect is often in a (spreadsheets of numerical data, transcripts of interviews, ptions of artefacts) which need to be summarised, interpreted ed before you can draw is often best to summarise information to identify ising helps you to compare information in a so that you (or your reader) does to sort through a lot of information to make comparisons. Interpreting interview data you can prepare g frequently-raised issues of interviewees under categories cal data can usually be atically, as means (averages), medians, modes or information is summarised, you will find it easier fy patterns and interpret meanings. Diagrams, photographs, maps, graphs) or tables (lists of written cal information) will enable you to demonstrate your s and tables must:Be numbered correctly referred to (by number) and relevant to the presented in a consistent a descriptive caption so that they can be understood if necessary (captions usually go above a table, and below must have axes labelled and all units of ation such as raw data tables, photographs of specimens, cts may be more appropriately inserted as provider no 00121b |. Content revision:thursday, 4 august site will work and look better in a browser that supports web standards, but it is accessible to any browser or internet ch degree ch degree ones in your ew of a research ion - first e meeting, research induction plan & statement of approval & responsible ch proposal & confirmation of collection & submission & sibilities, roles & & your sity research es, regulations & ch degree graduate rships & as davey research ch training program (rtp).