How do you analyze data

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? 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. 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. 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. 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. 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 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. 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. 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. 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. Theme topic i’ve been thinking about recently is extent to which data analysis is an art versus a science. Since the notion of an algorithm or a computer program provides us with an extremely useful test for the depth of our knowledge about any given subject, the process of going from an art to a science means that we learn how to automate course, the phrase “analyze data” is far too general; it needs to be placed in a much more specific context. So choose your favorite specific context and consider this question: is there a way to teach a computer how to analyze the data generated in that context?

These are typically done using time series data of ambient pollution from central monitors and community-level counts of some health outcome (e. Similarly, there has been a lot of research into the statistical methodology for conducting time series studies and i would wager that as a result of that research we actually know something about what to do and what not to is our level of knowledge about the methodology for analyzing air pollution time series data to the point where we could program a computer to do the whole thing? Assume we basically start with a rectangular dataset with time series data on a health outcome (say, daily mortality counts in a major city), daily air pollution data, and daily data on other relevant variables (e. Need to check for missing data and maybe stop analysis if proportion of missing data is too high? Typically, some summary statistics for the data are reported along with the estimate + confidence interval for the air pollution association. I’ve left out the cleaning and preparation of the data here, which also involves making many choices. Well, i would argue that if we cannot completely automate a data analysis for a given context, then either we need to narrow the context, or we have some more statistical research to do. Thinking about how one might automate a data analysis process is a useful way to identify where are the major statistical gaps in a given area. Enable javascript to view the comments powered by ts powered by analysis is the process of interpreting the meaning of the data we have collected, organized, and displayed in the form of a table, bar chart, line graph, or other representation. In this teacher guide, you can learn the importance of utilizing data analysis with your students and strategies for teaching students how to conduct data and charts (631). Data analysis is the process of interpreting the meaning of the data we have collected, organized, and displayed in the form of a table, bar chart, line graph, or other representation. The process of collecting, organizing, and analyzing data is not always a simple, sequential process; sometimes a preliminary analysis of a data set may prompt us to look at the data in another way, or even to go back and collect additional data to test an emerging hypothesis. For example, students could survey their classmates on how they are transported to school (such as by car, by bus, by foot, or another way), and then display the data in a circle graph. After analyzing the data in this graph, students might look at the data in a different way. In all grades, students look at graphical displays and describe them by identifying aspects such as the greatest value, the least value, and the relationship of one data point to another. Students in the intermediate grades learn how to summarize or characterize a data set in greater depth by determining the range and two measures of center, the mode and median.

High-school students should be able to design their own investigations that include effective sampling, representative data, and an unbiased interpretation of the results. At every grade level, you should encourage students to think about the meaning of the data they have collected and displayed. The ability to make inferences and predictions based on data is a critical skill students need to develop. In studying data and statistics, students can also learn that solutions to some problems depend on assumptions and have some degree of uncertainty. By paying close attention to patterns, the stories behind outliers, relationships between and among data sets, and the external factors that may have affected the data, students may come to have a deeper understanding of the crucial distinction between theory and 're viewing your - 1st of 3 free 2 more resources at no cost, and then subscribe for full just $6. Your independent variable on the x-axis of your graph and the dependent variable on the some time to carefully review all of the data you have collected from your experiment. Think about what you have discovered and use your data to help you explain why you think certain things ations and summarizing , you will need to perform calculations on your raw data in order to get the results from which you will generate a conclusion. Any calculations that are necessary for you to analyze and understand the data from your calculations from known formulas that describe the relationships you are testing. You have more than one set of data, show each series in a different color or symbol and include a legend with clear ent types of graphs are appropriate for different experiments. To generate a time series plot with your choice of x-axis units, make a separate data column that contains those units next to your dependent variable. Is a sample excel spreadsheet (also available as a pdf) that contains data analysis and a analysis makes for a good data analysis chart? A good chart, you should answer "yes" to every there sufficient data to know whether your hypothesis is correct? A good graph, you should answer "yes" to every you selected the appropriate graph type for the data you are displaying? All rights uction of material from this website without written permission is strictly of this site constitutes acceptance of our terms and conditions of fair data analysis process: 5 steps to better decision most businesses and government agencies, lack of data isn’t a problem. In fact, it’s the opposite: there’s often too much information available to make a clear so much data to sort through, you need something more from your data:You need to know it is the right data for answering your question;. Need to draw accurate conclusions from that data; need data that informs your decision making short, you need better data analysis.

With the right data analysis process and tools, what was once an overwhelming volume of disparate information becomes a simple, clear decision improve your data analysis skills and simplify your decisions, execute these five steps in your data analysis process:Step 1: define your your organizational or business data analysis, you must begin with the right question(s). Decide what to the government contractor example, consider what kind of data you’d need to answer your key question. Decide how to measure ng about how you measure your data is just as important, especially before the data collection phase, because your measuring process either backs up or discredits your analysis later on. Your question clearly defined and your measurement priorities set, now it’s time to collect your data. As you collect and organize your data, remember to keep these important points in mind:Before you collect new data, determine what information could be collected from existing databases or sources on hand. Collect this data ine a file storing and naming system ahead of time to help all tasked team members collaborate. This process saves time and prevents team members from collecting the same information you need to gather data via observation or interviews, then develop an interview template ahead of time to ensure consistency and save your collected data organized in a log with collection dates and add any source notes as you go (including any data normalization performed). This practice validates your conclusions down the you’ve collected the right data to answer your question from step 1, it’s time for deeper data analysis. Begin by manipulating your data in a number of different ways, such as plotting it out and finding correlations or by creating a pivot table in excel. A pivot table lets you sort and filter data by different variables and lets you calculate the mean, maximum, minimum and standard deviation of your data – just be sure to avoid these five pitfalls of statistical data you manipulate data, you may find you have the exact data you need, but more likely, you might need to revise your original question or collect more data. Either way, this initial analysis of trends, correlations, variations and outliers helps you focus your data analysis on better answering your question and any objections others might this step, data analysis tools and software are extremely helpful. If you need a review or a primer on all the functions excel accomplishes for your data analysis, we recommend this harvard business review 5: interpret analyzing your data and possibly conducting further research, it’s finally time to interpret your results. Meaning that no matter how much data you collect, chance could always interfere with your you interpret the results of your data, ask yourself these key questions:Does the data answer your original question? Your interpretation of the data holds up under all of these questions and considerations, then you likely have come to a productive conclusion. The only remaining step is to use the results of your data analysis process to decide your best course of following these five steps in your data analysis process, you make better decisions for your business or government agency because your choices are backed by data that has been robustly collected and analyzed. With practice, your data analysis gets faster and more accurate – meaning you make better, more informed decisions to run your organization most to draw the most accurate conclusions from your data?

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