Data Collection In Research: Data Processing And Analysis
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Data Collection--DC is simply how information is gathered. Data collection--DC is the process of gathering and measuring information on variables of interest, in an established systematic fashion that enables one to answer stated research questions, test hypotheses, and evaluate outcomes. The DC component of research is common to all fields of study including physical and social sciences, humanities, business, etc. While methods vary by discipline, the emphasis on ensuring accurate and honest collection remains the same. The goal for all DC is to capture quality evidence that then translates to rich datas analysis and allows the building of a convincing and credible answer to questions that have been posed. Regardless of the field of study or preference for defining datas (quantitative, qualitative), accurate DC is essential to maintaining the integrity of research. Both the selection of appropriate DC instruments (existing, modified, or newly developed) and clearly delineated instructions for their correct use reduce the likelihood of errors occurring. A formal DC process is necessary as it ensures that datas gathered are both defined and accurate and that subsequent decisions based on arguments embodied in the findings are valid. The process provides both a baseline from which to measure and in certain cases a target on what to improve. Generally there are three types of DC and they are 1. Surveys: Standardized paper-and-pencil or phone questionnaires that ask predetermined questions. 2. Interviews: Structured or unstructured one-on-one directed conversations with key individuals or leaders in a community. 3. Focus groups: Structured interviews with small groups of like individuals using standardized questions, follow-up questions, and exploration of other topics that arise to better understand participants. Consequences from improperly collected data include: Inability to answer research questions accurately, Inability to repeat and validate the study. Distorted findings result in wasted resources and can mislead other researchers to pursue fruitless avenues of investigation. This compromises decisions for public policy, and causes harm to human participants and animal subjects. While the degree of impact from faulty DC may vary by discipline and the nature of investigation, there is the potential to cause disproportionate harm when these research results are used to support public policy recommendations.
Essentially collecting datas means putting the design for collecting information into operation. After one has decided how one is going to get information – whether by direct observation, interviews, surveys, experiments and testing, or other methods – and have to implement the plan. There’s a bit more to collecting datas, however. If one is conducting observations, for example, one would have to define what one is observing and arrange to make observations at the right times, so one actually observe what one need to. It is important to record the observations in appropriate ways and organize them so they’re optimally useful. Recording and organizing datas may take different forms, depending on the kind of information one is collecting. The way one collects datas would relate to how one is planning to analyze and use it. Regardless of what method one decide to use, recording should be done concurrent with DC if possible, or soon afterwards, so that nothing gets lost and memory doesn’t fade. Some of the things one might do with the information collected include: Gathering together information from all sources and observations; Making photocopies of all recording forms, records, audio or video recordings, and any other collected materials, to guard against loss, accidental erasure, or other problems; Entering narratives, numbers, and other information into a computer program, where they can be arranged and/or worked on in various ways; Performing 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 numbers; Transcribing (making an exact, word-for-word text version of) the contents of audio or video recordings; Coding datas (translating datas, particularly qualitative datas 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); Organizing datas in ways that make them easier to work with. How one would do this would depend on the research design and the evaluation questions. One could group observations by the dependent variable (indicator of success) they relate to, by individuals or groups of participants, by time, by activity, etc. One might also want to group observations in several different ways, so that one could study interactions among different variables. There 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. This could be a program, method, system, or other action. A dependent variable is what may change as a result of the independent variable or intervention. A dependent variable could be a behavior, outcome, or other condition. A smoking cessation program, for example, is an independent variable that may change group members’ smoking behavior, the primary dependent variable.
Analyzing information involves examining it in ways that reveal the relationships, patterns, trends, etc. that can be found within it. That may mean subjecting it to statistical operations that can tell not only what kinds of relationships seem to exist among variables, but also to what level one can trust the answers one is getting. It may mean comparing information to that from other groups (a control or comparison group, statewide figures, etc.), to help draw some conclusions from the datas. The point, in terms of evaluation is to get an accurate assessment in order to better understand the work and its effects on those one is concerned with, or in order to better understand the overall situation. There are two kinds of datas one is apt to be working with, although not all evaluations will necessarily include both. Quantitative datas refer to the information that is collected as, or can be translated into, numbers, which can then be displayed and analyzed mathematically. Qualitative datas are collected as descriptions, anecdotes, opinions, quotes, interpretations, etc., and are generally either not able to be reduced to numbers, or are considered more valuable or informative if left as narratives. Quantitative and qualitative information needs to be analyzed differently. Quantitative datas are typically collected directly as numbers. Some examples include: The frequency (rate, duration) of specific behaviors or conditions; Test scores (e.g., scores/levels of knowledge, skill, etc.); Survey results (e.g., reported behavior, or outcomes to environmental conditions; ratings of satisfaction, stress, etc.); Numbers or percentages of people with certain characteristics in a population (diagnosed with diabetes, unemployed, Spanish-speaking, under age 14, grade of school completed, etc.). Datas can also be collected in forms other than numbers, and turned into quantitative datas 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. For instance, community initiatives often want to document the amount and intensity of environmental changes they bring about – the new programs and policies that result from their efforts. Whether or not this kind of translation is necessary or useful depends on the nature of what one is observing and on the kinds of questions the evaluation is meant to answer. Quantitative datas are 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” datas 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. They can allow to compare those changes to one another, to changes in another variable, or to changes in another population. They might be able to tell at a particular degree of reliability, whether those changes are likely to have been caused by the intervention or program, or by another factor, known or unknown. And they can identify relationships among different variables, which may or may not mean that one causes another.
Unlike numbers or “hard datas,” qualitative information tends to be “soft,” meaning it can’t always be reduced to something definite. That is in some ways a weakness, but it’s also a strength. A number may tell how well a student did on a test; the look on her face after seeing her grade, however, may tell even more about the effect of that result on her. That look can’t be translated to a number, nor can a teacher’s knowledge of that student’s history, progress, and experience, all of which go into the teacher’s interpretation of that look. And that interpretation may be far more valuable in helping that student succeed than knowing her grade or numerical score on the test. Qualitative datas 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.g., importance, satisfaction, ease of use). The challenges of translating qualitative into quantitative datas have to do with the human factor. Even if most people agree on what 1 (lowest) or 5 (highest) means in regard to rating “satisfaction” with a program, ratings of 2, 3, and 4 may be very different for different people. Furthermore, the numbers say nothing about why people reported the way they did. One may dislike the program because of the content, the facilitator, the time of day, etc. The same may be true when one is counting instances of the mention of an event, such as the onset of a new policy or program in a community based on interviews or archival records. Where one person might see a change in program he considers important another may omit it due to perceived unimportance. Qualitative datas can sometimes tell things that quantitative datas can’t. It may reveal why certain methods are working or not working, whether part of what one is doing conflicts with participants’ culture, what participants see as important, etc. It may also show patterns – in behavior, physical or social environment, or other factors – that the numbers in the quantitative datas don’t, and occasionally even identify variables that researchers weren’t aware of. It is often helpful to collect both quantitative and qualitative information. Quantitative 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 datas is generally accomplished by methods more subjective – dependent on people’s opinions, knowledge, assumptions, and inferences (and therefore biases) – than that of quantitative datas. 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. However 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 analyses.
Why should one collect and analyze datas for evaluation? Part of the answer here is that not every organization, particularly small community-based or non-governmental ones, will necessarily have extensive resources to conduct a formal evaluation. They may have to be content with less formal evaluations, which can still be extremely helpful in providing direction for a program or intervention. An informal evaluation will involve some datas gathering and analysis. This DC and sense making is critical to an initiative and its future success, and has a number of advantages. The datas can show whether there was any significant change in the dependent variable(s) hoped to influence. Collecting and analyzing datas helps to see whether the intervention brought about the desired results. The term “significance” has a specific meaning when one is discussing statistics. The level of significance of a statistical result is the level of confidence one can have in the answer got. Generally, researchers don’t consider a result significant unless it shows at least a 95% certainty that it’s correct (called the .05 level of significance, since there’s a 5% chance that it’s wrong). The level of significance is built into the statistical formulas: once one gets a mathematical result, a table (or the software being used) will tell the level of significance. Thus, if datas analysis finds that the independent variable (the intervention) influenced the dependent variable at the .05 level of significance, it means there’s a 95% probability or likelihood that the program or intervention had the desired effect. The .05 level is generally considered a reasonable result, and the .01 level (99% probability) is considered about as close to certainty as one is likely to get. A 95% level of certainty doesn’t mean that the program works on 95% of participants, or that it will work 95% of the time. It means that there’s only a 5% possibility that it isn’t actually what’s influencing the dependent variable(s) and causing the changes that it seems to be associated with. They can uncover factors that may be associated with changes in the dependent variable(s). Datas 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. This can be used to identify key aspects of implementation. They can show connections between or among various factors that may have an effect on the results of evaluation. Some types of statistical procedures look for connections (“correlations” is the research term) among variables. Certain dependent variables may change when others do. These changes may be similar – i.e., both variables increase or decrease (e.g., as children’s proficiency at reading increases, the amount of reading they do also increases). Or the opposite may be observed – i.e. the two variables change in opposite directions (as the amount of exercise they engage in increases, peoples’ weight decreases). Correlations don’t mean that one variable causes another, or that they both have the same cause, but they can provide valuable information about associations to expect in an evaluation. They can help shed light on the reasons that the work was effective or, perhaps, less effective than one had hoped. By combining quantitative and qualitative analysis, one 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 approach for the population – these as well as other factors that influence success can be highlighted by careful DC and analysis. This knowledge gives a basis for adapting and changing what to make it more likely one would achieve the desired outcomes in the future. They can provide with credible evidence to show stakeholders that the program is successful, or that one have uncovered, and are addressing limitations. Stakeholders, such as funders and community boards, want to know their investments are well spent. Showing evidence of intermediate outcomes (e.g. new programs and policies) and longer-term outcomes (e.g., improvements in education or health indicators) is becoming increasingly important to receiving – and retaining – funding. Their use shows that one is serious about evaluation and about improving the work. Being a good trustee or steward of community investment includes regular review of datas regarding progress and improvement. They can show the field what one is learning, and thus pave the way for others to implement successful methods and approaches. In that way, it would be helping to improve community efforts and, ultimately, quality of life for people who benefit.
Do note that these above information are given only as a guideline and would vary based on the requirement of the project. However even with these instructive steps it is not possible for researchers to do a good DC like how our expert researchers do. This is why you need the help of our research services company. So why wait? Read below for more details.
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