What is the difference between analyzing and evaluating

It is important to periodically assess and adapt your activities to ensure they are as effective as they can be. Evaluation can help you identify areas for improvement and ultimately help you realize your goals more efficiently. Additionally, when you share your results about what was more and less effective, you help advance environmental education. The information you collect allows you to better communicate your program's impact to others, which is critical for public relations, staff morale, and attracting and retaining support from current and potential funders.

Why conduct evaluations? Evaluations fall into one of two broad categories: formative and summative. Formative evaluations are conducted during program development and implementation and are useful if you want direction on how to best achieve your goals or improve your program.

Summative evaluations should be completed once your programs are well established and will tell you to what extent the program is achieving its goals. Norland, E. From education theory.. Pancer, s. Rossi R H. Evaluation: a systematic approach Thousand Oaks. For additional information on the differences between outcomes and impacts, including lists of potential EE outcomes and impacts, see MEERA's Outcomes and Impacts page. A well-planned and carefully executed evaluation will reap more benefits for all stakeholders than an evaluation that is thrown together hastily and retrospectively.

Though you may feel that you lack the time, resources, and expertise to carry out an evaluation, learning about evaluation early-on and planning carefully will help you navigate the process. MEERA provides suggestions for all phases of an evaluation. But before you start, it will help to review the following characteristics of a good evaluation list adapted from resource formerly available through the University of Sussex, Teaching and Learning Development Unit Evaluation Guidelines and John W.

Evans' Short Course on Evaluation Basics :. Your evaluation should be crafted to address the specific goals and objectives of your EE program. However, it is likely that other environmental educators have created and field-tested similar evaluation designs and instruments. Rather than starting from scratch, looking at what others have done can help you conduct a better evaluation.

It ensures that diverse viewpoints are taken into account and that results are as complete and unbiased as possible. Input should be sought from all of those involved and affected by the evaluation such as students, parents, teachers, program staff, or community members. Your contribution can help change lives. Donate now.

Sixteen training modules for teaching core skills. Learn more. Essentially, collecting data means putting your design for collecting information into operation. 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. Analyzing information involves examining it in ways that reveal the relationships, patterns, trends, etc.

It may mean comparing your information to that from other groups a control or comparison group, statewide figures, etc. Quantitative data refer to the information that is collected as, or can be translated into, numbers, which can then be displayed and analyzed mathematically. Qualitative data are collected as descriptions, anecdotes, opinions, quotes, interpretations, etc. As you might expect, quantitative and qualitative information needs to be analyzed differently.

Quantitative data are typically collected directly as numbers. Some examples include:. Data 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. 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.

Quantitative 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. 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 you to compare those changes to one another, to changes in another variable, or to changes in another population. They might be able to tell you, at a particular degree of reliability, whether those changes are likely to have been caused by your 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.

A number may tell you how well a student did on a test; the look on her face after seeing her grade, however, may tell you even more about the effect of that result on her. And that interpretation may be far more valuable in helping that student succeed than knowing her grade or numerical score on the test. Qualitative 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.

The challenges of translating qualitative into quantitative data have to do with the human factor. 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.

Where one person might see a change in program he considers important another may omit it due to perceived unimportance. 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.

Be aware, however, that quantitative analysis is influenced by a number of subjective factors as well. 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 data gathering and analysis. This data collection and sensemaking is critical to an initiative and its future success, and has a number of advantages. The level of significance of a statistical result is the level of confidence you can have in the answer you get.

Thus, if data analysis finds that the independent variable the intervention influenced the dependent variable at the. 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. Which of these approaches you take depends on your research purposes. Both approaches are legitimate, but ongoing data collection and review can particularly lead to improvements in your work.

Who should actually collect and analyze data also depends on the form of your evaluation. Analysis also could be accomplished by a participatory process. 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 conclusions.

We've previously discussed designing an observational system to gather information. There are other excellent possibilities for analysis besides statistical procedures, however. A few include:. They may or may not be socially significant i. Among American teenagers, for instance, there is probably a fairly high correlation between an increase in body size and an understanding of algebra.

This is not because one causes the other, but rather the result of the fact that American schools tend to begin teaching algebra in the seventh, eighth, or ninth grades, a time when many , , and year-olds are naturally experiencing a growth spurt.

User assumes all risk of use, damage, or injury. You agree that we have no liability for any damages. What is Analyzing? The following are the six types of analyses: Descriptive: It is a quantitative description of commonly large data which basically demands less effort as compared to the other types.

Exploratory: This type seeks to reveal data connections. Inferential: It tests theories and generally tests the responses of a sample population. Predictive: This analyzes current and past information to predict future values. Causal: Analyzing causality entails testing how a variable is affected by another. Mechanistic: As compared to the other types, mechanistic analysis demands the most effort as exact changes of variables among individual objects need to be tested.

What is Evaluating? The following are the two types of evaluations: Formative Evaluation -Needs Assessment: Evaluates who or what need certain training as well as the skill sets that still need to be learned.

Summative Evaluation -Outcome Evaluation: Determines to what extent are the goals being met such as the degree of addition or modification of knowledge, skill, values, and the like in a short-term setting. Difference Between Analyzing and Evaluating Process in Analyzing and Evaluating The main difference is that analyzing involves breaking a concept into its parts for better interpretation while evaluating requires determination of significance.

Inference of Analyzing and Evaluating The inference from analyses concerns interpretations as to implications, meanings, and justifications. Sequence in Analyzing and Evaluating Generally, analyzing comes first before evaluating.

Compulsory Results in Analyzing and Evaluating Typically, the output of an evaluation should be the resulting quality. Length of Mental Process in Analyzing and Evaluating Analyzing usually includes a longer thinking process as it deals with segmentation and classification while evaluating basically deals with the conclusion.

Testing of Analyzing and Evaluating Evaluating is more related with testing as compared to analyzing. Output in Analyzing and Evaluating The end-product of an evaluation is a conclusion whereas the result of an analysis is a better understanding.

Subjectivity in Analyzing and Evaluating A subjective perspective is more likely to influence the evaluating process as compared to the analyzing process since making judgments may involve emotions. Academic Studies in Analyzing and Evaluating Analyzing is more often done in academic researches as compared to evaluating as the academe is usually concerned with in-depth studies.

This has also resulted in not being able to differentiate between the two terms. Analyzing is when the given data is studied, explained and broken down for further clarity. This process is widely used in the research field, along with academic fields that deal with the study of data like data science. Evaluating is finding the importance or the power of the value.

This process is done after analyzing for concluding the study on the data. Therefore, evaluating is a conclusive process. Obtaining a result in analyzing is not necessary since this process only helps us understand the data better.

But this is not the case for evaluating. Since the process is conclusive, obtaining a result is of utmost importance. Both the processes are required to obtain complete information about a data and to be able to use the data for further researches or evaluation of other data. Skip to content Analyzing and evaluating are two terms that go hand in hand.