File Name: quantitative data analysis and interpretation .zip
Click here to check the full schedule: Library Complete Schedule. Quantitative research focuses on gathering numerical data and generalizing it across groups of people or to explain a particular phenomenon.
It is time to learn about the effects of your environmental education EE program! What do the patterns, differences, and relationships in the data suggest about how well the program is achieving its objectives? There are many different types of analyses that vary in complexity. To help you determine which type of analysis to choose, consider the following:.
It is time to learn about the effects of your environmental education EE program! What do the patterns, differences, and relationships in the data suggest about how well the program is achieving its objectives? There are many different types of analyses that vary in complexity.
To help you determine which type of analysis to choose, consider the following:. The paragraphs below discuss types of analyses according to whether you collected quantitative or qualitative data, and point you to software that can be used to analyze these data.
Based on the information and resources in this section you should gain insight into what type of analysis to conduct for your evaluation, and how to conduct at least some of these analyses yourself.
Before you can analyze quantitative data, you will need to code data and enter data into a spreadsheet or a statistical analysis program see Step 5.
You will want to begin with descriptive analyses of your data. These analyses are called descriptive because they allow you to summarize large amounts of information. Descriptive statistics include frequencies counts , percents, ranks, measures of central tendency e. For example, you can calculate the mean response to a question or the percent of respondents who answered a question in a particular way.
When a difference is statistically significant, it means that the difference is probably not due to chance. This does not tell you if the difference is meaningful or trivial! Effect sizes provide you with a quantitative way to assess to what extent a significant difference may also be substantively important. To learn more about effect sizes, review the section on power analysis, statistical significance, and effect size. After conducting descriptive analyses, you may want to conduct more complex inferential analyses.
These analyses include testing for significant differences. For example, you can test whether participants in your program scored higher on a multiple choice exam than individuals in a control group. For example, were female participants more likely to correctly answer the questions than male participants? Software programs are invaluable in carrying out most quantitative statistical procedures. In fact, many of the procedures would be impossible for most individuals to carry out without such software.
Though sample sizes in qualitative data tend to be smaller, the data sets themselves are typically large, complicated, and often messy to organize and analyze. In qualitative analysis, there are also fewer rules and standard procedures to guide the process than in quantitative analysis. However, good qualitative analysis, like good quantitative analysis, is highly systematic and disciplined. Most of the qualitative analysis you are likely to conduct for an evaluation is what is called content analysis.
Content analysis involves systematically analyzing the content of data, breaking it into meaningful pieces, and organizing those pieces in a way that allows their characteristics and meaning to be better understood. This step involves selecting, focusing, condensing, and transforming data. The process should be guided by thinking about which data best answer the evaluation questions.
This involves creating an organized, compressed way of arranging data such as through a diagram, chart, matrix, or text. The display should help facilitate identifying themes, patterns, and connections that help answer your evaluation questions. This step usually involves coding, where you mark passages of text or parts of images or sections of a video, etc. During this last step, revisit the data many times to verify, test, or confirm the themes and patterns you have identified.
While the three steps provide a good overall guideline, you may need to cycle through the steps repeatedly. Qualitative analysis is a cyclical and iterative process, with many rounds of investigating evidence, modifying hypotheses, and revisiting the data from a new light.
You will need to reexamine data repeatedly as new questions and connections emerge and as you gain a more thorough understanding of the information you collected. Throughout the process of examining and reexamining data, concentrate on the following:.
Using a computer program can enrich and improve the process of qualitative analysis by speeding up some analysis tasks e. Software cannot, however, help you pick out the meaningful codes, themes, or categories, or eliminate the challenging intellectual work of qualitative analysis Frechtling and Sharp, There are many software programs available for purchase and there are some that can be downloaded for free.
Not surprisingly, free programs tend not to be as powerful or perform as many functions. They also are not updated as frequently. Nevertheless, such programs may be worth looking into if their capabilities meet your needs.
The following resources will help you sift through many of the qualitative analysis software programs available to find one that will best meet your needs:. Barry, C. Fitzpatrick, J. Sanders, and B. Frechtling, J. Seidel, J. Qualitative Data Analysis. The Ethnograph v5 Manual, Appendix E. Miles, M.
B, and Huberman, A. Qualitative Data Analysis, 2nd Ed. Newbury Park, CA: Sage. Skip to main content. Step 6: Analyze Data It is time to learn about the effects of your environmental education EE program!
What type of analysis do I need? To help you determine which type of analysis to choose, consider the following: Whether you collected quantitative or qualitative data, The resources expertise, time, funding you have available for analysis, The evaluation questions you want to answer i.
Descriptive Statistics You will want to begin with descriptive analyses of your data. Statistical significance is not the same as substantive significance! Inferential Statistics After conducting descriptive analyses, you may want to conduct more complex inferential analyses.
The resources below can help you learn more about how to analyze quantitative data: Analyzing Quantitative Data. University of Wisconsin Extension Program, Development and Evaluation Unit Beginner This guide presents an introduction to descriptive statistics and explains how to use them in evaluation. Inferential Statistics Trochim, W.
Beginner This page describes how inferential statistics can be used in outcome evaluations. Guide to Program Evaluation for Aquatic Educators. It presents the steps involved in analysis, summarizes the purpose of statistical analysis, and explains how to reach conclusions from the analysis.
A table gives an overview of the different types of statistical analyses and their uses in evaluations of EE and aquatic education programs. Hyper Stat Online Statistics Textbook Beginner Intermediate This online statistics textbook, available for free, offers a detailed discussion of how to use both descriptive and inferential statistics. Be sure to analyze data at the appropriate level! Data reduction This step involves selecting, focusing, condensing, and transforming data.
Data display This involves creating an organized, compressed way of arranging data such as through a diagram, chart, matrix, or text. Conclusion drawing and verification During this last step, revisit the data many times to verify, test, or confirm the themes and patterns you have identified.
Throughout the process of examining and reexamining data, concentrate on the following: Patterns, recurring themes, similarities, and differences Ways in which these patterns or lack thereof help answer evaluation questions Any deviations from these patterns and possible explanations for these Interesting or particularly insightful stories Specific language people use to describe phenomena To what extent patterns are supported by past studies or other evaluations and if not, what might explain the differences To what extent patterns suggest that additional data may need to be collected The following resources will help guide you through the process of qualitative data analysis: Analyzing Qualitative Data.
The steps include focusing the analysis, categorizing data, identifying patterns and connections, and interpretation. The guide offers many examples, useful tips, and pitfalls to avoid. Sharp, National Science Foundation. The chapter also provides advice on how to judge the quality of qualitative analysis, and includes a list of tips.
Online QDA website Quantitative Data Analysis Beginner Intermediate Advanced This website provides a wealth of information to support qualitative data analysis, including introductory background and extensive information on different software packages. Before You Get Started Step 2. Program Logic. Goals of Evaluation Step 4.
Evaluation Design. Collecting Data Step 6. Analyzing Data Step 7. Reporting Results.
A Guide To The Methods, Benefits & Problems of The Interpretation of Data
Data analysis is a process of inspecting, cleansing , transforming , and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. In today's business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively. Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. EDA focuses on discovering new features in the data while CDA focuses on confirming or falsifying existing hypotheses. Predictive analytics focuses on the application of statistical models for predictive forecasting or classification, while text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a species of unstructured data. All of the above are varieties of data analysis.
Quantitative data analysis is one of those things that often strikes fear into students when they reach the research stage of their degree. As we discussed earlier, quantitative data analysis is powered by statistical analysis. But first — a quick detour:. For example, if you were interested in researching Tesla owners in the US, then the population would be all Tesla owners in the US.
Home Consumer Insights Market Research. Quantitative data is defined as the value of data in the form of counts or numbers where each data-set has an unique numerical value associated with it. This data is any quantifiable information that can be used for mathematical calculations and statistical analysis, such that real-life decisions can be made based on these mathematical derivations. This data can be verified and can also be conveniently evaluated using mathematical techniques.
By Saul McLeod , updated Jump to Quantitative Research Data. Qualitative research is the process of collecting, analyzing, and interpreting non-numerical data, such as language.
Data analysis and interpretation have now taken center stage with the advent of the digital age… and the sheer amount of data can be frightening. Through the art of streamlined visual communication, data dashboards permit businesses to engage in real-time and informed decision making, and are key instruments in data interpretation. Data interpretation refers to the implementation of processes through which data is reviewed for the purpose of arriving at an informed conclusion. The interpretation of data assigns a meaning to the information analyzed and determines its signification and implications. The importance of data interpretation is evident and this is why it needs to be done properly.
Стратмор сидел за современным письменным столом с двумя клавиатурами и монитором в расположенной сбоку нише. Стол был завален компьютерными распечатками и выглядел каким-то чужеродным в этом задернутом шторами помещении. - Тяжелая неделя? - спросила. - Не тяжелей, чем обычно. - Стратмор пожал плечами. - Фонд электронных границ замучил неприкосновенностью частной жизни и переписки.
Он называл ее… - Речь его стала невнятной и едва слышной. Медсестра была уже совсем близко и что-то кричала Беккеру по-испански, но он ничего не слышал. Его глаза не отрывались от губ Клушара.
В панике она сразу же представила себе самое худшее. Ей вспомнились мечты коммандера: черный ход в Цифровую крепость и величайший переворот в разведке, который он должен был вызвать. Она подумала о вирусе в главном банке данных, о его распавшемся браке, вспомнила этот странный кивок головы, которым он ее проводил, и, покачнувшись, ухватилась за перила. Коммандер.
Справа бесконечной чередой мелькали кадры, запечатлевшие последние минуты Танкадо: выражение отчаяния на его лице, вытянутую руку, кольцо, поблескивающее на солнце.