About Research Data
- DataThe term data refers to a collection of information. A more detailed definition includes types of data that combine to be the collected information such as numbers, words, pictures, video, audio, and concepts. Many definitions of data include the word fact, or facts, but this implies an inference about the data and not the data themselves. This occurs more often in the physical sciences. One may also see the word raw as a descriptor of the data. This description is used to separate data such as the number 42 from information such as at 42 one is 5 years older than one’s sibling. Once data are gathered, they are typically put into a format that can be analyzed by machine or human. The format can be a spreadsheet, notecards, or literary analysis software and serves to increase the ease of data analysis.
In the generic split of quantitative versus qualitative research, quantitative research gathers data that are in numerical form. The original data can be in nonnumerical form such as statements that are recoded on some specific numerical scale. Quantitative data separate into categories based on their measurement type— nominal (e.g., gender), ordinal (e.g., law school class rank), interval (e.g., degrees Fahrenheit), and ratio (e.g., degrees Kelvin).
Qualitative data are generally nonnumerical but have a greater variety of sources. Those data sources are generally categorized as verbal and nonverbal. Data are verbal if the majority of what is being analyzed is words. Verbal data sources include items such as personal diaries, letters, media reports, surveys/interviews, and fieldnotes. Within the group of interviews the data can come from in-depth/unstructured interviews, semi-structured interviews, structured interviews, questionnaires containing substantial open-ended comments, focus groups, and so on.
Nonverbal data sources include items such as student concept maps, kinship diagrams, pictures, video, film, art, and print advertisements. Each type of data and how it was collected has different strengths and weaknesses in relation to the research questions and analysis techniques. For example, nonparticipant observations from video collected through surveillance cameras potentially allow the researcher to collect data without influence in the field, but there are issues with the ethics of these observations.
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Credo Reference is an easy-to-use tool for starting research. Gather background information on your topic from hundreds of full-text encyclopedias, dictionaries, thesauri, quotations, and subject-specific titles, as well as 500,000+ images and audio files and over 1,000 videos.
Articles
- Could Big Data be the end of theory in science? A few remarks on the epistemology of data-driven scienceThe era of Big Data is claimed to herald the end of hypothesis‐driven research in favour of sophisticated algorithms uncovering information from huge data sets. But a closer look reveals that Big Data actually enhances the testing of hypothesis and experimentation, rather than replaces them.
- Data Science: A Comprehensive OverviewThe 21st century has ushered in the age of big data and data economy, in which data DNA , which carries important knowledge, insights, and potential, has become an intrinsic constituent of all data-based organisms. An appropriate understanding of data DNA and its organisms relies on the new field of data science and its keystone, analytics . Although it is widely debated whether big data is only hype and buzz, and data science is still in a very early phase, significant challenges and opportunities are emerging or have been inspired by the research, innovation, business, profession, and education of data science. This article provides a comprehensive survey and tutorial of the fundamental aspects of data science: the evolution from data analysis to data science, the data science concepts, a big picture of the era of data science, the major challenges and directions in data innovation, the nature of data analytics, new industrialization and service opportunities in the data economy, the profession and competency of data education, and the future of data science. This article is the first in the field to draw a comprehensive big picture, in addition to offering rich observations, lessons, and thinking about data science and analytics.
- Data Science in Statistics Curricula: Preparing Students to "Think with Data"A growing number of students are completing undergraduate degrees in statistics and entering the workforce as data analysts. In these positions, they are expected to understand how to use databases and other data warehouses, scrape data from Internet sources, program solutions to complex problems in multiple languages, and think algorithmically as well as statistically. These data science topics have not traditionally been a major component of undergraduate programs in statistics. Consequently, a curricular shift is needed to address additional learning outcomes. The goal of this article is to motivate the importance of data science proficiency and to provide examples and resources for instructors to implement data science in their own statistics curricula. We provide case studies from seven institutions. These varied approaches to teaching data science demonstrate curricular innovations to address new needs. Also included here are examples of assignments designed for courses that foster engagement of undergraduates with data and data science.
Got Data? What Are You Wondering About?
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Data analysis is not just for large organizations and large datasets; it's also for the average person. Learn how to put data to work in your own life - from charting your cell phone usage to personalizing your medical care or improving your exercise routine.
How to Summarize Data with Statistics
- Kanopy This link opens in a new windowConfront how ALL data has uncertainty, and why statistics is a powerful tool for reaching insights and solving problems. Begin by describing and summarizing data with the help of concepts such as the mean, median, variance, and standard deviation. Learn common statistical notation and graphing techniques, and get a preview of the programming language R, which will be used throughout the course.
Webpages
- Building a Data Culture Builds Better Revenue, TooAlation recently released the results of its Q1 2022 State of Data Culture Report. The company says the report “provides a regular assessment of the progress enterprises have made in establishing a data culture within their organizations, the challenges they face in embracing data-driven decision making, and key drivers for data and analytics.” We explore some of the report’s findings with Ashley Womack, director of corporate marketing at Alation.
- Fundamentals of Research Data and Variables: The Devil Is in the DetailsDesigning, conducting, analyzing, reporting, and interpreting the findings of a research study require an understanding of the types and characteristics of data and variables. Descriptive statistics are typically used simply to calculate, describe, and summarize the collected research data in a logical, meaningful, and efficient way. Inferential statistics allow researchers to make a valid estimate of the association between an intervention and the treatment effect in a specific population, based upon their randomly collected, representative sample data. Categorical data can be either dichotomous or polytomous. Dichotomous data have only 2 categories, and thus are considered binary. Polytomous data have more than 2 categories. Unlike dichotomous and polytomous data, ordinal data are rank ordered, typically based on a numerical scale that is comprised of a small set of discrete classes or integers...
- The right data strategy is the foundation for better customer experiencesThird-party cookies might be going the way of the dinosaur, but organizations will still need data about their customers’ online behaviors to deliver the personalized experiences they expect. This means a shift in data strategy to focus on multiple types of first-party data, collected with customer consent. First-party data is data that customers agree to share with brands, including their preferences, online behaviors, and personal information. And while first-party data is a rich source of intelligence, it often lives in silos and is unorganized, rendering much of it useless when it comes to helping the organization drive better decisions and actions.