USING TECHNOLOGY TO COMPUTE AND ANALYSE DATA


Technology is the making, usage, and knowledge of tools, machines, techniques, crafts, systems or methods of organization in order to solve a problem or perform a specific function. It can also refer to the collection of such tools, machinery, and procedures. Technologies significantly affect human as well as other animal species’ ability to control and adapt to their natural environments. The word technology comes from Greek (technología); and from (téchnē), meaning “art, skill, craft”, The term can either be applied generally or to specific areas: examples include construction technology, medical technology, and information technology.
The human species’ use of technology began with the conversion of natural resources into simple tools. The prehistorical discovery of the ability to control fire increased the available sources of food and the invention of the wheel helped humans in travelling in and controlling their environment. Recent technological developments, including the printing press, the telephone, and the Internet, have lessened physical barriers to communication and allowed humans to interact freely on a global scale. However, not all technology has been used for peaceful purposes; the development of weapons of ever-increasing destructive power has progressed throughout history, from clubs to nuclear weapons.
Technology has affected society and its surroundings in a number of ways. In many societies, technology has helped develop more advanced economies (including today’s global economy) and has allowed the rise of a leisure class. Many technological processes produce unwanted by-products, known as pollution, and deplete natural resources, to the detriment of the Earth and its environment. Various implementations of technology influence the values of a society and new technology often raises new ethical questions. Examples include the rise of the notion of efficiency in terms of human productivity, a term originally applied only to machines, and the challenge of traditional norms.
Data analysis
Data Analysis is the domain from which the data are harvested is a science or an engineering field. Data processing and information systems are considered terms that are too broad and the more specialized term data analysis is typically used. This is a focus on the highly-specialized and highly-accurate algorithmic derivations and statistical calculations that are less often observed in the typical general business environment.
Using technology to compute and analyses data is any process by which through technological means, computer program are use to enter data and summaries, analyses or otherwise convert data into usable information. The process may be automated and run on a computer. It involves recording, analyzing, sorting, summarizing, calculating, disseminating and storing data. Because data are most useful when well-presented and actually informative, data-processing systems are often referred to as information systems. Nevertheless, the terms are roughly synonymous, performing similar conversions; data-processing systems typically manipulate raw data into information, and likewise information systems typically take raw data as input to produce information as output. Data processing may or may not be distinguished from data conversion, when the process is merely to convert data to another format, and does not involve any data manipulation.
Type of data
Data can be of several types
Quantitative data data is a number
o Often this is a continuous decimal number to a specified number of significant digits
o Sometimes it is a whole counting number
Categorical data data one of several categories
Qualitative data data is a pass/fail or the presence or lack of a characteristic
The process of data analysis
Data analysis and computation is a process that uses technological softwares, within which several phases can be distinguished:
Data cleaning
Data cleaning is an important procedure during which the data are inspected, and erroneous data are—if necessary, preferable, and possible—corrected. Data cleaning can be done during the stage of data entry. If this is done, it is important that no subjective decisions are made. The guiding principle provided by Adèr (ref) is: during subsequent manipulations of the data, information should always be cumulatively retrievable. In other words, it should always be possible to undo any data set alterations. Therefore, it is important not to throw information away at any stage in the data cleaning phase. All information should be saved (i.e., when altering variables, both the original values and the new values should be kept, either in a duplicate data set or under a different variable name), and all alterations to the data set should carefully and clearly documented, for instance in a syntax or a log.
Initial data analysis
The most important distinction between the initial data analysis phase and the main analysis phase, is that during initial data analysis one refrains from any analysis that are aimed at answering the original research question. The initial data analysis phase is guided by the following four questions:[3]
Quality of data
The quality of the data should be checked as early as possible. Data quality can be assessed in several ways, using different types of analyses: frequency counts, descriptive statistics (mean, standard deviation, median), normality (skewness, kurtosis, frequency histograms, normal probability plots), associations (correlations, scatter plots).
Other initial data quality checks are: The choice of analyses to assess the data quality during the initial data analysis phase depends on the analyses that will be conducted in the main analysis phase and the type of technological package adopted
Quality of measurements
The quality of the measurement instruments should only be checked during the initial data analysis phase when this is not the focus or research question of the study. One should check whether structure of measurement instruments corresponds to structure reported in the literature.
There are two ways to assess measurement quality:
• Confirmatory factor analysis
• Analysis of homogeneity
Lastly using technology to compute and analyzed data have gone a long way to change the face of global data computing and analyses. In today’s world a whole new field of opportunities now abounds with the resultant use of technological applications to data management.

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