What is quantitative data

Bivariate dataanalysis of single-variable datapictures of single-variable databivariate dataprobabilityfactorials, permutations, and combinationsin the real ative v. Quantitative data is information about quantities; that is, information that can be measured and written down with numbers. Some examples of quantitative data are your height, your shoe size, and the length of your fingernails. You've got to be close to breaking the ative data is information about qualities; information that can't actually be measured. Some examples of qualitative data are the softness of your skin, the grace with which you run, and the color of your eyes. However, try telling photoshop you can't measure color with 's a quick look at the difference between qualitative and quantitative data. The way we typically define them, we call data 'quantitative' if it is in and 'qualitative' if it is ative research is empirical research where the data are not in the form of numbers (punch, 1998, p. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to interest in qualitative data came about as the result of the dissatisfaction of some psychologists (e. Since psychologists study people, the traditional approach to science is not seen as an appropriate way of carrying out research, since it fails to capture the totality of human experience and the essence of what it is to be human. Why’ a particular phenomenon, or behavior, operates as it does in a particular s (used to obtain qualitative data). Good example of a qualitative research method would be unstructured interviews which generate qualitative data through the use of open questions. This helps the researcher develop a real sense of a person’s understanding of a that qualitative data could be much more than or text.

The researcher does leave the field with mountains of empirical data and then easily write up her findings. Qualitative interpretations are constructed, and various techniques can be used to make sense of the data, such t analysis, grounded theory (glaser & strauss, 1967), thematic analysis (braun & clarke, 2006) or discourse can be understood adequately only if they are seen in context. Therefore, qualitative research is an interactive process in which the persons studied teach the researcher about their qualitative researcher is an integral part of the data, without the active participation of the researcher, no data design of the study evolves during the research, and can be adjusted or changed as it the qualitative researcher, there is no single reality, it is subjective and exist only in reference to the is data driven, and emerges as part of the research process, evolving from the data as they are e of the time and costs involved, qualitative designs do not generally draw samples from large-scale data problem of adequate validity or reliability is a major criticism. Because of the subjective nature of qualitative data and its origin in single contexts, it is difficult to apply conventional standards of reliability and validity. For example, because of the central role played by the researcher in the generation of data, it is not possible to replicate qualitative studies. Also, contexts, situations, events, conditions and interactions cannot be replicated to any extent nor can generalisations be made to a wider context than the one studied with any time required for data collection, analysis and interpretation is lengthy. Analysis of qualitative data is difficult and expert knowledge of an area is necessary to try to interpret qualitative data and great care must be taken when doing so, for example, if looking for symptoms of mental e of close researcher involvement, the researcher gains an insider's view of the field. This allows the researcher to find issues that are often missed (such as subtleties and complexities) by the scientific, more positivistic ative descriptions can play the important role of suggesting possible relationships, causes, effects and dynamic ative analysis allows for ambiguities/contradictions in the data, which are a reflection of social reality (denscombe, 2010). Research uses a descriptive, narrative style, this research might be of particular benefit to the practitioner as she or he could turn to qualitative reports in order to examine forms of knowledge that might otherwise be unavailable, thereby gaining new tative tative research gathers data in numerical form which can be put into categories, or in rank order, or measured in units of measurement. This type of data can be used to construct graphs and tables of raw tative researchers aims to establish general laws of behavior and phenonomon across different settings/contexts. Research is used to test a theory and ultimately support or reject s (used to obtain quantitative data). However, other research methods, such as controlled observations and questionnaires can produce both quantitative example, a rating scale or closed questions on a questionnaire would generate quantitative data as these produce either numerical data or data that can be put into categories (e.

Findings are therefore likely to be context-bound and simply a reflection of the assumptions which the researcher brings to the tics help us turn quantitative data into useful information to help with decision can use statistics to summarise our data, describing patterns, connections. Descriptive statistics help us ise our data whereas inferential statistics are used to identify statistically ences between groups of data (such as intervention and control groups in ised control study). Without bias), and is separated from the design of the study is determined before it the quantitative researcher reality is objective and exist separately to the researcher, and is capable of being seen by ch is used to test a theory and ultimately support or reject t: quantitative experiments do not take place in natural settings. Small scale quantitative studies may be less reliable because of low quantity of data (denscombe, 2010). This also affects the ability to generalize study findings to wider mation bias: the researcher might miss observing phenomena because of focus on theory or hypothesis testing rather than on theory of hypothesis ific objectivity: quantitative data can be interpreted with statistical and since statistics are based on the principles of mathematics, the quantitative approach is viewed as scientifically objective, and rational (carr, 1994; denscombe, 2010). For testing and validating already constructed analysis: sophisticated software removes much of the need for prolonged data analysis, especially with large volumes of data involved (antonius, 2003). Quantitative data is based on measured values and can be checked by others because numerical data is less open to ambiguities of interpretation. Doing quantitative research in the social sciences: an integrated approach to research design, measurement and statistics. Quantitative data defines whereas qualitative data 'quantitative data' in a psychology professor rejected the teds paper, because he felt it just consisted of teds opinion and it didnt have enough quantitative data to support its found this tried to perform a study to find out what our customers thought but we didn't have appropriate quantitative data to verify that our study was found this helpful. Went to the house to take a census, the data collected will be turned into the office. They look over the information and write it down as quantitative found this also might like... Analyzing a company from an investment perspective it is important to assess it from both a qualitative and a quantitative perspective.

Paste this html in your website to link to this dictionary by letter:Mobilesurvey participant t us australian bureau of complete survey topics @ a glance methods & & media education links helpabs tical language - quantitative and qualitative tanding statistics draft statistical capability framework statistical language abs presents... Abs sports stats abs tative and qualitative t on this page requires adobe flash player to be ad adobe flash playeralternatively, read the transcripts, attached below, containing a text version of the information displayed in the flash animation explains the concept of quantitative and qualitative data. Data are measures of values or counts and are expressed as tative data are data about numeric variables (e. Data are measures of 'types' and may be represented by a name, symbol, or a number ative data are data about categorical variables (e. Quantityqualitative = collected about a numeric variable will always be quantitative and data collected about a categorical variable will always be qualitative. Therefore, you can identify the type of data, prior to collection, based on whether the variable is numeric or are quantitative and qualitative data important? And qualitative data provide different outcomes, and are often used together to get a full picture of a population. For example, if data are collected on annual income (quantitative), occupation data (qualitative) could also be gathered to get more detail on the average annual income for each type of tative and qualitative data can be gathered from the same data unit depending on whether the variable of interest is numerical or categorical. Is important to identify whether the data are quantitative or qualitative as this affects the statistics that can be number of times an observation occurs (frequency) for a data item (variable) can be shown for both quantitative and qualitative graphs below arrange the quantitative and qualitative data to show the frequency distribution of the absolute frequencies can be calculated on quantitative and qualitative data, relative frequencies can also be produced, such as percentages, proportions, rates and ratios. For example, the graphs above show 4 people (20%) worked less than 30 hours per week, and 6 people (30%) are ptive (summary) statistics:Statistics that describe or summarise can be produced for quantitative data and to a lesser extent for qualitative quantitative data are always numeric they can be ordered, added together, and the frequency of an observation can be counted. Therefore, all descriptive statistics can be calculated using quantitative qualitative data represent individual (mutually exclusive) categories, the descriptive statistics that can be calculated are limited, as many of these techniques require numeric values which can be logically ordered from lowest to highest and which express a can be calculated, as it it the most frequency observed value. Median, measures of shape, measures of spread such as the range and interquartile range require an ordered data set with a logical low-end value and high-end value.

Variance and standard deviation require the mean to be calculated, which is not appropriate for categorical variables as they have no numerical ntial statistics:By making inferences about quantitative data from a sample, estimates or projections for the total population can be tative data can be used to inform broader understandings of a population, or to consider how that population may change or progress into the example, a simple income projection for an employee in 2015 may be inferred from the rate of change for data collected in 2000, 2005, and shown in the graph below, data collected over time indicates a 5% increase every five years. Data are not compatible with inferential statistics as all techniques are based on numeric to statistical language page last updated 4 july 2013 return to topprivacy |. For permission to do anything beyond the scope of this licence and copyright terms contact sity of southern zing your social sciences research zing your social sciences research paper: quantitative purpose of this guide is to provide advice on how to develop and organize a research paper in the social of research flaws to ndent and dependent ry of research terms. An oral g with g someone else's to manage group of structured group project survival g a book le book review ing collected g a field informed g a policy g a research tative methods emphasize objective measurements and the statistical, mathematical, or numerical analysis of data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational techniques. Quantitative research focuses on gathering numerical data and generalizing it across groups of people or to explain a particular , earl r. London: sage publications, teristics of quantitative goal in conducting quantitative research study is to determine the relationship between one thing [an independent variable] and another [a dependent or outcome variable] within a population. Quantitative research designs are either descriptive [subjects usually measured once] or experimental [subjects measured before and after a treatment]. Quantitative research focuses on numeric and unchanging data and detailed, convergent reasoning rather than divergent reasoning [i. Main characteristics are:The data is usually gathered using structured research results are based on larger sample sizes that are representative of the research study can usually be replicated or repeated, given its high cher has a clearly defined research question to which objective answers are aspects of the study are carefully designed before data is are in the form of numbers and statistics, often arranged in tables, charts, figures, or other non-textual t can be used to generalize concepts more widely, predict future results, or investigate causal cher uses tools, such as questionnaires or computer software, to collect numerical overarching aim of a quantitative research study is to classify features, count them, and construct statistical models in an attempt to explain what is to keep in mind when reporting the results of a study using quantitative methods:Explain the data collected and their statistical treatment as well as all relevant results in relation to the research problem you are investigating. Interpretation of results is not appropriate in this unanticipated events that occurred during your data collection. Explain your handling of missing data and why any missing data does not undermine the validity of your n the techniques you used to "clean" your data a minimally sufficient statistical procedure; provide a rationale for its use and a reference for it. Keep figures small in size; include graphic representations of confidence intervals whenever tell the reader what to look for in tables and :  when using pre-existing statistical data gathered and made available by anyone other than yourself [e.

Government agency], you still must report on the methods that were used to gather the data and describe any missing data that exists and, if there is any, provide a clear explanation why the missing data does not undermine the validity of your final , earl r. Los angeles, ca: sage, research design for quantitative designing a quantitative research study, you must decide whether it will be descriptive or experimental because this will dictate how you gather, analyze, and interpret the results. An experimental design includes subjects measured before and after a particular treatment, the sample population may be very small and purposefully chosen, and it is intended to establish causality between introduction to a quantitative study is usually written in the present tense and from the third person point of view. Methods section of a quantitative study should describe how each objective of your study will be achieved. The methods section should be presented in the past population and sampling -- where did the data come from; how robust is it; note where gaps exist or what was excluded. Collection – describe the tools and methods used to collect information and identify the variables being measured; describe the methods used to obtain the data; and, note if the data was pre-existing [i. Note that no data set is perfect--describe any limitations in methods of gathering analysis -- describe the procedures for processing and analyzing the data. In quantitative studies, it is common to use graphs, tables, charts, and other non-textual elements to help the reader understand the data. Further information about how to effectively present data using charts and graphs can be found tical analysis -- how did you analyze the data? Of trends, comparison of groups, or relationships among variables -- describe any trends that emerged from your analysis and explain all unanticipated and statistical insignificant sion of implications – what is the meaning of your results? Do not report any statistical data here; just provide a narrative summary of the key findings and describe what was learned that you did not know before conducting the endations – if appropriate to the aim of the assignment, tie key findings with policy recommendations or actions to be taken in research – note the need for future research linked to your study’s limitations or to any remaining gaps in the literature that were not addressed in your , thomas r. Kennesaw state ths of using quantitative tative researchers try to recognize and isolate specific variables contained within the study framework, seek correlation, relationships and causality, and attempt to control the environment in which the data is collected to avoid the risk of variables, other than the one being studied, accounting for the relationships the specific strengths of using quantitative methods to study social science research problems:Allows for a broader study, involving a greater number of subjects, and enhancing the generalization of the results;.

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Generally, quantitative methods are designed to provide summaries of data that support generalizations about the phenomenon under study. In order to accomplish this, quantitative research usually involves few variables and many cases, and employs prescribed procedures to ensure validity and reliability;. Los angeles, ca: sage, tions of using quantiative tative methods presume to have an objective approach to studying research problems, where data is controlled and measured, to address the accumulation of facts, and to determine the causes of behavior. As a consequence, the results of quantitative research may be statistically significant but are often humanly specific limitations associated with using quantitative methods to study research problems in the social sciences include:Quantitative data is more efficient and able to test hypotheses, but may miss contextual detail;. Development of standard questions by researchers can lead to "structural bias" and false representation, where the data actually reflects the view of the researcher instead of the participating subject;.