# The different types of data thst derives from qualitative and quantitative variables

And now the little boy even has a name for it. Therefore, what can only be done is to gather data by letting the independent variable occur naturally, and observe its effects. There is also another problem with mapping counts or totals and other spatially extensive data within areas using the choropleth method.

If they collect the relevant data, they will be able to make informed decisions on how to use business resources efficiently. Mapping the population density for census tracts B reveals that the people are concentrated in the southwest—a fact obscured by the population density by county A.

As against this, data collection is structured in quantitative data. Figures 2 to 4 demonstrate a very important caveat: Narratives often make use of adjectives and other descriptive words to refer to data on appearance, color, texture, and other qualities.

Qualitative data is criticized for its unreliability so it is backed by quantitative data; quantitative data is criticized for its lack of description and explanation thus it is backed by qualitative data. Qualitative or Quantitative Data? Just as having data will improve decision-making and the quality of the decisions, it will also improve the quality of the results or output expected from any endeavor or activity.

Statistics uses a lot of mathematics and many major concepts like probability, populations, samples, and distribution, etc.

Commonly used mapping tranformations computed using the following operations, where na is the number of observations in one category, nb is the number of observations in another category, N is the total of all categories, and A is the area of the unit.

Attitudes strongly disagree, disagree, neutral, agree, strongly agree are also ordinal variables, however we may not know which value is the best or worst of these issues. Quantitative data can also be effectively portrayed using symbol variations such as orientation and pattern spacing, but hue, shape, lightness, and size are most often used because they are the most easily and correctly understood symbols.

Spatially intensive data can be derived from spatially extensive data. Unlike quantitative data, they are generally not measurable, and are only gained mostly through observation. Leaders cannot make decisive strategies without facts to support them. Decision-making processes become smoother, and decisions are definitely better, if there is data driving them.

The data is not evenly distributed within the unit, as is the case with most areal data. These data, on the other hand, deals with quality, so that they are descriptive rather than numerical in nature. The research methodology is exploratory in qualitative data, i.

The result is often in the form of statistics that is meaningful and, therefore, useful.

Data collection methods will help ensure the accuracy and integrity of data collected. Unlike quantitative data, which recommends the final course of action.

In this case the data that you collected was probably narrative in form, so you would use qualitative techniques to analyze the transcripts looking for content and themes relevant to the program. He must be highly capable and experienced in controlling these types of interactions.

For example, the social security number is a number, but not something that one can add or subtract. Arbitrarily dividing the units does illustrate the properties of spatially intensive and extensive data, but it is not something you would probably ever need or want to do.

For example, a researcher conducting a study on the recovery of married mothers from alcoholism will choose women who are 1 married, 2 have kids, and 3 recovering alcoholics.

Maguire, and David W. The research methodology is exploratory in qualitative data, i. Note that the distance between these categories is not something we can measure.Qualitative Data vs Quantitative Data.

In the study of statistics, the main focus is on collecting data or information. There are different methods of collecting data, and there are different types of data collected.

The different types of data are primary, secondary, qualitative, or quantitative. Quantitative data always are associated with a scale measure.

Probably the most common scale type is the ratio-scale. Observations of this type are on a scale that has a meaningful zero value but also have an equidistant measure (i.e., the difference between 10 and 20 is.

On the contrary, quantitative data is the one that focuses on numbers and mathematical calculations and can be calculated and computed. These data types are used in a number of fields like marketing, sociology, business, public health and so on.