Type and Analysis of Data
Data is used to describe things by assigning a value to them. The values are then organized, processed, and presented within a given context so that it becomes useful.
Type of Data
Qualitative data is data that uses words and descriptions. Qualitative data can be observed but is subjective and therefore difficult to use for the purposes of making comparisons. Descriptions of texture, taste, or an experience are all examples of qualitative data
Consider measurable like your height, weight, age, and shoe size. With a firm grasp on qualitative and quantitative data,
Quantitative data is data that is expressed with numbers. Quantitative data is data which can be put into categories, measured, or ranked. Length, weight, age, cost, rating scales, are all examples of quantitative data. Example-The individual’s current living situation, smoking status, or whether he/she is employed.
Categorical data is data that has been placed into groups. An item cannot belong to more than one group at a time.
Categorical data is a collection of information that is divided into groups. If an organization or agency is trying to get a bio data of its employees, the resulting data is referred to as categorical.
Continuous data is data that can be measured and broken down into smaller parts and still have meaning. Money, temperature and time are continuous.
Continuous data is numerical data measured on a continuous range or scale. In continuous data, all values are possible with no gaps in between. Examples of continuous data are a person’s height or weight, and temperature.
The word analysis means the categorizing, ordering and summarizing the data statistically to obtain answers to research questions.
Interpretation becomes easier, if you put the data in to forms that are understandable. Interpretation means that you-
- Study the results of the analysis,
- make inferences about its occurrences of relations, and
- Draw conclusions about these relations.
Types of Data Analysis-
Descriptive Analysis-Descriptive analysis is used to describe the basic features of the data in the study. They provide simple summaries about the sample and the measures.
Descriptive statistics are computed to describe the data at hand. These can be numbers, percentages, averages (mean), or indications of how "spread out" (variability) are the data, or relationships among two sets of data (correlation).
The descriptive measure include-
- Frequency distribution, percentages, proportion, graphical presentation
- Measures of central tendency - mean, median and mode
- Measures of variability - range, standard deviation and variance
- Measures of relationship between two or more variables - correlation coefficient
Introduction to Statistics various statistical tests are classified as inferential methods. Basically, inferential statistics are necessary:
- When hypotheses are to be tested.
- When we need to infer that the observed relation would occur in similar. Sample groups or large population, we use inferential statistics to provide a means.
- For drawing conclusions about a population.