Estimation, Approximating a Precise Value 1

Estimation (Approximating a Precise Value) is very useful especially when someone wishes to know whether he/ she has arrived at a logical solution to a problem under study. It is useful to learn about how to estimate the total sum of a bill to avoid immediate overpayments. For example, one can estimate the total amount of shop (supermarket) receipts. The estimate of these receipts can be done by rounding the amount of each item to the nearest half and keeping a running total mentally from the first item to the last one.

Estimation of a Utility Bill

Suppose the following is a shop receipt, with the estimated amount and running total. Consider, the estimation, approximating a precise value for a utility bill.
Shop Item, Actual Amount, Estimated Amount, Running Total.

Shop ItemActual AmountEstimated AmountRunning Total
Item 14.504.504.50
Item 23.503.508
Item 31.31.59.5
Item 40.600.510
Item 52.95313
Item 62.85316
Item 71.601.5017.5
Item 82.75320.5
Item 92.42.523
Total22.4523 

From the above example, it can be observed that estimation is a process of finding an estimate of a value. It saves time and results in the nearest possible exact value. An estimate can be overestimated (when the estimate exceeds the actual value) and underestimated (when the estimate falls short of the actual value).

Estimation, Approximating a Precise Value

In some cases, an estimate can be performed to round all of the numbers that you are working to the nearest 10 (or 100 or 1000) and then do the necessary calculations. In everyday life, the estimation can be used before you solve a problem in an easier and faster way. It helps you to determine whether your answer is reasonable. Estimation is also useful when you need an approximate amount instead of a precise value.

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Quantitative Qualitative Variables: Statistical Data (2021)

This article is about Quantitative Qualitative Variables. First, we need to understand the concept of data and variables. Let us start with some basics.

The word “data” is frequently used in many contexts and ordinary conversations. Data is Latin for “those that are given” (the singular form is “datum”). Data may therefore be thought of as the results of observation. In this post, we will learn about quantitative qualitative variables with examples.

Data are collected in many aspects of everyday life.

  • Statements given to a police officer, physician, or psychologist during an interview are data.
  • So are the correct and incorrect answers given by a student on a final examination.
  • Almost any athletic event produces data.
  • The time required by a runner to complete a marathon,
  • The number of spelling errors a computer operator commits in typing a letter.

  Data are also obtained in the course of scientific inquiry:

  • the positions of artifacts and fossils in an archaeological site,
  • The number of interactions between two members of an animal colony during a period of observation,
  • The spectral composition of light emitted by a star.

Data comprise variables. Variables are something that changes from time to time, place to place, and/or person to person. Variables may be classified into quantitative and qualitative according to the form of the characters they may have.

Quantitative Qualitative Variables

Let us understand the major concept of Quantitative Qualitative variables by defining these types of variables and their related examples. The examples are self-explanatory and all of the examples are from real-life problems.

Qualitative Variables

A variable is called a quantitative variable when a characteristic can be expressed numerically such as age, weight, income, or several children, that is, the variables that can be quantified or measured from some measurement device/ scales (such as weighing machine, thermometer, and liquid measurement standardized container).

On the other hand, if the characteristic is non-numerical such as education, sex, eye color, quality, intelligence, poverty, satisfaction, etc. the variable is referred to as a qualitative variable. A qualitative characteristic is also called an attribute. An individual or an object with such a characteristic can be counted or enumerated after having been assigned to one of the several mutually exclusive classes or categories (or groups).

Quantitative Variables

Mathematically, a quantitative variable may be classified as discrete or continuous. A discrete variable can take only a discrete set of integers or whole numbers, which are the values taken by jumps or breaks. A discrete variable represents count data such as the number of persons in a family, the number of rooms in a house, the number of deaths in an accident, the income of an individual, etc.

A variable is called a continuous variable if it can take on any value- fractional or integral––within a given interval, that is, its domain is an interval with all possible values without gaps. A continuous variable represents measurement data such as the age of a person, the height of a plant, the weight of a commodity, the temperature at a place, etc.

A variable whether countable or measurable is generally denoted by some symbol such as $X$ or $Y$ and $X_i$ or $X_j$ represents the $i$th or $j$th value of the variable. The subscript $i$ or $j$ is replaced by a number such as $1,2,3, \cdots, n$ when referred to a particular value.

Quantitative Qualitative Variables

Examples of Statistical Data

Note that statistical data can be found everywhere, few examples are:

  • Any financial/ economics data
  • Transactional data (from stores, or banks)
  • The survey, or census (of unemployment, houses, population, roads, etc)
  • Medical history
  • Price of product
  • Production, and yields of a crop
  • My history, your history is also statistical data
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Qualitative vs Quantitative Research (2021)

In this post, we will discuss Qualitative vs Quantitative Research. Qualitative and quantitative research are two fundamental approaches to research, each with its own strengths and applications. Qualitative vs quantitative research involves collecting data based on some qualities and quantities, respectively. Let us discuss both the Qualitative and Quantitative Research in detail below.

Qualitative vs Quantitative Research

Qualitative Research

Qualitative research involves collecting data from in-depth interviews, observations, field notes, and open-ended questions in questionnaires, etc. The researcher himself is the primary data collection instrument and the data could be collected in the form of words, images, patterns, etc. For Qualitative Research, Data Analysis involves searching for patterns, themes, and holistic features. Results of such research are likely to be context-specific and reporting takes the form of a narrative with contextual description and direct quotations from researchers.

Quantitative Research

Quantitative research involves collecting quantitative data based on precise measurement using some structured, reliable, and validated collection instruments (questionnaires) or through archival data sources. The nature of quantitative data is in the form of variables and its data analysis involves establishing statistical relationships. If properly done, the results of such research are generalizable to the entire population. Quantitative research could be classified into two groups depending on the data collection methodologies:

Research Design: qualitative vs Quantitative Research

Experimental Research

The main purpose of experimental research is to establish a cause-and-effect relationship. The defining characteristics of experimental research are the active manipulation of independent variables and the random assignment of participants to the conditions to be manipulated, everything else should be kept as similar and as constant as possible. To depict the way experiments are conducted, a term used is called the design of the experiment. There are two main types of experimental design.  

Within-Subject Design
In a within-subject design, the same group of subjects serves in more than one treatment

Between Subjects Design
In between-group design, two or more groups of subjects, each of which is tested by a different testing factor simultaneously.

Non-Experimental Research

Non-Experimental Research is commonly used in sociology, political science, and management disciplines. This kind of research is often done with the help of a survey. There is no random assignment of participants to a particular group nor do we manipulate the independent variables. As a result, one cannot establish a cause-and-effect relationship through non-experimental research. There are two approaches to analyzing such data: 

Tests for approaches to analyzing such data such as IQ level of participants from different ethnic backgrounds.

Tests for significant association between two factors such as firm sales and advertising expenditure.

Examples:

  • Quantitative: A study that surveys 1000 people to determine the average income in a city and its correlation with education level.
  • Qualitative: Research that interviews cancer patients about their experiences with treatment and explores the emotional impact of the disease.

Choosing Qualitative or Quantitative Research

The best approach depends on the research question. However, a general guideline is:

  • Use quantitative research to explore “what” and “how much” questions, measure relationships, and test theories.
  • Use qualitative research to understand “why” and “how” questions, gain insights into experiences, and explore social contexts.

Remember, Qualitative and Quantitative researches are not mutually exclusive. Sometimes, researchers use a mixed methods approach that combines both quantitative and qualitative methods for a more comprehensive understanding.

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