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## What is quantitative data analysis?

Quantitative data pertains to data presented in the form of numbers wherein each set of data is in correspondence with a numerical value. Any quantifiable data is used to perform mathematical calculations and statistical analysis, hence these are to be ultimately recorded and reflected in the study as well. The analysis of these sets of quantitative data through statistical techniques is called quantitative data analysis. To understand the process of the analysis better, researchers should take note of the two main branches of statistics, which is the main method of interpreting numerical data , which are descriptive statistics and inferential. Descriptive statistical methods describe the technicalities of certain data through standard deviation, mean, median, and skewness. Through this method, we are able to identify the common properties of each set of data. On the other hand, inferential statistical methods describe a general population through deducting the focus towards only a sample of the whole itself in order to make a generalization.

## Sampling Methods for Quantitative Research

Sampling is an integral part especially in making quantitative research. It purpose is simply to generate an inference through examining a part of the whole. The sampling method you choose is very arbitrary of the kind of results you are going to get in your research, as it determines the accuracy and the individual selection of where the study mostly circumnavigates its focus on. Generally speaking, in statistical studies, samples are used when the whole population is too large to gauge. Samples serve as the representation of the population, hence they should be chosen without bias, and are at best highly objective. Quantitative data analysis simply cannot stand without the proper sampling method that tailor-fits the kind of research being conducted to begin with. Here are examples of sampling methods essential in quantitative research.

**Probability Method**: The theory of probability is the branch of mathematics that focuses on the analysis of random phenomena. This is the main gist of the probability method since it uses random selection in determining its participants. A distinguishing trait of the probability method is that anyone can be chosen as a participant regardless of any specific attribute. Each person has an equal chance as the next one, hence making your study entirely objective in terms of representing everyone in a population. Onward will be the subset of sampling methods that fall under the probability method.

**Simple Random Sampling Method**: This type of probability method is very self-explanatory based on its name. When using the simple random sampling, researchers assign numbers to people in a certain population, and use an automated device in order to randomly pick out numbers. The numbers picked out through the automated process will be the individuals to be included in the sample. Researchers may also use the lottery method, which is randomly drawing ballots containing numbers, in order to conclude their participants, if they choose not to opt to the automated process.

**Stratified Random Sampling Method**: Also called the “random quota sampling”, this method takes an interesting approach in picking out participants in a sample. When using the stratified random sampling method, researchers collect people from different groups in a population based on things like sex, gender, age, and the like, and assign them in mutually exclusive categories, ultimately segregating them based on the chose arbitration. The researchers then pick people from the given categories in order to form one holistic sample from a diverse population. This means that all groups get representation in the sample since everyone gets to have equal opportunities of being chosen through simple random selection.

**Random Cluster Sampling Method**: When using the random cluster sampling method, researchers out to divide the population into smaller categories referred to as clusters. This method is often used when studying a wide population, specifically divided by virtue of geographical location. In this method, researchers usually use schools, cities, or districts in order to pick out its participants from the different clusters at hand.

**Systematic Sampling Method**: There is an old probability technique wherein there is a pattern in which members of a group are selected in a certain period of time in order to form a conclusion. This is simply the dynamics of systematic sampling. Through this method, the researchers pick the “nth” individual in a population, which means that they get to decide the specific number of the pattern, hence still laying out equal opportunities for people in order to be represented.

**Non-probability method**: In non-probability sampling methods, the individual participants selected are chosen through a non-random criterion, hence representation is ultimately compromised. However, even if to an extent, the arbitration is subjective to the researchers in and of themselves, this method is still useful and strategic especially when researchers are targeting a certain part of the population. This method enables them to draw out accurate results, and ultimately makes their job easier through a clear cut of individuals that fit their standards. Onward will be the subset of sampling methods that fall under the non-probability method.

**Convenience Method**: In the convenience sampling method, researchers choose the participants who are the most accessible and convenient for them to have as sample. This method is the easiest and cheapest way to gather data, however, the reliability of the results is, to an extent, not promising because the sample is not representative of the whole population, nor does serve purpose to a specific target of the research.

**Voluntary Sampling Method**: The voluntary sampling method is somewhat similar to the convenience method, the difference is that the participants are the ones approaching the researchers instead of the converse as demonstrated in the convenience method. The participants choose to be part of the data-gathering by virtue of their convenience and interest, usually through online polls, or public surveys. Still, results are likely to be biased since participation is also reflective on the kind of focus or benefits that the research itself promises to its audience.

**Purposive Sampling Method**: This method is also called judgment sampling, as it allows the researchers to select its participants depending on the specific issue their research is focusing to solve or shed light on. This means that a specific part of the population is the group of people they are exactly examining, hence why they alone serve as the sample of the whole study.

**Snowball Sampling Method**: Snowball sampling is usually used when participants are scarce in a certain area. The name itself plays a metaphor on the dynamics of it as a method, since it pertains to participants hooking people they know up to the researchers. It forms a pyramid effect of the respondents of a certain study.

## How to Analyze Quantitative Data

After learning the different ways of gathering samples in order to draw quantitative data, researchers now have to analyze and interpret the sets of numerical value they have at hand. In the aftermath of the analysis, researchers should be able to answer the “what” and “how many” questions they have regarding the topic, and that is what sets it apart from qualitative research. It is important to follow the important steps in analyzing quantitative data since these amounts hold great gravity in your research. Here are the steps in analyzing your gathered quantitative data.

### Step 1: Data Preparation and Organization

After using one of the methods of sampling in the earlier portions of this article, your data should be drawn and prepared for use in your study. The data and the results should be jotted down or taken note of in a document, and data sets should be indicated through legends for better interpretation and clarity.

### Step 2: Data Interpretation

Quantitative data can be interpreted in two ways: descriptive statistics and inferential statistics. The former is used to describe sets of data, and summarize them in an orderly way, while the latter takes a portion of the whole in order to make generalizations about it, usually presented through probability distribution, hypothesis testing, regression analysis, and correlation testing. These data are then to be reviewed and explored by the researchers.

### Step 3: Thematic Coding of Data

In order to better identify the data you are dealing with, it is important to code them in a way that lets them become a lot more attached to the problem that your research is focused on. One way to do this is through thematically coding your sets of data, depending on where you have drawn them. This is an essential part of the process since it enables you to have your data presentation be a lot more coherent and understandable.

### Step 4: Data Presentation

In order to make your data more tangible and easily quantifiable, models are to be had. After the analytical methods undergone, graphical methods are then drawn in order present data. This could be done through a bar chart, pie chart, numerical table, or any graphical presentation of your choice, depending on the kind of data you are presenting. Each of these models have a specific target of data that are best shown through them.

## What are the types of quantitative research?

The primary types of quantitative research are descriptive, which discusses the various characteristics of the target sample of the study, quasi-experimental which aims to build the cause and effect relationship between the different variables at hand in the study, and experimental which focuses on solving the problem at hand with a dominantly scientific approach.

## What are examples of quantitative data?

Anything of numerical value can be branded as quantitative data, and this looks like length, mass, time, and temperature. The given examples are included but certainly are not what quantitative data are limited to.

## What are disadvantages of non-probability sampling?

It is ultimately advisable to opt to the probability method of sampling, but sometimes, it is inevitable to choose the non-probability method. A few disadvantages are than it has an unknown proportion compared to the rest of the population, hence data outcome may very much well be non-inclusive to the situations of those outside the chosen circle. Another is that the overall generalization is deemed less reliable due to the nature of the sample chosen by the researchers.

Quantitative data analysis is very important in making complex data understandable, and even simplified for the public to consume. It is a meticulous process to be taken, hence every detail has to be correct and precise, from the data gathering, to the sampling techniques to be used, and everything in between. With all that said, these are things that contribute greatly to the betterment of our society as a whole by virtue of it standing and even lifted higher with the wonders of knowledge pushed forward by research.