**How to formulate a good research question**

The research question is the most important part of the whole research. A correct question helps you to conduct your research in an easy and flexible way.

In this paper, I will show you how to formulate a correct research question. In addition, I will show you why certain research questions are not good and how you can improve them.

If you still have to start your research, this is the best investment you can make. I'm sure this will prevent you from feeling stupid, or experiencing feelings of abandonment when your research is a mess.

Get acces to this paper. It's free if you use this **discount code: ****free trial**.

Wouldn't it be nice to display an overview of the total research in a single graph? Well, this is possible. You can do that by making a research design.

Creating a research design is bound by specific rules. In this paper you will learn these rules.

Once you know how to make such a graph, every study becomes a lot easier to do. The design gives you guidelines for sampling, collecting the data and analyzing the collected data. It is the methodology in a nutshell to find an answer to the research question.

Your research will go much faster when you know how to make a research design. But you will automatically find that out when you read this paper.

**Nine ways to draw a sample**

I have read many research reports in which it was completely unclear how the sample was drawn. Most researchers just do something what they think is right. Usually, the reason for this behaviour is missing. Nevertheless, the selection of a good sample is crucial. It determines the quality of your research and with a poor sample, the research loses a lot of quality.

Nine forms of sampling can be distinguished. If you apply the correct method correctly in a given situation, you will get a representative response. Then you will have done well. If you do it wrong, your research can become meaningless.

In this paper I explain which methods are available and how to apply them.

Many researchers claim to have a representative response when their sample is large enough. Two mistakes can already be found in this vision. First, the sample size is not related to representativeness. And second, the size of the sample is not the problem. The problem that counts is the size of the response.

Now, you might be worried. Maybe you should be.

Another way to calculate the sample size is to calculate an estimated proportion with a confidence interval of 95%. Based on the standard calculations, the result is always a sample size of 384.1 or 385. This view also does not stand in a critical discussion. In many studies, estimating a proportion is not important. It is therefore not a reason to calculate the sample size based on the estimation of a proportion.

The correct way to calculate the sample size is much more complex. And by the way, the right way does not exist.

This paper explains how to deal with the problem of sample size. Read it and discover how you can apply the solution to your research.

**Representativity**

A very important topic in research is representativeness. Perhaps it is even the most important topic. In the case of a non-representative response, not all of the conclusions that can be drawn may be justified. Unfortunately, even many scientists treat this subject incorrectly.

Different views on representativeness can be distinguished. One of these is taking a random sample. However, it can be demonstrated that with random sampling at least five percent of the samples will be non-representative.

Very often, researchers claim to have drawn a representative sample. However, a representative sample is not the problem. A representative response is required. A non-representative sample can even be used as a strategy to get a representative response. All this is explained in this paper.

If you want to improve the quality of your research, make sure you have a representative response. You can learn how to achieve this in this paper.

**Factor analysis and Cronbach's alpha**

Measuring in science means providing valid and reliable data.

One way to get valid and reliable data is to create a questionnaire with a list of statements about a topic. In questionnaires, respondents are asked to give their answer on a five-point Likert scale. The list of statements is supposed to measure a construct. To statistically test whether the construct has been measured correctly, a form of factor analysis is performed. And to test whether the construct has been reliably measured, Cronbach's alpha must be calculated.

This, in a nutshell, is the essence of why factor analysis should be applied and Cronbach's alpha should be calculated. Much more details can be found in our paper.

**The statistical test procedure**

Statistics are essentially very simple. It is about calculating a number with the data and comparing this number with a number in a table. Is that all? Yes that's all.

However, you must know how to calculate the number with your data and you must know which number to compare it to. For this, you must followed a procedure.

This procedure is described in detail in this paper. Some steps in the procedure are simple, others are more difficult and need more thinking time. In the beginning, it might look impossible to learn, however, when you become more experienced, you will be able to complete the whole procedure in a split second.

**How to choose the correct statistical test**

One of the most difficult things in statistics is to figure out which statistical test has to be applied. Before you make a choice, you need to know what variables are, how they are measured, what dependent and independent variables are and whether it is about comparing features or comparing groups.

This paper explains it all. You can then use the tables in this paper to determine which statistical test you should use.

**How to present statistical results**

When you have analyzed the data, a new problem arises: How to present the results in a report?

It is not permitted to copy the results of statistical software programs and paste these in a paper. In fact, an estimated ninety-nine percent of the numbers in the statistical software output of the can be deleted. Only a few digits are required.

A distinction must be made between presenting the results in text and presenting the data in tables. Slightly fewer numbers are presented in tables.

This paper provides a guideline for presenting statistical results. When the rules in this paper are followed, fine-looking tables are produced that are consistent with the rules of many scientific journals.

**Analyzing data from interviews**

Do you get stuck in your research because you don't know what to do with the data from the interviewees? If so, you are not the only one.

Many students think that analyzing interviews is easy. Often they are disappointed. Usually, it takes a lot of work to organize the data in such a way before they can begin the real analysis and write a report on the results of the interviews.

Our paper contains a step-by-step plan that explains how you can professionally process the data from interviews. In this way, you will prevent bias and you will be able to give a good answer to your research question. It will still be a lot of work, but you will get much better results, so that your research becomes credible.

Because:

Good research provides you with better information.

With better information, you can make better decisions.

With better decisions, you can create a healthier, wealthier and freer world,

for people, fauna and flora, for this and future generations.

That’s why we think it’s necessary for you to know how to carry out proper research.