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Wouldn't it be nice to display an overview of the total research in a single graph? Well, this is possible with a graph called conceptual model.

Creating a research design or a conceptual model  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 work it out. The design gives  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.

Any research will go much faster or is much easier to understand with a research design or a conceptual model in your mind. 

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 often used 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.

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.

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 papers explains what you need to know about variables and then presents tables to determine which statistical test should be used.

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.

This paper contains a step-by-step plan that explains how you can professionally analyse the data from interviews. Processing the data this way, 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.