How can you deal with Web and Market data?
Estatistics.eu can help you to decide how to collect data from offline and online resources, to gather web data for market analysis. Designing a survey, a questionnaire or an experiment is a very hard task. The first task is to decide the aim of your research / market research. The second aim is to choose the right tools for doing it. The third task is to analyze and interpret these data. Estatistics.eu can help you all the way through.
The first task, that is, deciding your aim, is very essential. You must decide what you really want to research. Designing a research either for marketing reasons or for scientific reasons, the logic and the steps that are followed are the same. If your aim/s are not very solid and of good reason then, your results may reflect something else that the aim that you stated in the beginning, and therefore to introduce bias.
Secondly, deciding, what tools you will use and how, is a hard task because a lot of options exist e.g. online or hard-copy questionnaire ? Observation ? Metadata ? Data collection ? experimental setting ? Nowadays data collection and surveys are used almost in every setting, especially in market research. Surveys and questionnaires are very good, cheap, cost-effective tools if they are used in the right way but they are not the only option as it was described. Using the wrong tool for your aim, you may have ended to have a batch of data that are not entirely suitable for your research aim. For example, when you are designing a new questionnaire, simple things that you may think such as how you will encode age variable? 20-25 and 25-30? There is an overlap, namely “25”! or 18-25, 26-30? The intervals are not equal! If you use an online form, how you will validate the entry that it will not contain typos? You will permit participants aged under 18 to take part to this survey? How you will block their entries? Have you gathered all the necessary paperwork for that? Cautious to all these things!
Thirdly, the way your collected data must be cleaned (see data cleaning and data screening) from outliers (extreme points or values) / multivariate outliers, typos, how to handle missing or ambiguous data, how to deal with skewedness (positive or negative), kurtosis (e.g. platykurtosis), if you apply recoding and transformation, and thereafter to be analyzed with the right way. The statistical analysis must also be interpreted in the right context with any limitation in mind and it must provide directions though for future types of research in order to expand its usefulness.