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Discussion of inferential analysis of data is often in contrast with descriptive analysis. Inferential data analysis can be used discuss a larger population,

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Before we proceed, let’s take a quick review of statistics, this overview will help you gain an understanding of future concepts discussed. Please access the following link:

Teach me STATISTICS in half an hour! (This is an optional overview)

Chapter 20: Inferential Analysis 

The Inferential Data Analysis Definition

In the branch of knowledge of statistical terms inferential data is one step beyond. The attempt is to find conclusions that go little bit more than the existing data suggests. It can be used to predict, or infer, what a sample population may think about a product or a change in policy. Several statistical models are used to do inferential data analysis, and these can include Analysis of Variance (ANOVA), regression analysis, Analysis of Covariance (ANCOVA) and other possibilities found within the General Linear Model.

Descriptive and Inferential Analysis of Data

Discussion of inferential analysis of data is often in contrast with descriptive analysis. Inferential data analysis can be used discuss a larger population, but descriptive statistics may only be used effectively with a group being examined; generalization to a larger group is not possible. Descriptive statistics are reviewed with tools such as frequency distribution, central tendency, and will make use of visuals to describe the data. The boundaries are the given population. Descriptive statistics can go no further than the limits of the data set.

These differences do not mean that descriptive data analysis and inferential data analysis cannot be used together. As a matter fact, descriptive and inferential analysis of data can be used as a one – two step movement to get considerable amount of information. Given a small population, descriptive data analysis will bring forth very accurate parameters of that population. When it is time to go one step beyond and get some idea of what a larger group’s inclinations might be, inferential data analysis can take the earlier results and make accurate guesses.

The Benefits of Inferential Data Analysis

For example, in the field of market research is a major beneficiary of inferential data analysis. In a fast-paced business world corporation do not have the time or resources to collect large samples for analysis. Inferential data analysis permits a representative sample to be used. It is then possible to take the results and use it to infer what the larger consumer population will do. Hypothesis testing can also be done with inferential data analysis. This can allow business to have a better idea of whether a notion is going to be well received by the buying public. Given the amount of money that is spent on product development and active marketing, the informed conclusions drawn from inferential statistics can be very helpful. It eliminates the shot in the dark approach to offering new products.

Inferential studies speak in the language of probability. Designing the proper experimental boundaries can make the conclusions extremely reliable. That is perhaps the most important part of any inferential data analysis. The sample must be well constructed, and the proper tools be used. Inferential data analysis as well as intelligent data analysis can permit business establishments to move forward with a little more confidence in any marketing campaign. At the same time, it must be remembered that steps forward must be done with caution. Inferences are highly educated, but there is the chance of an error or sudden changes. Prudence is always recommended when using the findings.

Please note there are several terms that we must become familiar with:

The definitions can be found on pages 356 and 357 of your Tappen textbook.

· Statistical Significance

· Research hypothesis

· Null hypothesis

· Effect size

· Confidence intervals

· Degrees of freedom

· Variance

· Mean

· Variable

· Independent variable

· Dependent variable

Analysis of variance (ANOVA)

What is a t-Test?

Regression Analysis (Regression: Crash Course Statistics #32)

Analysis of Covariance (ANCOVA) – easily explained

ANOVA, ANCOVA, MANOVA and MANCOVA: Understand the difference

Mastering analytical procedures that are reviewed in this chapter will benefit you as a learner. It is suggested that you become familiar with statistical software packages. Please remember that all procedures listed in this chapter have benefits and areas of opportunity (drawbacks). Never forget to consider these concepts when implanting them into your research

Chapter 21: Analyzing qualitative data

Qualitative data analysis lets you find patterns and themes in your data. Qualitative data analysis is the process of examining qualitative data to find an explanation for a specific phenomenon. The most structure approach to the analysis of qualitative data utilization coding and quantifying of the qualitative data. For those who are engaged in qualitative studies, you will find that your research notes and transliterated conversations have accumulated and how will you manage the collected data. This is where the processing of these materials becomes imperative.

Website Link (Qualitative Analysis)

Qualitative data refers to non-numeric information such as interview transcripts, notes, video and audio recordings, images and text documents.

Qualitative Data Analysis

Please review the follow topics in your Tappen Textbook.

· Processing the data (p. 395)

· Purposes of Quantification (p. 397)

· Structured and unstructured analysis (p. 398)

· Content analysis (p. 402)

· Analyzing the text (p. 403)

· Unstructured analysis (p. 405)

· Ethnographic analysis (p. 414)

· Grounded theory (p. 418)

· Phenomenological analysis (p. 422)

How to Analyze the Quantitative Data

· Choose your approach: Decide on your methodology.

· Develop your framework:

· Grounded theory / emergent coding / inductive (data driven).

· Framework analysis / structured / (theory driven).

· Get to know your data: Read your data thoroughly before analyzing what was obtained.

· Choose your technology: Many people choose to use standard spreadsheet or word processing software to help manage their data.

· Code and recode: Analyze your data, source by source, line by line, and reduce it into meaningful codes

· Explore and share results.

For many scholars, many think that qualitative research is easier than quantitative analysis. This may not always be true. The execution of a qualitative analysis incorporates a skillset of work that aims to create inspiration by the researcher to the reader and endeavors to make an impact on the others by the researcher. 

Video Link:

Qualitative data analysis (Qualitative interviews #4)

Qualitative Data Analysis

Advantages and Disadvantages of Qualitative Analysis Website Link:
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