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The Two Main Types of Statistical Analysis for Greater Survey D

  • Give Your Survey Data New Shape Using These Two Different Types of Statistical Analysis

    Many marketing and insights teams rely on concrete insight to drive marketing decisions based on quantitative consumer data. Using a variety of research methods like quantitative surveys, segmentation, and brand equity analysis, teams can unlock critical insight about their target market to make better branding decisions — but not without applying the right statistical analysis to interpret and focus on the numbers and quantitative data that really matters.

    Learn about the different types of statistical analysis and the statistical tools used for data analysis that are available for data research and analysis, and unlock the answer to the vital question: What is statistical research and how can it make marketers better data interpreters?

    What is Statistical Research?

    Marketers may have a visceral reaction when it comes to statistical methods of research, and we totally understand — it can be confusing even with a survey data analysis experience as easy to use as KnowledgeHound. As leaders in the world of data analytics and UX , we strive to make statistical analysis easy for all, and if you aren’t even sure where to get started interpreting your quantitative data, we recommend sectioning statistical analysis into small, bite-sized pieces, starting with your research collection method-of-choice, your methodology, and finally analysis.

    Your research methodology is of utmost importance as it is the basis of how the research is performed, and you can unpack the importance of choosing a methodology further. The methodology can conclude why the statistical methods in research were chosen, and how they solidify the types of statistical tests used. Research methodology can be split in many parts including, amongst others, descriptive, analytical, exploratory, quantitative, or qualitative.

    Although the wording is similar, methodology is different from the methods used in a research project. There are two main approaches used for data collection: quantitative methods — including conducting surveys or experiments — and qualitative methods.

    Data may be grouped into four main types based on methods for collection: observational, experimental, simulation, and derived. And though there are overall research methods, there are a few basic methods of statistical analysis which include finding the mean, standard deviation, and regression, as well as hypothesis testing and sample size determination.

    By forming a methodology for the research and utilizing research methods during data collection and analysis, statistical research has its framework for statistical analysis.

    Statistical research, then, is the overall format used to uncover answers to a research question that ends with turning collected data into a visualized format to gain insight into the data and glean information to discuss the research question.

    What Is a Statistical Test and How Can KnowledgeHound Assist in Your Testing Experience?

    Similar to understanding statistical methods for research, once broken down, a statistical test is simple. In the research process, everything, including the methodology and the methods used in research, point back to the hypothesis — an expected outcome based on an amount of knowledge that needs further inquiries regarding the subject — that the project is based around.

    Simply put, a statistical test is testing the hypothesis. Breaking it down even more, a statistical test proves if the hypothesis that is being performed with variables and the constant is statistically significant or does not hold a significant relationship.

    The types of statistical analysis for a quantitative project can be split in two main ways by selecting a parametric test or a non-parametric test.

    In choosing parametric, a researcher can choose between regression, comparison, or correlation tests. A correlation test is one which determines whether or not two variables are connected, and is known as a Pearson’s r test. There are three types of regression tests and, out of the three, the two most commonly used are simple linear and multiple linear. Regression tests are used to test cause-effect relationships.

    Comparison tests in statistical analysis look at the means between two or more groups. The likelist test used if a researcher is looking at comparing the means of more than two groups would be an ANOVA. Although ANOVA tests are not currently an available statistical test in KnowledgeHound, the platform allows for testing for means and column proportions by concluding if data in pairs of cells in the same row are statistically significant.

    Non-parametric tests aren’t used as frequently as parametric tests due to the framework of the hypothesis testing and what the researcher’s methods are; but when non-parametric tests are used, researchers lean heavily on the Spearman’s r and the Chi square test of independence tests.

    Although KnowledgeHound does not currently support means testing like regression analysis, as a progressive and user-friendly platform, users are able to view and manipulate respondent data from statistical tests data to then subsequently analyze and form strategies.

    What’s more, to enhance the usability, advance the statistical testing experience, and to further integrate the relationship with KnowledgeHound with users through feedback, the platform recently expanded the significance in statistical testing to include a confidence level of 90%, improved from including just 95%.

    What are the Types of Statistical Analysis?

    By discovering the components of what makes up statistical research, including the methodology, data collection methods, and statistical analysis methods, and even understanding what a statistical test is (and how they are broken down into parametric and non-parametric tests), another element of statistical research that is important to pay attention to is understanding the types of statistical analysis.

    Of the many types of statistical analysis — there are seven! — there are two preferred methods of quantitative data investigation once the analysis is ready to take place. Although we will look at the most popular two methods, the remaining five — which are predictive, prescriptive, exploratory, causal, and mechanistic — all have merit in their statistical analysis type.

    Descriptive statistics, the first popular type of statistical analysis, looks at the summarization and organization of the collected data from the statistical tests performed by the researcher. This means that the researcher divides the data into specific categories that are able to be easily identified like age, eye color, or gender, when placed in a visual format like a graph, table, or chart.

    The second favored type of statistical analysis is inferential statistics. As the words imply, like descriptive statistics analysis which describes the data, the researcher will make inferences regarding the collected data using the inferential statistics analysis method.

    Many researchers will use both methods due to wanting to gain a more thorough understanding of the data and to help those reading the data to gain a more holistic comprehension of the data’s nuts and bolts (descriptive statistics) and to test the hypothesis (inferential statistics).

    Thanks to KnowledgeHound’s statistical significance testing with the types of statistical analysis that the platform does support, there are ways for researchers and non-researchers to leverage respondent data and improve business capabilities. From a user-friendly interface when parsing through the platform to a malleable experience with verified data, KnowledgeHound’s ease of use with statistical tests and data analysis have proven to showcase the value of discovering survey data and what it can do for organizational insight and procedural planning.

    Choosing the Right Statistical Tools for Data Analysis

    Regardless of whether you use a quantitative or qualitative method of research, researchers will more than likely use at least one statistical tool for data analysis. For qualitative statistical analysis, there are many tools available for analyzing transcriptions and content as well as qualitative coding.

    With so many data points to analyze, especially when it comes to quantitative analysis, it’s often an unwanted task to search for and choose the right statistical tools for data analysis. There are many integrated methods of survey data analysis available on the market, but with KnowledgeHound, our analysis experience lets users get past the clutter of data to the point where the application of the data analysis happens sooner than later.

    Through the user-friendly and intuitive platform, researchers and non-researchers are able to use KnowledgeHound’s easy search-based interface to glean insights for their descriptive and inferential statistical analysis (and the other five types of statistical analysis). KnowledgeHound gives everyone the power to interpret quantitative survey data just like a seasoned statistician, which helps teams become better researchers and, by extension, better marketers.

    Also Read: How Can Data Analytics and UX Lead to Greater Insight?

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