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What are some of the most appropriate and robust statistical methods for your quantitative research?

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Quantitative research is a systematic and objective way of collecting and analyzing numerical data to answer specific research questions or test hypotheses. However, not all statistical methods are equally suitable or reliable for different types of data, research designs, and research objectives. In this article, you will learn about some of the most appropriate and robust statistical methods for your quantitative research, and how to choose them based on your research questions, data characteristics, and assumptions.

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1 Descriptive statistics

Descriptive statistics are the basic methods of summarizing and presenting the main features of your data, such as the mean, median, mode, standard deviation, frequency, and percentage. Descriptive statistics help you to understand the distribution, variability, and central tendency of your data, and to compare different groups or categories. Descriptive statistics are useful for any type of quantitative research, but they do not allow you to make inferences or generalizations beyond your sample.

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2 Inferential statistics

Inferential statistics are the methods of testing hypotheses and drawing conclusions about the population based on your sample data, such as t-tests, ANOVA, chi-square, correlation, and regression. Inferential statistics help you to examine the relationships, differences, and effects among your variables, and to estimate the significance and confidence of your results. Inferential statistics require you to have a clear research question and hypothesis, and to check the assumptions and conditions of each method before applying them.

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3 Parametric and non-parametric statistics

Parametric and non-parametric statistics are two broad categories of inferential statistics that differ in their assumptions and approaches. Parametric statistics assume that your data follow a normal distribution, have equal variances, and are measured on an interval or ratio scale. Parametric statistics are more powerful and precise, but also more sensitive to outliers and errors. Examples of parametric statistics are t-tests, ANOVA, and linear regression. Non-parametric statistics do not make any assumptions about the distribution or variance of your data, and can be used for ordinal or nominal data. Non-parametric statistics are more robust and flexible, but also less efficient and informative. Examples of non-parametric statistics are Mann-Whitney U test, Kruskal-Wallis test, and Spearman's rank correlation.

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4 Multivariate statistics

Multivariate statistics are the methods of analyzing more than two variables simultaneously, such as multiple regression, factor analysis, cluster analysis, and discriminant analysis. Multivariate statistics help you to explore the complex and interrelated patterns and structures of your data, and to reduce the dimensionality and redundancy of your variables. Multivariate statistics require you to have a large and representative sample, and to ensure the validity and reliability of your measurements.

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5 Mixed methods

Mixed methods are the approaches of combining quantitative and qualitative data and analysis in the same research project, such as sequential, concurrent, or transformative designs. Mixed methods help you to integrate the strengths and overcome the limitations of both types of data, and to provide a more comprehensive and nuanced understanding of your research problem. Mixed methods require you to have a clear rationale and purpose for mixing, and to balance the quality and rigor of both types of data and analysis.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Qualitative & Quantitative Research Methodologies

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What are some of the most appropriate and robust statistical methods for your quantitative research?

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1 Descriptive statistics

2 Inferential statistics

3 Parametric and non-parametric statistics

4 Multivariate statistics

5 Mixed methods

6 Here’s what else to consider

Qualitative & Quantitative Research Methodologies

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