What is Univariate, Bivariate and Multivariate analysis?

00:04:46
https://www.youtube.com/watch?v=gN0OQ6r78f4

Summary

TLDRThe video discusses three statistical techniques used in quantitative data analysis: univariate, bivariate, and multivariate analysis. Univariate analysis examines one variable to describe data patterns, often using descriptive techniques such as tables and charts. Bivariate analysis compares two variables to find correlations, utilizing methods like regression analysis. Lastly, multivariate analysis is used for exploring relationships among three or more variables, incorporating complex statistical techniques. The choice of analysis depends on the number of variables present in the research question, highlighting the necessity to understand the level of analysis required for proper data interpretation.

Takeaways

  • 📊 Univariate analysis involves one variable and aims to describe data patterns.
  • 📈 Bivariate analysis compares two variables to find correlations.
  • 🔍 Multivariate analysis examines relationships among three or more variables.
  • 📚 Descriptive techniques include frequency distributions and pie charts in univariate analysis.
  • 💡 Bivariate analysis uses regression and correlation coefficients for insights.
  • 🩺 An example of multivariate analysis is examining health measures with eating habits.
  • 🔑 Choosing the right analysis depends on the number of variables in the study.
  • 📉 Common techniques for multivariate analysis include cluster analysis and redundancy analysis.
  • 🔗 Knowing your research's variable count helps in selecting the right analytical method.
  • 📊 Each level of analysis provides different insights into data relationships.

Timeline

  • 00:00:00 - 00:04:46

    The video discusses the importance of understanding the level of quantitative data analysis, which is determined by the number of variables in the research. It introduces three analysis techniques: univariate, bivariate, and multivariate analysis. Univariate analysis analyzes a single variable, focusing on describing data patterns such as gender ratios in a classroom. Bivariate analysis involves two variables, such as studying the correlation between student scores and gender. Finally, multivariate analysis examines more than two variables, like health measures and eating habits. The choice of analysis technique depends on the number of variables involved in the research question.

Mind Map

Video Q&A

  • What is univariate analysis?

    Univariate analysis is the simplest form of statistical analysis that involves only one variable without establishing cause or effect.

  • When would I use bivariate analysis?

    Bivariate analysis is used when comparing two variables to understand the relationship between them.

  • What techniques are used in multivariate analysis?

    Common techniques in multivariate analysis include vector analysis, cluster analysis, and regression analysis.

  • How is univariate analysis conducted?

    Univariate analysis can be conducted using descriptive techniques like frequency distributions, histograms, and bar charts.

  • What is an example of bivariate analysis?

    An example of bivariate analysis is analyzing the relationship between students' genders and their exam scores.

  • What is the goal of multivariate analysis?

    The goal of multivariate analysis is to understand the relationships among three or more variables.

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  • 00:00:00
    [Music]
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    okay so in the quantitative data
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    analysis one of the most important
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    factor is to understand the level of
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    your analysis this is determined by the
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    number of variables that you have in
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    your research and depending on that the
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    level of analysis is divided into three
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    different analysis techniques univariate
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    analysis bivariate analysis and a
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    multivariate analysis let's go through
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    each of these one by one with examples
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    univariate analysis is the most basic
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    form of a statistical data analysis
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    technique so when the data contains only
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    one variable and doesn't deal with a
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    cause or effect relationship with then a
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    univariate analysis technique is used
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    for instance in a survey of a classroom
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    the researcher may be looking to count
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    the number of boys and girls so in this
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    instance the data would simply reflect
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    the number which is only a single
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    variable and the quantity of the boys
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    and girls so the key objective of
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    univariate analysis is to simply
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    describe the data to find patterns
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    within the data and that you can do by
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    simply using like main million mode
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    dispersion variance the ways univariate
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    analysis is conducted could they most
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    basic form descriptive analysis
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    techniques like you know frequency
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    distribution tables frequency polygons
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    histograms using pie charts or bar
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    charts etc okay so bivariate analysis is
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    slightly more analytic than univer
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    analysis when the data set contains two
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    variables and the researchers aim to
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    undertake comparison between the two
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    data set then by vert analysis is the
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    right type of analysis to undertake for
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    example in a survey of a classroom the
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    researcher may be looking to analyze the
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    ratio of students who scored above 85%
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    corresponding to their genders so seeing
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    this case there are two variables gender
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    there is male or female which is one
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    independent variable and the result
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    which is the dependent variable so a
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    bivariate analysis will measure the
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    correlation between the two variables to
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    undertake bivariate analysis there
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    popular statistical techniques like
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    correlation coefficients regression
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    analysis and regression analysis could
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    also be like linear regression simple
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    regression and there a hold above
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    different of the regression pattern or
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    style that could be used to undertake a
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    bivariate analysis and lastly the
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    multivariate analysis which is the most
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    complex form of statistical analysis and
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    used only when there are more than two
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    variables in the data set
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    so here's an example a doctor has
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    collected data on cholesterol blood
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    pressure and weight she also collected
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    data on the aiding habits of the
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    subjects for example how many ounces of
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    red meat fish dairy products and
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    chocolates consumed per week
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    now she wants to investigate the
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    relationship between the three measures
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    of health and eating habits so in this
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    instance and multivariate analysis would
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    be required to understand the
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    relationship of age variables with each
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    other as you can see there are multiple
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    variables involved in this research
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    question so it commonly is multivariate
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    analysis techniques include vector
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    analysis cluster analysis of variance
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    analysis multi-dimensional scaling
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    redundancy analysis whole if of other
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    statistical techniques so I hope you
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    have understood the selection of the
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    quantitative data analysis is dependent
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    on a range of different vectors and the
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    number of variables is only one of them
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    so once you know how many variables have
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    you got in your research question
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    or the studying consideration then
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    you'll be able to select whether you
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    wanna go for a univariant boy variant or
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    a multivariate analysis
Tags
  • univariate analysis
  • bivariate analysis
  • multivariate analysis
  • quantitative data
  • statistics
  • data analysis techniques
  • correlation
  • regression
  • data patterns
  • research methods