Quantitative Data Analysis
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1 Quantitative Data Analysis Text durch Klicken hinzufügen
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3 Definition agesnews-image/umfragebogen_-_cirquedesprit Fotolia.jpg / tatics/ bilder e.de/ s /t /de/b ilder/ hieme/fina tw_ph l apie/f ys rageb ogen.j iother pg Quantitative data means data, or evidence, based on numbers. WHO_fragebogen.jpg m/2012/07/doodle-deluxesmall.jpeg
4 Kinds of quantitative data Nominal data Description of categories categorical data Unordered set No numeric value No arithmetical operations Example: Gender (female, male) /article ece/alternates/w620/co nchita.jpg
5 Kinds of quantitative data Ordinal data Possess a natural order ranked data Arithmetical operations (<,>) Example: ratings, school grades
6 Kinds of quantitative data Interval data Measurements Distance between units Ranking of categories is proportionate Arithmetical operations (+,-) Example: interval of years (1914,1918) same as (2000, 2004) Ratio data Interval data with true zero Arithmetical operations (+,-,*,/) Example: age, height, weight can be 0
7 Kinds of quantitative data Discrete data Measurement leads to whole number Example: number of children Continuous data Measurements in greater accuracy Example: age in years, days, minutes, seconds...
8 Preparation for analysis Encoding data predefined answers Themes in answers to open questions Mutually exclusive, exhaustive, consistently applied Error and outlier detection logical inconsistencies Bad values Note: record what is wrong with the data item
9 Visual aids Univariate data Plot Boxplot Histogram Bargram
10 Visual aids Bar charts Frequencies Nominal data Pie Charts
11 Visual aids Line graphs Trends in data Continous data -of-the-day-the-iphones-market-share-is-dead-in-the-water.jpg Be careful not to overload the reader.
12 Visual aids Bivariate data Scatterplot Relationship between variables Many values required
13 Visual aids Multivariate data Geometric Transformation Scatterplot Matrix Parallel Coordinate Plot Glyph-based Visualizations Chernoff Faces Shape Coding
14 Visual aids Pixel-based Visualizations Downscaling of Dimensions 3D Visualizations Skupin 2004
15 Visual aids Guidelines Make it easy to read Entitle it Show information about the units Give enough information, but do not overload your visualization
16 Statistics Describing the central tendency Mean average add up a set of scores and divide by the number of scores Median Mid point in a range of scores List values in order read off the middle value Mode Value which occurs most frequently
17 Statistics Describing the distribution Range: Highest and lowest data value Fractile: Dividing up the data Quartiles Deciles Percentiles Standard deviation: Average distance of each data value from the mean
18 Statistics Describing correlations Null hypothesis and significance Premise: No true relationship between variables Probability Chi-square test Two variables are associated to a significant level? Comparison between observed in data expected by chance T-tests Significant difference? Independent groups T-test: Uses mean and SD of two sets of data Dependent / matched pairs T-test: Mean of groups + difference between scores
19 Analysis and Evaluation Analysis: What do your results show? What do they imply? What is important? How do they relate to other reported research? Do your findings confirm those of other researchers? What relevance do they have?
20 Analysis and Evaluation Evaluation: + Scientifically respectable + Based on well-established techniques + Not based on subjective impressions + quick analysis using software danger of going overboard dependent on quality of initial data forced to plan ahead beware of hidden subjectivity
21 Take away message Algorithmical evaluation of large datasets Numbers don't lie Don't draw conclusions you can't back up
22 Discussion
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25 U.S. States by population
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