Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Univariate analysis:Medical statistics Part IV

1,568 views

Published on

Univariate analysis

Published in: Health & Medicine

Univariate analysis:Medical statistics Part IV

  1. 1. Univariate Analysis Medical statistics Part IV
  2. 2. Univariate analysis :Watch one variable at a time across sample
  3. 3. Data analysis Descriptive Inferential
  4. 4. 1 or 2 or multi Univariate Bivariate Multivariate
  5. 5. Variables Qualitative= Categorical Quantitative = Numerical  Values are mutually exclusive  Different values represent different categories  Discrete  Ordered Category Variables  multiple category variables that are formed by “sectioning” a quantitative variable age categories of 0-10, 11-20, 21-30, 31-40  most grading systems are like this 90- 100 A, etc.  Values are mutually exclusive  Different values represent different amounts  Discrete or Continuous  discrete  No “partial counts” just “whole numbers” e.g., how many siblings do you have  continuous  fractions, decimals, parts possible  must decide on level of precision e.g., how tall are you = 6’ 5’11” 5’10.65”
  6. 6. Define one Univariate analysis  Descriptive  Simplest  First procedure one does when examining data  Quantitative  One variable watched at a time  The tools involved depend with the kind of variable  Variable may be a continuous or discrete
  7. 7. 3 major tools used in Univariate analysis  Distribution [of frequency]  Central tendency[mean,median and mode]  Dispersion
  8. 8. Distribution(of frequency)  individual value  range  Charts
  9. 9. finding frequency is key measurement Description of frequency 1) counts 2) percentages 3) percentile values 4) Central tendency 5) Dispersion[standard deviation 6) distribution: Skew=“direction of the distribution tail” 7) kurtosis 8) Standard Error of the Mean (SEM) 9) charts : bar charts and histograms 10) Box plot
  10. 10. Central Tendency Mean :summing all the scores and dividing by the number of students Median: the score found at the exact middle of the set of values Mode :the most frequently occurring value in the set of scores
  11. 11. Dispersion :Spread around the central tendency Range Standard deviation Range=highest value minus the lowest value The Standard Deviation shows the relation that set of scores has to the mean of the sample More accurate
  12. 12. Standard deviation
  13. 13. The SPSS tools • following procedures: "Frequencies", "Descriptives" and "Explore" all located under the "Analyse" menu.
  14. 14. Standard Error of the Mean (SEM) • Standard Error of the Mean (SEM) standard deviation • SEM = ---------------- n  The SEM tells the average sampling mean sampling error -- by how much is our estimate of the population mean wrong, on the average  the smaller the population std, the more accurate will tend to be our population mean estimate from the sample  larger samples tend to give more accurate population estimates
  15. 15. Thanks

×