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This document discusses measures of variability used in statistics. It defines variability as the spread or dispersion of scores. The key measures of variability discussed are the range, variance, and standard deviation. The range is the difference between the highest and lowest scores. The variance is the average of the squared deviations from the mean and represents how far the scores deviate from the mean. The standard deviation is the square root of the variance and represents how much scores typically deviate from the mean. Larger standard deviations indicate greater variability in the scores.

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Measures of Variability Levin and Fox Chapter 4 Statistics for Political Science
Variability Limits of Central Tendencies: Mode, Mean, Median They offer a limited view of data set. Example: Weather in Honolulu and Phoenix Same Mean Temperature: 75° Variability? But how are yearly temperatures distributed on the mean? What is the range of yearly temperatures?
Variability But how are yearly temperatures distributed on the mean?
Measures of Variability Measures of variability tell us: • The extent to which the scores differ from each other or how spread out the scores are. • How accurately the measure of central tendency describes the distribution. • The shape of the distribution.
Measures of Variability Some Definitions: Population (universe) is the collection of things under consideration Sample is a portion of the population selected for analysis Statistic is a summary measure computed to describe a characteristic of the sample
Measures of Variability Just what is variability? Variability is the spread or dispersion of scores. Measuring Variability There are a few ways to measure variability and they include: 1) The Range 2) The Mean Deviation 3) The Standard Deviation 4) The Variance
Variability Measures of Variability Range: The range is a measure of the distance between highest and lowest. R= H – L Temperature Example: Range: Honolulu: 89° – 65° 24° Phoenix: 106° – 41° 65°
The Range The simplest and most straightforward measurement of variation is the range which measures variation in interval-ratio variables. Range = highest score – lowest score R = H – L
Range: Examples If the oldest person included in a study was 89 and the youngest was 18, then the range would be 71 years. Or, if the most frequent incidences of disturbing the peace among 6 communities under study is 18 and the least frequent incidences was 4, then the range is 14.
Okay, so now you tell me the range… This table indicates the number of metropolitan areas, as defined by the Census Bureau, in six states. What is the range in the number metropolitan areas in these six states? – R=H-L – R=9-3 – R=6 Delaware 3 Idaho 4 Nebraska 4 Kansas 5 Iowa 4 Montana 3 California 9
Variance and Standard Deviation Variance: is a measure of the dispersion of a sample (or how closely the observations cluster around the mean [average]). Also known as the mean of the squared deviations. Standard Deviation: the square root of the variance, is the measure of variation in the observed values (or variation in the clustering around the mean).
The Variance Remember that the deviation is the distance of any given score from its mean. The variance takes into account every score. But if we were to simply add them up, the plus and minus (positive and negative) scores would cancel each other out because the sum of actual deviations is always zero! ) ( X X  0 ) (    X X
So, what we should we do? We square the actual deviations and then add them together. – Remember: When you square a negative number it becomes positive! SO, S2 = sum of squared deviations divided by the number of scores. The variance provides information about the relative variability. The Variance
Variance: Weeks on Unemployment: X (weeks) Deviation: (raw score from the mean, squared) 9 8 6 4 2 1 9-5= 4 8-5=3 6-5=1 4-5=-1 2-5=-3 1-5=-4 42 = 16 32 = 9 12 = 1 -12 = 1 -32 = 9 -42 = 16 ΣX=30 χ= 30=5 6 Step 1: Calculate the Mean Step 3: Calculate Sum of square Dev Step 2: Calculate Deviation
The mean of the squared deviations is the same as the variance, and can be symbolized by s2 scores of number total mean the from deviations squared the of sum variance where      N X X s 2 2 ) ( The Variance
Variance: Weeks on Unemployment: X (weeks) Deviation: (raw score from the mean, squared) Variance: 9 8 6 4 2 1 9-5= 4 8-5=3 6-5=1 4-5=-1 2-5=-3 1-5=-4 42 = 16 32 = 9 12 = 1 -12 = 1 -32 = 9 -42 = 16 (weeks squared) ΣX=30 χ= 30=5 6 Step 1: Calculate the Mean Step 3: Calculate Sum of square Dev Step 2: Calculate Deviation Step 4: Calculate the Mean of squared dev.
Standard Deviation: It is the typical (standard) difference (deviation) of an observation from the mean. Think of it as the average distance a data point is from the mean, although this is not strictly true. What is a standard deviation?
Standard Deviation: The standard deviation is calculated by taking the square root of the variance. What is a standard deviation?
Variance: Weeks on Unemployment: X (weeks) Deviation: (raw score from the mean, squared) Variance: Standard Deviation: (square root of the variance) 9 8 6 4 2 1 9-5= 4 8-5=3 6-5=1 4-5=-1 2-5=-3 1-5=-4 42 = 16 32 = 9 12 = 1 -12 = 1 -32 = 9 -42 = 16 (weeks squared) ΣX=30 χ= 30=5 6 s = 2.94 Step 1: Calculate the Mean Step 3: Calculate Sum of square Dev Step 2: Calculate Deviation Step 4: Calculate the Mean of squared dev. Step 5: Calculate the Square root of the Var.
Variance: Weeks on Unemployment: X (weeks) Deviation: (raw score from the mean, squared) Variance: Standard Deviation: s =√Σ(X - χ )2 N (square root of the variance) 9 8 6 4 2 1 9-5= 4 8-5=3 6-5=1 4-5=-1 2-5=-3 1-5=-4 42 = 16 32 = 9 12 = 1 -12 = 1 -32 = 9 -42 = 16 (weeks squared) ____ √ 8.67 ΣX=30 χ= 30=5 6 s = 2.94 Step 1: Calculate the Mean Step 3: Calculate Sum of square Dev Step 2: Calculate Deviation Step 4: Calculate the Mean of squared dev. Step 5: Calculate the Square root of the Var.
Two Ways to Look at Standard Deviation: Sample and Population
S2 = sum of squared deviations divided by the number of scores. The Variance
Some Things to Remember about Sample Variance Uses the deviation from the mean Remember, the sum of the deviations always equals zero, so you have to square each of the deviations S2= sum of squared deviations divided by the number of scores Provides information about the relative variability
It is the typical (standard) difference (deviation) of an observation from the mean. Think of it as the average distance a data point is from the mean, although this is not strictly true. What is a standard deviation?
Two Ways to Look at Standard Deviation: Sample and Population
Variability Weeks on Unemployment: Standard Deviation (S) Thus, in this example, “the distribution deviates from the mean by nearly 3 weeks.” In other words, on average…the scores in this distribution deviate from the mean by nearly 3 weeks.” (71) Judges and Sentencing: You are an attorney with a choice of judges: Judge A: gives all defendants convicted of assault 24 months Judge B: gives all defendants convicted of assault 6-months to 6 years depending on priors.
Prison Sentences: Judge A X (months) X - χ Deviation: (X – χ)2 (raw score from the mean, squared) Variance: S2 =Σ(X – χ)2 N (“the mean of the squared deviation.”) Stand. Deviation: S =√Σ(X - χ )2 N (square root of the variance) 34 30 31 33 36 34 34-33= 1 30-33=-3 31-33=-2 33-33=0 36-33=3 34-33=1 12 = 1 -32 = 9 -22 = 4 02 = 0 32 = 9 12 = 1 24= 4.0 6 (months squared) ____ √ 4.0 ΣX=198 χ= 198= 33 6 Σ(X – χ)2 = 24 S = 2.0 Step 1: Calculate the Mean Step 2: Calculate Deviation Step 3: Calculate Sum of square Dev Step 4: Calculate the Mean of squared dev. Step 5: Calculate the Square root of the Var.
Prison Sentences: Judge B X (months) X - χ Deviation: (X – χ)2 (raw score from the mean, squared) Variance: S2 =Σ(X – χ)2 N (“the mean of the squared deviation.”) Stand. Deviation: S =√Σ(X - χ )2 N (square root of the variance) 26 43 22 35 20 34 36-30= -6 43-30=13 22-30=-8 35-30=5 20-30=-10 34-30=4 -62 = 36 132 = 169 -82 = 64 52 = 25 102 = 100 42 = 8 402= 67.0 6 (months squared) ____ √ 67.0 ΣX=180 χ=180= 30 6 Σ(X – χ)2 =402 S = 8.18 Step 1: Calculate the Mean Step 2: Calculate Deviation Step 3: Calculate Sum of square Dev Step 4: Calculate the Mean of squared dev. Step 5: Calculate the Square root of the Var.
Variability Judges and Sentences: Judge A has a larger mean, smaller variance and standard deviation. “Judge A seems harsher but fairer. Judge B is more lenient but more inconsistent.” (74) Thus Judge A would be a better judge to face. Their mean is larger (so you will get a longer sentence) but are less likely to get longer sentence than if you faced Judge B.
Standard Deviation Is the square root of the variance. Uses the same units of measurement as the raw scores. Tells how much scores deviate below and above the mean
Standard Deviation Standard Deviation: Applications Standard deviation also allows us to: 1) Measure the baseline of a frequency polygon. 2) Find the distance between raw scores and the mean – a standardized method that permits comparisons between raw scores in the distribution – as well as between different distributions.
Standard Deviation Standard Deviation: Baseline of a Frequency Polygon. The baseline of a frequency polygon can be measured in units of standard deviation. Example: = 80 s = 5 Then, the raw score 85 lies one Standard Deviation above the mean (+1s).
Standard Deviation Standard Deviation: The Normal Range Unless highly skewed, approximately two-thirds of scores within a distribution will fall within the one standard deviation above and below the mean. Example: Reading Levels Words per minute. = 120 s = 25
measures-of-variability-11.ppt
measures-of-variability-11.ppt
measures-of-variability-11.ppt
The deviation (definitional) formula for the population standard deviation   N X X    2  The larger the standard deviation the more variability there is in the scores The standard deviation is somewhat less sensitive to extreme outliers than the range (as N increases) Standard Deviation
  N X X S    2 What’s the difference between this formula and the population standard deviation? The deviation (definitional) formula for the sample standard deviation In the first case, all the Xs represent the entire population. In the second case, the Xs represent a sample. Standard Deviation
Calculating S using the Raw-Score Formula   N N X X S     2 2 To calculate ΣX2 you square all the scores first and then sum them To calculate (ΣX)2 you sum all the scores first and then square them Standard Deviation
The Raw-Score Formula: Example   N N X X S     2 2 X X2 21 25 24 30 34 X = 134 441 625 576 900 1151 X2 = 3698 5 5 134 3698 2   S 5 2 . 3591 3698  S 62 . 4 36 . 21   S
Application to Normal Distribution Knowing the standard deviation you can describe your sample more accurately Look at the inflection points of the distribution
measures-of-variability-11.ppt
measures-of-variability-11.ppt
A Note about Transformations: Adding or subtracting just shifts the distribution, without changing the variation (variance). Multiplying or dividing changes the variability, but it is a multiple of the transformation Transformations can be useful when scores are skewed from the “mean” that the researcher prefers to work with. Also, transformations can be helpful when comparing multiple sets of scores.
Standard Deviation: Example X 21 25 24 30 34 0   X X  26.8 -5.8 -1.8 -2.8 3.2 7.2 33.64 3.24 7.84 10.24 51.84 62 . 4 36 . 21 5 8 . 106    S 106.8
A note on N vs. n and N vs. n+1 N is used to represent the number of scores in a population. Some authors also use it to represent the number of scores in a sample as well. n is used to represent the number of scores in a sample. When calculating the variance and standard deviation, you will sometimes see N + 1 or n + 1 instead of N or n. N+1 or n+1 is used by many social scientists when computing from a sample with the intention of generalizing to a larger population. N+1 provides a better estimate and is the formula used by SPSS.
Estimating the population standard deviation from a sample ŝ S, the sample standard, is usually a little smaller than the population standard deviation. Why? The sample mean minimizes the sum of squared deviations (SS). Therefore, if the sample mean differs at all from the population mean, then the SS from the sample will be an underestimate of the SS from the population Therefore, statisticians alter the formula of the sample standard deviation by subtracting 1 from N
Variance is Error in Predictions The larger the variability, the larger the differences between the mean and the scores, so the larger the error when we use the mean to predict the scores Error or error variance: average error between the predicted mean score and the actual raw scores Same for the population: estimate of population variance
Summarizing Research Using Variability Remember, the standard deviation is most often the measure of variability reported. The more consistent the scores are (i.e., the smaller the variance), the stronger the relationship.
Chapter 4: Review
Review: Measures of Variability Just what is variability? Variability is the spread or dispersion of scores. Measuring Variability There are a few ways to measure variability and they include: 1) The Range 2) The Mean Deviation 3) The Standard Deviation 4) The Variance
Review: The Range Measures of Variability Range: The range is a measure of the distance between highest and lowest. R= H – L Temperature Example: Range: Honolulu: 89° – 65° 24° Phoenix: 106° – 41° 65°
Review: Variance and Standard Deviation Variance: is a measure of the dispersion of a sample (or how closely the observations cluster around the mean [average]) Standard Deviation: the square root of the variance, is the measure of variation in the observed values (or variation in the clustering around the mean).
Review: The Variance Remember that the deviation is the distance of any given score from its mean. The variance takes into account every score. But if we were to simply add them up, the plus and minus (positive and negative) scores would cancel each other out because the sum of actual deviations is always zero! ) ( X X  0 ) (    X X
So, what we should we do? We square the actual deviations and then add them together. – Remember: When you square a negative number it becomes positive! SO, S2 = sum of squared deviations divided by the number of scores. The variance provides information about the relative variability. Review: The Variance
Review: Variance: Weeks on Unemployment: X (weeks) Deviation: (raw score from the mean, squared) 9 8 6 4 2 1 9-5= 4 8-5=3 6-5=1 4-5=-1 2-5=-3 1-5=-4 42 = 16 32 = 9 12 = 1 -12 = 1 -32 = 9 -42 = 16 ΣX=30 χ= 30=5 6 Step 1: Calculate the Mean Step 3: Calculate Sum of square Dev Step 2: Calculate Deviation
The mean of the squared deviations is the same as the variance, and can be symbolized by s2 scores of number total mean the from deviations squared the of sum variance where      N X X s 2 2 ) ( Review: The Variance
Review: Variance: Weeks on Unemployment: X (weeks) Deviation: (raw score from the mean, squared) Variance: 9 8 6 4 2 1 9-5= 4 8-5=3 6-5=1 4-5=-1 2-5=-3 1-5=-4 42 = 16 32 = 9 12 = 1 -12 = 1 -32 = 9 -42 = 16 (weeks squared) ΣX=30 χ= 30=5 6 Step 1: Calculate the Mean Step 3: Calculate Sum of square Dev Step 2: Calculate Deviation Step 4: Calculate the Mean of squared dev.
Standard Deviation: It is the typical (standard) difference (deviation) of an observation from the mean. Think of it as the average distance a data point is from the mean, although this is not strictly true. Review: Standard Deviation
Standard Deviation: The standard deviation is calculated by taking the square root of the variance. Review: Standard Deviation?
Review: Standard Deviation: Weeks on Unemployment: X (weeks) Deviation: (raw score from the mean, squared) Variance: Standard Deviation: (square root of the variance) 9 8 6 4 2 1 9-5= 4 8-5=3 6-5=1 4-5=-1 2-5=-3 1-5=-4 42 = 16 32 = 9 12 = 1 -12 = 1 -32 = 9 -42 = 16 (weeks squared) ΣX=30 χ= 30=5 6 s = 2.94 Step 1: Calculate the Mean Step 3: Calculate Sum of square Dev Step 2: Calculate Deviation Step 4: Calculate the Mean of squared dev. Step 5: Calculate the Square root of the Var.

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measures-of-variability-11.ppt

  • 1. Measures of Variability Levin and Fox Chapter 4 Statistics for Political Science
  • 2. Variability Limits of Central Tendencies: Mode, Mean, Median They offer a limited view of data set. Example: Weather in Honolulu and Phoenix Same Mean Temperature: 75° Variability? But how are yearly temperatures distributed on the mean? What is the range of yearly temperatures?
  • 3. Variability But how are yearly temperatures distributed on the mean?
  • 4. Measures of Variability Measures of variability tell us: • The extent to which the scores differ from each other or how spread out the scores are. • How accurately the measure of central tendency describes the distribution. • The shape of the distribution.
  • 5. Measures of Variability Some Definitions: Population (universe) is the collection of things under consideration Sample is a portion of the population selected for analysis Statistic is a summary measure computed to describe a characteristic of the sample
  • 6. Measures of Variability Just what is variability? Variability is the spread or dispersion of scores. Measuring Variability There are a few ways to measure variability and they include: 1) The Range 2) The Mean Deviation 3) The Standard Deviation 4) The Variance
  • 7. Variability Measures of Variability Range: The range is a measure of the distance between highest and lowest. R= H – L Temperature Example: Range: Honolulu: 89° – 65° 24° Phoenix: 106° – 41° 65°
  • 8. The Range The simplest and most straightforward measurement of variation is the range which measures variation in interval-ratio variables. Range = highest score – lowest score R = H – L
  • 9. Range: Examples If the oldest person included in a study was 89 and the youngest was 18, then the range would be 71 years. Or, if the most frequent incidences of disturbing the peace among 6 communities under study is 18 and the least frequent incidences was 4, then the range is 14.
  • 10. Okay, so now you tell me the range… This table indicates the number of metropolitan areas, as defined by the Census Bureau, in six states. What is the range in the number metropolitan areas in these six states? – R=H-L – R=9-3 – R=6 Delaware 3 Idaho 4 Nebraska 4 Kansas 5 Iowa 4 Montana 3 California 9
  • 11. Variance and Standard Deviation Variance: is a measure of the dispersion of a sample (or how closely the observations cluster around the mean [average]). Also known as the mean of the squared deviations. Standard Deviation: the square root of the variance, is the measure of variation in the observed values (or variation in the clustering around the mean).
  • 12. The Variance Remember that the deviation is the distance of any given score from its mean. The variance takes into account every score. But if we were to simply add them up, the plus and minus (positive and negative) scores would cancel each other out because the sum of actual deviations is always zero! ) ( X X  0 ) (    X X
  • 13. So, what we should we do? We square the actual deviations and then add them together. – Remember: When you square a negative number it becomes positive! SO, S2 = sum of squared deviations divided by the number of scores. The variance provides information about the relative variability. The Variance
  • 14. Variance: Weeks on Unemployment: X (weeks) Deviation: (raw score from the mean, squared) 9 8 6 4 2 1 9-5= 4 8-5=3 6-5=1 4-5=-1 2-5=-3 1-5=-4 42 = 16 32 = 9 12 = 1 -12 = 1 -32 = 9 -42 = 16 ΣX=30 χ= 30=5 6 Step 1: Calculate the Mean Step 3: Calculate Sum of square Dev Step 2: Calculate Deviation
  • 15. The mean of the squared deviations is the same as the variance, and can be symbolized by s2 scores of number total mean the from deviations squared the of sum variance where      N X X s 2 2 ) ( The Variance
  • 16. Variance: Weeks on Unemployment: X (weeks) Deviation: (raw score from the mean, squared) Variance: 9 8 6 4 2 1 9-5= 4 8-5=3 6-5=1 4-5=-1 2-5=-3 1-5=-4 42 = 16 32 = 9 12 = 1 -12 = 1 -32 = 9 -42 = 16 (weeks squared) ΣX=30 χ= 30=5 6 Step 1: Calculate the Mean Step 3: Calculate Sum of square Dev Step 2: Calculate Deviation Step 4: Calculate the Mean of squared dev.
  • 17. Standard Deviation: It is the typical (standard) difference (deviation) of an observation from the mean. Think of it as the average distance a data point is from the mean, although this is not strictly true. What is a standard deviation?
  • 18. Standard Deviation: The standard deviation is calculated by taking the square root of the variance. What is a standard deviation?
  • 19. Variance: Weeks on Unemployment: X (weeks) Deviation: (raw score from the mean, squared) Variance: Standard Deviation: (square root of the variance) 9 8 6 4 2 1 9-5= 4 8-5=3 6-5=1 4-5=-1 2-5=-3 1-5=-4 42 = 16 32 = 9 12 = 1 -12 = 1 -32 = 9 -42 = 16 (weeks squared) ΣX=30 χ= 30=5 6 s = 2.94 Step 1: Calculate the Mean Step 3: Calculate Sum of square Dev Step 2: Calculate Deviation Step 4: Calculate the Mean of squared dev. Step 5: Calculate the Square root of the Var.
  • 20. Variance: Weeks on Unemployment: X (weeks) Deviation: (raw score from the mean, squared) Variance: Standard Deviation: s =√Σ(X - χ )2 N (square root of the variance) 9 8 6 4 2 1 9-5= 4 8-5=3 6-5=1 4-5=-1 2-5=-3 1-5=-4 42 = 16 32 = 9 12 = 1 -12 = 1 -32 = 9 -42 = 16 (weeks squared) ____ √ 8.67 ΣX=30 χ= 30=5 6 s = 2.94 Step 1: Calculate the Mean Step 3: Calculate Sum of square Dev Step 2: Calculate Deviation Step 4: Calculate the Mean of squared dev. Step 5: Calculate the Square root of the Var.
  • 21. Two Ways to Look at Standard Deviation: Sample and Population
  • 22. S2 = sum of squared deviations divided by the number of scores. The Variance
  • 23. Some Things to Remember about Sample Variance Uses the deviation from the mean Remember, the sum of the deviations always equals zero, so you have to square each of the deviations S2= sum of squared deviations divided by the number of scores Provides information about the relative variability
  • 24. It is the typical (standard) difference (deviation) of an observation from the mean. Think of it as the average distance a data point is from the mean, although this is not strictly true. What is a standard deviation?
  • 25. Two Ways to Look at Standard Deviation: Sample and Population
  • 26. Variability Weeks on Unemployment: Standard Deviation (S) Thus, in this example, “the distribution deviates from the mean by nearly 3 weeks.” In other words, on average…the scores in this distribution deviate from the mean by nearly 3 weeks.” (71) Judges and Sentencing: You are an attorney with a choice of judges: Judge A: gives all defendants convicted of assault 24 months Judge B: gives all defendants convicted of assault 6-months to 6 years depending on priors.
  • 27. Prison Sentences: Judge A X (months) X - χ Deviation: (X – χ)2 (raw score from the mean, squared) Variance: S2 =Σ(X – χ)2 N (“the mean of the squared deviation.”) Stand. Deviation: S =√Σ(X - χ )2 N (square root of the variance) 34 30 31 33 36 34 34-33= 1 30-33=-3 31-33=-2 33-33=0 36-33=3 34-33=1 12 = 1 -32 = 9 -22 = 4 02 = 0 32 = 9 12 = 1 24= 4.0 6 (months squared) ____ √ 4.0 ΣX=198 χ= 198= 33 6 Σ(X – χ)2 = 24 S = 2.0 Step 1: Calculate the Mean Step 2: Calculate Deviation Step 3: Calculate Sum of square Dev Step 4: Calculate the Mean of squared dev. Step 5: Calculate the Square root of the Var.
  • 28. Prison Sentences: Judge B X (months) X - χ Deviation: (X – χ)2 (raw score from the mean, squared) Variance: S2 =Σ(X – χ)2 N (“the mean of the squared deviation.”) Stand. Deviation: S =√Σ(X - χ )2 N (square root of the variance) 26 43 22 35 20 34 36-30= -6 43-30=13 22-30=-8 35-30=5 20-30=-10 34-30=4 -62 = 36 132 = 169 -82 = 64 52 = 25 102 = 100 42 = 8 402= 67.0 6 (months squared) ____ √ 67.0 ΣX=180 χ=180= 30 6 Σ(X – χ)2 =402 S = 8.18 Step 1: Calculate the Mean Step 2: Calculate Deviation Step 3: Calculate Sum of square Dev Step 4: Calculate the Mean of squared dev. Step 5: Calculate the Square root of the Var.
  • 29. Variability Judges and Sentences: Judge A has a larger mean, smaller variance and standard deviation. “Judge A seems harsher but fairer. Judge B is more lenient but more inconsistent.” (74) Thus Judge A would be a better judge to face. Their mean is larger (so you will get a longer sentence) but are less likely to get longer sentence than if you faced Judge B.
  • 30. Standard Deviation Is the square root of the variance. Uses the same units of measurement as the raw scores. Tells how much scores deviate below and above the mean
  • 31. Standard Deviation Standard Deviation: Applications Standard deviation also allows us to: 1) Measure the baseline of a frequency polygon. 2) Find the distance between raw scores and the mean – a standardized method that permits comparisons between raw scores in the distribution – as well as between different distributions.
  • 32. Standard Deviation Standard Deviation: Baseline of a Frequency Polygon. The baseline of a frequency polygon can be measured in units of standard deviation. Example: = 80 s = 5 Then, the raw score 85 lies one Standard Deviation above the mean (+1s).
  • 33. Standard Deviation Standard Deviation: The Normal Range Unless highly skewed, approximately two-thirds of scores within a distribution will fall within the one standard deviation above and below the mean. Example: Reading Levels Words per minute. = 120 s = 25
  • 37. The deviation (definitional) formula for the population standard deviation   N X X    2  The larger the standard deviation the more variability there is in the scores The standard deviation is somewhat less sensitive to extreme outliers than the range (as N increases) Standard Deviation
  • 38.   N X X S    2 What’s the difference between this formula and the population standard deviation? The deviation (definitional) formula for the sample standard deviation In the first case, all the Xs represent the entire population. In the second case, the Xs represent a sample. Standard Deviation
  • 39. Calculating S using the Raw-Score Formula   N N X X S     2 2 To calculate ΣX2 you square all the scores first and then sum them To calculate (ΣX)2 you sum all the scores first and then square them Standard Deviation
  • 40. The Raw-Score Formula: Example   N N X X S     2 2 X X2 21 25 24 30 34 X = 134 441 625 576 900 1151 X2 = 3698 5 5 134 3698 2   S 5 2 . 3591 3698  S 62 . 4 36 . 21   S
  • 41. Application to Normal Distribution Knowing the standard deviation you can describe your sample more accurately Look at the inflection points of the distribution
  • 44. A Note about Transformations: Adding or subtracting just shifts the distribution, without changing the variation (variance). Multiplying or dividing changes the variability, but it is a multiple of the transformation Transformations can be useful when scores are skewed from the “mean” that the researcher prefers to work with. Also, transformations can be helpful when comparing multiple sets of scores.
  • 45. Standard Deviation: Example X 21 25 24 30 34 0   X X  26.8 -5.8 -1.8 -2.8 3.2 7.2 33.64 3.24 7.84 10.24 51.84 62 . 4 36 . 21 5 8 . 106    S 106.8
  • 46. A note on N vs. n and N vs. n+1 N is used to represent the number of scores in a population. Some authors also use it to represent the number of scores in a sample as well. n is used to represent the number of scores in a sample. When calculating the variance and standard deviation, you will sometimes see N + 1 or n + 1 instead of N or n. N+1 or n+1 is used by many social scientists when computing from a sample with the intention of generalizing to a larger population. N+1 provides a better estimate and is the formula used by SPSS.
  • 47. Estimating the population standard deviation from a sample ŝ S, the sample standard, is usually a little smaller than the population standard deviation. Why? The sample mean minimizes the sum of squared deviations (SS). Therefore, if the sample mean differs at all from the population mean, then the SS from the sample will be an underestimate of the SS from the population Therefore, statisticians alter the formula of the sample standard deviation by subtracting 1 from N
  • 48. Variance is Error in Predictions The larger the variability, the larger the differences between the mean and the scores, so the larger the error when we use the mean to predict the scores Error or error variance: average error between the predicted mean score and the actual raw scores Same for the population: estimate of population variance
  • 49. Summarizing Research Using Variability Remember, the standard deviation is most often the measure of variability reported. The more consistent the scores are (i.e., the smaller the variance), the stronger the relationship.
  • 51. Review: Measures of Variability Just what is variability? Variability is the spread or dispersion of scores. Measuring Variability There are a few ways to measure variability and they include: 1) The Range 2) The Mean Deviation 3) The Standard Deviation 4) The Variance
  • 52. Review: The Range Measures of Variability Range: The range is a measure of the distance between highest and lowest. R= H – L Temperature Example: Range: Honolulu: 89° – 65° 24° Phoenix: 106° – 41° 65°
  • 53. Review: Variance and Standard Deviation Variance: is a measure of the dispersion of a sample (or how closely the observations cluster around the mean [average]) Standard Deviation: the square root of the variance, is the measure of variation in the observed values (or variation in the clustering around the mean).
  • 54. Review: The Variance Remember that the deviation is the distance of any given score from its mean. The variance takes into account every score. But if we were to simply add them up, the plus and minus (positive and negative) scores would cancel each other out because the sum of actual deviations is always zero! ) ( X X  0 ) (    X X
  • 55. So, what we should we do? We square the actual deviations and then add them together. – Remember: When you square a negative number it becomes positive! SO, S2 = sum of squared deviations divided by the number of scores. The variance provides information about the relative variability. Review: The Variance
  • 56. Review: Variance: Weeks on Unemployment: X (weeks) Deviation: (raw score from the mean, squared) 9 8 6 4 2 1 9-5= 4 8-5=3 6-5=1 4-5=-1 2-5=-3 1-5=-4 42 = 16 32 = 9 12 = 1 -12 = 1 -32 = 9 -42 = 16 ΣX=30 χ= 30=5 6 Step 1: Calculate the Mean Step 3: Calculate Sum of square Dev Step 2: Calculate Deviation
  • 57. The mean of the squared deviations is the same as the variance, and can be symbolized by s2 scores of number total mean the from deviations squared the of sum variance where      N X X s 2 2 ) ( Review: The Variance
  • 58. Review: Variance: Weeks on Unemployment: X (weeks) Deviation: (raw score from the mean, squared) Variance: 9 8 6 4 2 1 9-5= 4 8-5=3 6-5=1 4-5=-1 2-5=-3 1-5=-4 42 = 16 32 = 9 12 = 1 -12 = 1 -32 = 9 -42 = 16 (weeks squared) ΣX=30 χ= 30=5 6 Step 1: Calculate the Mean Step 3: Calculate Sum of square Dev Step 2: Calculate Deviation Step 4: Calculate the Mean of squared dev.
  • 59. Standard Deviation: It is the typical (standard) difference (deviation) of an observation from the mean. Think of it as the average distance a data point is from the mean, although this is not strictly true. Review: Standard Deviation
  • 60. Standard Deviation: The standard deviation is calculated by taking the square root of the variance. Review: Standard Deviation?
  • 61. Review: Standard Deviation: Weeks on Unemployment: X (weeks) Deviation: (raw score from the mean, squared) Variance: Standard Deviation: (square root of the variance) 9 8 6 4 2 1 9-5= 4 8-5=3 6-5=1 4-5=-1 2-5=-3 1-5=-4 42 = 16 32 = 9 12 = 1 -12 = 1 -32 = 9 -42 = 16 (weeks squared) ΣX=30 χ= 30=5 6 s = 2.94 Step 1: Calculate the Mean Step 3: Calculate Sum of square Dev Step 2: Calculate Deviation Step 4: Calculate the Mean of squared dev. Step 5: Calculate the Square root of the Var.