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Spring 2004Copyright Barbara Mitchell, MA, CSP, Cambria Cty Comm Coll 1 Parsing* Peanut Butter An investigation of data types in the behavioral sciences *Parse: to analyze something in an orderly way. featuring

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Spring 2004 Copyright Barbara Mitchell, MA, CSP, Cambria Cty Comm Coll page 2 Knowing About Data Types Who cares? You do! Why? –Knowing the type of data helps you to judge the validity (value??) of research And therefore the amount of “trust” you can put in a claim –Knowing the type of data allows you to choose the type of statistical analysis to conduct Based on Graziano & Raulin, 2004, Allyn Bacon

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Spring 2004 Copyright Barbara Mitchell, MA, CSP, Cambria Cty Comm Coll page 3 Let’s get started There are fundamentally two types of data –Categorical –Continuous Measurement (Score data)

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Spring 2004 Copyright Barbara Mitchell, MA, CSP, Cambria Cty Comm Coll page 4 Categorical sorts data into categories or groups Nominal –where the order of the items or objects is arbitrary (unordered) –For example: all colors of cats Ordinal –where there is a logical ordering to the items or objects (ordered or ranked in a meaningful way) –For example: all shades of blue

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Spring 2004 Copyright Barbara Mitchell, MA, CSP, Cambria Cty Comm Coll page 5 Nominal Datum (plural=Data) The only property of nominal data is IDENTITY Nominal Scales are NAMING scales: –Names of babies –Names of political parties Numbers may be assigned but they are meaningless, ex. Blue = 1 and red = 2 does not mean that blue is ½ of red Nominal Scales classify or categorize Nominal data can only be understood in terms of the frequency of occurrence Chi Square is the most common statistical test for nominal data

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Spring 2004 Copyright Barbara Mitchell, MA, CSP, Cambria Cty Comm Coll page 6 Test yourself Nominal or Ordinal –Learning style –Non-smoker –Peanut butter taste (good, bad) –Freshman, sophomore, junior, senior –Peanut butter taste (pleasant, less than pleasant, indifferent, a little odd, unpleasant Nominal Ordinal Not so good-Go BackDid OK – Move On

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Spring 2004 Copyright Barbara Mitchell, MA, CSP, Cambria Cty Comm Coll page 7 Another Test Data on juice preferences Data on Shreck likeability Data on myth gullibility Data on illness severity Number of students getting an “A” Crayon color (red blue, green) Ordinal Nominal

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Spring 2004 Copyright Barbara Mitchell, MA, CSP, Cambria Cty Comm Coll page 8 So What’s with the Peanut Butter? You can collect Nominal data You can collect Ordinal data Do you like Peanut Butter? (YES or NO) How much different is brand X from brand Y?

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Spring 2004 Copyright Barbara Mitchell, MA, CSP, Cambria Cty Comm Coll page 9 Nominal Data IS –Unordered –Objects cannot be put in a logical (increasing or decreasing) sequence –There is no better or worse –Often found in a YES/NO (more/less) format

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Spring 2004 Copyright Barbara Mitchell, MA, CSP, Cambria Cty Comm Coll page 10 Statistical Evaluation of Nominal Data Since you cannot mathematically operate on YES /NO type questions… And you have the problem that there is no MAGNITUDE between any two items, by definition, cannot be the same… Your analytical choices are –Frequency distributions, percentages –Chi-square(Χ 2 ) goodness of fit, and (Χ 2 ) test for independence

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Spring 2004 Copyright Barbara Mitchell, MA, CSP, Cambria Cty Comm Coll page 11 Ordinal Data IS –In some sort of logical order –Each relates to the other in some way –May indicate a preference or value, asks HOW MUCH? But is not ‘measurable’ –Most often seen in a “Likert” scale “totally agree’ |‘agree’ | ‘no preference’ | ‘disagree’ | ‘totally disagree’ In 1932, Renis Likert invented a measurement method, called the Likert Scales, used in attitude surveys. They allowed answers that ranged from "strongly disagree" to "strongly agree." Retrieved 3.14.04 from www.nwlink.com/~donclark/hrd/history/likert.html

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Spring 2004 Copyright Barbara Mitchell, MA, CSP, Cambria Cty Comm Coll page 12 Ordinal Data Properties Have IDENTITY and MAGNITUDE as properties Numbers represent an order of some sort City size (sq. miles, or # of people), class rank, grades of ABCDF are ordinal data. Ordinal data CANNOT be compared because there is no information about the differences between categories or ranks For example – a LIKERT SCALE of |The Best!| Nice| OK| So-So| Uck!| Tells us nothing about the DISTANCE between each choice. There is no way to measure relative size.

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Spring 2004 Copyright Barbara Mitchell, MA, CSP, Cambria Cty Comm Coll page 13 Statistical Evaluation of Ordinal Data Ordered (ordinal) data indicated that there is a relationship between the elements in the group. A Median can be located in an ordinal grouping to measure central tendency, A Range or Inter-quartile range can be calculated for variability, If describing a relationship in the data, then a Spearman rank-order correlation between the items can be calculated. Ways to DESCRIBE the data

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Spring 2004 Copyright Barbara Mitchell, MA, CSP, Cambria Cty Comm Coll page 14 Statistical Evaluation of Ordinal Data 2 If you have 2 groups (IV1, IV2), and –The groups are independent, then the Mann- Whitney U-test is used –The groups are not independent, the the Wilcoxon signed-rank test is used If more than 2 groups, and –The groups are independent, then Kruskal-Wallis one-way ANOVA –The groups are not independent, the Friedman 2- way ANOVA (see p.325 in Graziano & Raulin, 2004) Ways to make INFERENCES about the data

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Spring 2004 Copyright Barbara Mitchell, MA, CSP, Cambria Cty Comm Coll page 15 Quiz Time Take the Quiz on Course Compass (Categorical Quiz) If you did well, link to Part 2 If you need to see this section again

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Spring 2004 Copyright Barbara Mitchell, MA, CSP, Cambria Cty Comm Coll page 16 Part 2a – Score Data (continuous) Interval and Ratio data is continuous with equal intervals between points……. –Have equal intervals between each measure, –That is, the distance (depth, temperature, amount) from one point to the next (say point 1 to point 2) is the same as the interval between any other two consecutive points (so it would be the same from point 30 to point 31) –All interval and ratio data can be operated on mathematically (add, subtract, multiply, divide, square root)* Ratio data has a meaningful ZERO point Our book states that you can only add and subtract with interval data, however, others argue that you can conduct all five operations on interval data. Ratios can only be calculated on ratio data

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Spring 2004 Copyright Barbara Mitchell, MA, CSP, Cambria Cty Comm Coll page 17 Part 2b – Interval Data Properties Interval data have the properties if IDENTITY, MAGNITUDE, and EQUAL INTERVALS between values on the scale- They ask HOW MUCH? Interval: –Fahrenheit & Centigrade (Zero is a reference point) –Calendar Years –I.Q. –Family Income Note about I.Q. type tests: We CAN say that a score of 120 points is 60 points higher than a score of 60 points. But we CANNOT say that the one person is twice a smart as the other because there is NO TRUE ZERO

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Spring 2004 Copyright Barbara Mitchell, MA, CSP, Cambria Cty Comm Coll page 18 Interval Statistical Properties Interval data cane be ADDED and SUBTRACTED. The Students t-test and the Analysis of Variance (ANOVA) are statistical tests that respect the limits if INTERVAL data

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Spring 2004 Copyright Barbara Mitchell, MA, CSP, Cambria Cty Comm Coll page 19 Part 2c – Ratio Data Properties Ratio data has the properties of IDENTITY, MAGNITUDE, EQUAL INTERVAL, and a TRUE ZERO. Ratio –Kelvin (absolute Zero) –Years of Experience –Length –Age –Reaction times

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Spring 2004 Copyright Barbara Mitchell, MA, CSP, Cambria Cty Comm Coll page 20 Ratio Statistical Tests The Students t-test and the Analysis of Variance (ANOVA) are statistical tests Product moment correlational tests, many others. Ratio/proportional data

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Spring 2004 Copyright Barbara Mitchell, MA, CSP, Cambria Cty Comm Coll page 21 Test yourself Interval or Ratio Data –Response time to a customer –Smoking Reduction Program –Score on the SAT –Effects of medication training program –Improving speed of response Interval Ratio Interval Not so good-Go BackDid OK – Move On

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Spring 2004 Copyright Barbara Mitchell, MA, CSP, Cambria Cty Comm Coll page 22 Another Test Sugar concentration in Peanut Butter Variation of Volume on Shrek Movie and viewer complaints Fat content of Peanut Butter & salivation Data on childhood temperature ranges SAT score of 560 Crayon quality as a % of wax Ratio Interval Ratio

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Spring 2004 Copyright Barbara Mitchell, MA, CSP, Cambria Cty Comm Coll page 23 More about Peanut Butter? Interval Scale: –Measuring sugar concentration in the mouth following eating of several different brands of peanut butter Ratio Scale –Measuring fat concentrations of peanut butter relative to the amount of liquid drunk following the ingestion of 2 tablespoons of peanut butter.

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Spring 2004 Copyright Barbara Mitchell, MA, CSP, Cambria Cty Comm Coll page 24 Statistical Tests for Interval and Ratio Data Descriptive: –Mean –Median –Mode Variability –Variance –Standard Deviation Related Groups: –Pearson product-moment correlation Inferential: –T-test –ANOVA See pp. 323 & 324 in Graziano & Raulin, 2004

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Spring 2004 Copyright Barbara Mitchell, MA, CSP, Cambria Cty Comm Coll page 25 Quiz Time Take the Quiz on Course Compass (Score Quiz) If you did well, link If you need to see Section 2 again

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Spring 2004 Copyright Barbara Mitchell, MA, CSP, Cambria Cty Comm Coll page 26 PaRtY! By, George, I think you’ve gOt iT!

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