chapter 13 descriptive data analysis. statistics science is empirical in that knowledge is acquired...
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Chapter 13
Descriptive Data Analysis
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Statistics
Science is empirical in that knowledge is acquired by observation
Data collection requires that we make measurements of our observations
Measurements then yield data Statistics are used for analyzing data
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3 Basic Steps in Data Analysis
1. Select the appropriate statistical technique2. Apply the technique3. Interpret the result
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Descriptive statistics
Used to organize, simplify, and summarize the collected data
Data typically consist of a set of scores called a distribution. These scores result from the measurements taken
The original measurements or values in a distribution are called raw scores
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Types of Scores
Continuous a continuous progression from the smallest possible
amount to the largest possible amount, with measurement theoretically possible at any point along the continuum; may be expressed as a fraction (e.g., height, weight, temperature, strength)
Discrete measurement and classification are possible only in
whole units; no fractional units (e.g., size of family, number of schools in country)• Dichotomous – 2 category variable (yes/no;
alive/dead)
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Scales of Measurement
Nominal Ordinal Interval Ratio
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Nominal
Merely classifies objects in accordance with similarities and differences with respect to some property; no hierarchy of scores
Examples • color of hair• gender• response to a yes/no question• shoe preference
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Ordinal
Type of data that is characterized by the ability to rank order on the basis of an underlying continuum
No common unit of measurement Examples
• class ranks• place of finish in a race
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Interval
Data having known and equal distances between score units, but having an arbitrary zero point
Example• temperature on Fahrenheit scale
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Ratio
Possesses same properties of interval data, but does have a true zero point
Examples• height or weight• distance measurement
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Computer Analysis
Variety of computer programs for statistical computations; mainframe and desktop SPSS
• See Appendix A in textbook for more information SAS Statview Excel
Fast, easy to use, widely available
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Organizing and Graphing Scores
Frequency distributions Simple frequency distribution Group frequency distribution
Graphing techniques Histogram Frequency polygon
Normal curve Bell-shaped curve Skewed distribution
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Simple Frequency Distribution
Score Frequency Cumulative Freq.
X f cf
22 1 15
19 2 14
18 3 12
17 5 9
16 2 4
13 1 2
11 1 1
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Group Frequency Distribution
Class Interval f cf
66 – 68 2 30
63 – 65 4 28
60 – 62 2 24
57 – 59 2 22
54 – 56 2 20
51 – 53 3 18
48 – 50 2 15
45 – 47 1 13
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Histogram
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11 13 16 17 18 19 22
Fre
quen
cy
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Frequency Polygon
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11 13 16 17 18 19 22
Fre
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Normal Curve
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Symmetrical Curves
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Distribution Shapes
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Types of Descriptive Statistics
Measures of Central Tendency mean median mode
Measures of Variability standard deviation variance range minimum/maximum
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Measuring Group Position
Percentile ranks and percentile Standard scores
z score T score
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Relationships Among Variables
Correlational Statistics Correlation is a family of statistical techniques
that is used to determine the relationship between 2 or more variables• correlation coefficient ranges from -1.0 to +1.0• scatterplot is a graphic illustration of the
relationship between 2 variables• correlation provides information about the
magnitude and direction of a relationship, but does not imply a cause-and-effect relationship between the variables
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Correlational Techniques
Pearson product-moment correlation (r) requires interval or ratio scores every subject has scores on two variables most frequently used
Spearman rank-order correlation (rs) nonparametric technique for use with ordinal
scores every subject has scores on two variables
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Interpretation of Correlation
Coefficient of determination (r2) Portion of the total variance in a variable that
can be explained or accounted for by the variance of the other variable
Square of the correlation coefficient
If r = .70 … then r2 = .49
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Question of Accuracy
Linear relationship Curvilinear relationship Reliability of test scores
Low reliability reduces correlation Range of scores
Correlation will be smaller for a homogeneous group than a heterogeneous group