Chapter 12 - Detailed & Easy to Learn

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13 Dec 2010
Chapter 12 – Understanding Research Results: Description and Correlation
There are 2 reasons for using statistics: first, statistics are used to describe the data and second,
statistics are used to make inferences, on the basis of simply data, about a population
The levels of the variable can be described using 1 of 4 scales of measurement: nominal, ordinal,
interval and ratio
The levels of nominal scale variables have no numerical, quantitative properties. The levels are
simply different categories or groups. Most independent variable in experiments are nominal
Variables with ordinal scale levels involve minimal quantitative distinctions. We can rank order
the levels of the variable being studies from lowest to highest
Interval and ratio scale variables have much more detailed quantitative properties. With an
interval scale variable, the intervals between the levels are equal in size. There is no absolute zero
point that indicated an “absence” of mood
Ratio scale variables have both equal intervals and an absolute zero point that indicates the
absence of the variable being measured. Time, weight, length etc are the best examples
Depending on the way that the variables are studies, there are 3 basic ways of describing the
results: (1) comparing group percentages, (2) correlating scores of individuals on two variables
and (3) comparing group means
Comparing group percentages
Correlating individual scores
oNeeded when you do not have distinct groups of subjects
oInstead, individuals are measure on 2 variables, and each variable has a range of
numerical values
Comparing group means
oMuch research is design to compare the mean responses of participants in two or more
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When analyzing results it is useful to start by constructing a frequency distribution of data
A frequency distribution indicates the number of individuals that receive each possible score on a
Graphing frequency distributions
oPie charts
Divide a whole circle or pie” into slices that represent relative percentages
Pie charts are particularly useful when representing nominal scale information
oBar graphs
Use a separate and distinct bar for each piece of information
oFrequency polygons
Use a line to represent frequencies
This is most useful when the data represent interval or ratio scales
Uses bars to display a frequency distribution for a quantitative variable
The scale values are continuous
What can you discover by examining frequency distributions? First, you can directly observe how
your participants responded. You can see what scores are most frequent and you can look at the
shape of the distribution of scores. You can tell whether there are any outliers
Descriptive statistics allow researchers to make precise statements about the datatwo numbers
are needed to describe the data: a single number can be used to describe the central tendency and
another number describes the variability
Central tendency
oA CT statistic tells us what the sample as a whole, or on the average, is like
oThere are 3 measures of central tendency: mean, median and mode
oThe mean of a set of scores is symbolized by X in scientific reports and abbreviated as M
—the mean is an appropriate indicator of central tendency only when scores are measured
on an interval or ratio scale, because the actual values of the numbers are used in
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