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Chapter 12

Department

PsychologyCourse Code

PSYB01H3Professor

Connie BoudensChapter

12This

**preview**shows pages 1-2. to view the full**6 pages of the document.**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

SCALES OF MEASUREMENT: A REVIEW

•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

ANALYZING THE RESULTS OF RESEARCH INVESTIGATIONS

•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

groups

FREQUENCY DISTRIBUTIONS

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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

variable

•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

oHistograms

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

•Descriptive statistics allow researchers to make precise statements about the data – two 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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