Introducing Communication Research 2e © 2014 SAGE Publications
Chapter Seven
Generalizing From Research
Results: Inferential Statistics
Key Concepts
• Statistical significance.
• Normal distribution.
• Random sampling.
• Confidence level & confidence interval.
• Parametric vs. non-parametric.
• t-test, ANOVA & correlation.
• Type I and Type II error.
Introducing Communication Research 2e © 2014 SAGE Publications
Inferential Statistics
Inferential statistics help us –
• Estimate the probability that a sample
represents the population it came from.
• Decide whether groups differ significantly on a
variable.
• Decide whether there are significant
relationships among or between variables.
Introducing Communication Research 2e © 2014 SAGE Publications
Inferential Statistics and
the Normal Curve
• Inferential statistics assume that the values of a
variable in a population are normally
distributed.
• Assuming a normal distribution of values in a
population, we can calculate the probability that
a sample of that population has captured its
characteristics.
Introducing Communication Research 2e © 2014 SAGE Publications
The Normal Curve and
Standard Deviation
In a normal distribution –
• 68% of values occur plus or minus one
standard deviation (SD) from the mean.
• 95% of its values occur plus or minus two SDs
from the mean.
• 99.7% of its values occur plus or minus three
SDs from the mean.
Introducing Communication Research 2e © 2014 SAGE Publications
Statistical Significance
• Significance means that there is a better than
random chance that a relationship exists.
Introducing Communication Research 2e © 2014 SAGE Publications
Confidence Interval and
Confidence Level
• Confidence Interval
The possible range of values for a variable in a
population, calculated from a sample of the
population.
• Confidence Level
The probability of a calculated value occurring.
• Example –
There is a 95% probability that the mean lies between 0.98
and 10.42.
Introducing Communication Research 2e © 2014 SAGE Publications
For a Sample
• The distribution of the sample results is
called the sampling distribution.
• The standard deviation is called the
standard error.
• Standard error decreases as sample size
increases.
Introducing Communication Research 2e © 2014 SAGE Publications
Parametric vs Non-parametric
Statistics
• Parametric Statistics
Used where we can assume a normal distribution
of values in the population.
• Non-Parametric Statistics
Used where we cannot assume a normal
distribution of values in the population.
Introducing Communication Research 2e © 2014 SAGE Publications
Standard Deviation
• A measure of the extent to which a set of scores
vary on either side of their mean value.
• The square root of variance.
• For a sample, standard deviation is referred to as
the standard error.
Introducing Communication Research 2e © 2014 SAGE Publications
t-Test
Compares mean scores from two groups to
determine the probability the groups are
different.
• t-test for independent samples -
Used where the groups are different.
• t-test for dependent samples -
Used where the groups are the same.
• The higher the t-value, the lower the probability
it will occur.
Introducing Communication Research 2e © 2014 SAGE Publications
Degrees of Freedom
• A measure of “room to move.”
▫ How many ways could our data be combined and
still produce the same values for a statistic?
Introducing Communication Research 2e © 2014 SAGE Publications
One and Two-tailed Tests
• One-tailed Test
Presupposes that a difference between groups
will be directional
for example, that a value for group A will be
higher than for group B.
• Two-tailed Test
Presupposes that the difference will be non-
directional
for example, group A and group B simply differ;
either group could score higher or lower.
Introducing Communication Research 2e © 2014 SAGE Publications
Analysis of Variance (ANOVA)
• Assesses the probability of difference among two
or more groups.
• Compares the variance within groups with the
variance between groups.
• One-way ANOVA compares one variable across
more two or more groups.
• Multiple Analysis of Variance (MANOVA)
assesses relationships among multiple variables
across two or more groups.
Introducing Communication Research 2e © 2014 SAGE Publications
Correlation and Regression
Correlation – a measure of the strength of
association among and between variables.
Correlation coefficient - expresses the
strength of association between variables.
Regression - predicts a value for one variable
given a value for another variable.
• Relationships between variables may plot out as
linear or curvilinear
Introducing Communication Research 2e © 2014 SAGE Publications
Type I and Type II Error
Accept null hypothesis Reject null hypothesis
Null hypothesis is true in
the wider population.
No problem. Type I Error.
Decided wrongly that
there was a significant
result.
Null hypothesis is false
in the wider population.
Type II Error.
Decided wrongly that
there was no significant
result.
No problem.
Introducing Communication Research 2e © 2014 SAGE Publications
Chapter Summary
• Inferential statistics assess –
▫ the probability that a sample represents a population
▫ the strength of relationships among variables
▫ the strength of differences among groups.
• t-test – compares values of a continuous variable for two
groups.
• Correlation - assesses the strength of association
between variables.
• Regression - computes the values of one variable, given
the values of another.
• ANOVA compares values of a variable across multiple
groups.
Introducing Communication Research 2e © 2014 SAGE Publications
Vocabulary Review
Introducing Communication Research 2e © 2014 SAGE Publications
Web Resources
t- test Calculators
• http://www.physics.csbsju.edu/stats/t-test_NROW_form.htm
• http://graphpad.com/quickcalcs/ttest1.cfm
Introducing Communication Research 2e © 2014 SAGE Publications

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  • 1.
    Introducing Communication Research2e © 2014 SAGE Publications Chapter Seven Generalizing From Research Results: Inferential Statistics
  • 2.
    Key Concepts • Statisticalsignificance. • Normal distribution. • Random sampling. • Confidence level & confidence interval. • Parametric vs. non-parametric. • t-test, ANOVA & correlation. • Type I and Type II error. Introducing Communication Research 2e © 2014 SAGE Publications
  • 3.
    Inferential Statistics Inferential statisticshelp us – • Estimate the probability that a sample represents the population it came from. • Decide whether groups differ significantly on a variable. • Decide whether there are significant relationships among or between variables. Introducing Communication Research 2e © 2014 SAGE Publications
  • 4.
    Inferential Statistics and theNormal Curve • Inferential statistics assume that the values of a variable in a population are normally distributed. • Assuming a normal distribution of values in a population, we can calculate the probability that a sample of that population has captured its characteristics. Introducing Communication Research 2e © 2014 SAGE Publications
  • 5.
    The Normal Curveand Standard Deviation In a normal distribution – • 68% of values occur plus or minus one standard deviation (SD) from the mean. • 95% of its values occur plus or minus two SDs from the mean. • 99.7% of its values occur plus or minus three SDs from the mean. Introducing Communication Research 2e © 2014 SAGE Publications
  • 6.
    Statistical Significance • Significancemeans that there is a better than random chance that a relationship exists. Introducing Communication Research 2e © 2014 SAGE Publications
  • 7.
    Confidence Interval and ConfidenceLevel • Confidence Interval The possible range of values for a variable in a population, calculated from a sample of the population. • Confidence Level The probability of a calculated value occurring. • Example – There is a 95% probability that the mean lies between 0.98 and 10.42. Introducing Communication Research 2e © 2014 SAGE Publications
  • 8.
    For a Sample •The distribution of the sample results is called the sampling distribution. • The standard deviation is called the standard error. • Standard error decreases as sample size increases. Introducing Communication Research 2e © 2014 SAGE Publications
  • 9.
    Parametric vs Non-parametric Statistics •Parametric Statistics Used where we can assume a normal distribution of values in the population. • Non-Parametric Statistics Used where we cannot assume a normal distribution of values in the population. Introducing Communication Research 2e © 2014 SAGE Publications
  • 10.
    Standard Deviation • Ameasure of the extent to which a set of scores vary on either side of their mean value. • The square root of variance. • For a sample, standard deviation is referred to as the standard error. Introducing Communication Research 2e © 2014 SAGE Publications
  • 11.
    t-Test Compares mean scoresfrom two groups to determine the probability the groups are different. • t-test for independent samples - Used where the groups are different. • t-test for dependent samples - Used where the groups are the same. • The higher the t-value, the lower the probability it will occur. Introducing Communication Research 2e © 2014 SAGE Publications
  • 12.
    Degrees of Freedom •A measure of “room to move.” ▫ How many ways could our data be combined and still produce the same values for a statistic? Introducing Communication Research 2e © 2014 SAGE Publications
  • 13.
    One and Two-tailedTests • One-tailed Test Presupposes that a difference between groups will be directional for example, that a value for group A will be higher than for group B. • Two-tailed Test Presupposes that the difference will be non- directional for example, group A and group B simply differ; either group could score higher or lower. Introducing Communication Research 2e © 2014 SAGE Publications
  • 14.
    Analysis of Variance(ANOVA) • Assesses the probability of difference among two or more groups. • Compares the variance within groups with the variance between groups. • One-way ANOVA compares one variable across more two or more groups. • Multiple Analysis of Variance (MANOVA) assesses relationships among multiple variables across two or more groups. Introducing Communication Research 2e © 2014 SAGE Publications
  • 15.
    Correlation and Regression Correlation– a measure of the strength of association among and between variables. Correlation coefficient - expresses the strength of association between variables. Regression - predicts a value for one variable given a value for another variable. • Relationships between variables may plot out as linear or curvilinear Introducing Communication Research 2e © 2014 SAGE Publications
  • 16.
    Type I andType II Error Accept null hypothesis Reject null hypothesis Null hypothesis is true in the wider population. No problem. Type I Error. Decided wrongly that there was a significant result. Null hypothesis is false in the wider population. Type II Error. Decided wrongly that there was no significant result. No problem. Introducing Communication Research 2e © 2014 SAGE Publications
  • 17.
    Chapter Summary • Inferentialstatistics assess – ▫ the probability that a sample represents a population ▫ the strength of relationships among variables ▫ the strength of differences among groups. • t-test – compares values of a continuous variable for two groups. • Correlation - assesses the strength of association between variables. • Regression - computes the values of one variable, given the values of another. • ANOVA compares values of a variable across multiple groups. Introducing Communication Research 2e © 2014 SAGE Publications
  • 18.
    Vocabulary Review Introducing CommunicationResearch 2e © 2014 SAGE Publications
  • 19.
    Web Resources t- testCalculators • http://www.physics.csbsju.edu/stats/t-test_NROW_form.htm • http://graphpad.com/quickcalcs/ttest1.cfm Introducing Communication Research 2e © 2014 SAGE Publications