false

Textbook Notes
(368,966)

Canada
(162,317)

University of Toronto Scarborough
(18,538)

Psychology
(9,697)

PSYB01H3
(581)

Nussbaum D
(52)

Chapter 10

Unlock Document

Psychology

PSYB01H3

Nussbaum D

Fall

Description

CHAPTER 10
Does Spending Money On Others Promote Happiness?
Participants randomly assigned to:
Money condition($5 or $20)
Spending condition (self or others)
2 x 2 between-subjects factorial design
Statistical Approach
Descriptive statistics: used to describe the variables in a study, both one at a time and
in terms of their relations to each other.
Age, gender, socioeconomic status
Inferential statistics: used to estimate characteristics of a population from those that
were found in a random sample of that population.
Can also be used to test hypothesis about the relationships between variables
Level of Measurement - review
Nominal: categorical level of measurement
Interval: numbers indicating a variable’s values represent fixed measurement units but
have no absolute, or fixed, zero point
Ratio: numbers indicating a variable’s values represent fixed measuring units and have
an absolute zero point
Frequency of Distributions
Def.: shows the number of cases and/or the percentage of cases who receive each
possible score on a variable
Often precedes the formal statistical analysis
May group the values if:
There are more than 15-20/ category
It would clarify the distribution
Guidelines for combining values in a frequency distribution:
Categories should be logically defensible and preserve the distribution’s shape
Categories should be mutually exclusive and exhaustive so that every case
should be classifiable in one and only one category
Graphing Bar charts
Bars separated by spaces
Good for nominal data
Histograms
Displays a frequency distribution of a quantitative variable
Avoiding Misleading Graphs
Begin the graph of a quantitative variable at 0 on both axes
Always use bars of equal width
The two axes should be of approximately equal length
Avoid “chart junk”
Stupid lines, cross-hatching, too many marks
Descriptive Statistics
Whatever display is used, is important to preserve shape of graph. This means thinking
of central tendency, variability, and skewness.
Variability: the extent to which cases are spread out through the distribution or
clustered in just one location
Skewness: extent to which cases are clustered more at one or other end of the
distribution of a quantitative variable rather than in a symmetric pattern around its
center.
o Positive Right skew, tapering off in positive direction
o Negative Left skew, tapering off in negative direction
Measures of Central Tendency
Central tendency: most common value or the value around which cases tend to center
Choosing the appropriate measure of central tendency, the researcher needs to
consider level of measurement, skewness of the distribution of the variable, and the
purpose for which the statistic is used.
Mode (probability average)
Most frequent score
May be more than one
May fall far from the main clustering of cases in a distribution
Mode is used less often because it can give misleading impression of a distribution
central tendency. This can occur when a distribution is bimodal (has two or more categories with
an equal number of cases and with more cases than any other categories) rather
than unimodal (single mode). This means that there is no single mode.
Mode can fall far from the main clustering of cases in a distribution. Gives
misleading to say that the central tendency was the mode.
Median (position average)
Point that divides the distribution in half
Cannot be used at the nominal level
Adding the frequencies of the two middle values and dividing by two.
Mean (arithmetic average)
Sum numbers and divide by N
Cannot be used at the nominal level (and sometimes not at the ordinal level)
Mean vs. Median
Should consider the purpose of the statistic
Median often makes more sense if the scale is at the Ordinal level
Median is better for skewed distributions
Cannot use either of these for values at the nominal level, as the different
attributes of a variable cannot be ordered as higher or lower
Mean is most often used for quantitative variables
Variability
Variance: Average squared deviation of each case from the mean
Range
High score – low score
Drastically influenced by one high or low score
Range = highest value – lowest value + 1
Standard Deviation: Square root of the variance
Sampling Distributions
Distribution of statistics representing all possible samples drawn from a population of a
set size
Mean is the same as the population mean Standard error of the mean: degree to which the means of the samples vary from
the population mean
Inferential Statistics
Confidence that the mean of a random sample from a population is within a certain
range of the population mean
Calculating confidence limits:
1. Calculate the standard error
a.
b. N= n-1
2. Decide on a degree of confidence – this can be 95%, 99%, or 99.9%.
a. Usually, 95% is used
3. Multiply the standard error by 1.96
4. Add and subtract the value in step 3 from the sample mean
Inferential statistics (Cont.)
Can be used to estimate a population parameter from a sample statistic
Can also be used to test a hypothesis
Two of the most common hypothesis tests:
t-test
F-test
Hypothesis Testing

More
Less
Related notes for PSYB01H3

Join OneClass

Access over 10 million pages of study

documents for 1.3 million courses.

Sign up

Join to view

Continue

Continue
OR

By registering, I agree to the
Terms
and
Privacy Policies

Already have an account?
Log in

Just a few more details

So we can recommend you notes for your school.

Reset Password

Please enter below the email address you registered with and we will send you a link to reset your password.