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MSU SOC 281 exam 1
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Gravity
Terms in this set (59)
Overgeneralization
generalizing small sample to apply to large group
Selective observation
Only observing things that support your hypothesis
Inaccurate observation
measurement is wrong
Illogical reasoning
no logical connection
Resistance
resisting changing hypothesis based on evidence gathered
Descriptive
researching a description of the topic, describes what things look like
Exploratory
research to gather information about relatively unknown issues
Explanatory
looking to find cause and effect (correlation)
Evaluation
describing the impact of policies on the environment
Qualitative
word based, stories & observations
Quantitative
number based
measurement validity
correct measurement
does a measure measure what we think it measures?
External (generalizability) validity
extent to which we can apply a small sample to a larger population
exists when our findings are generalizable to the larger population
Internal (causal) validity
establish causality
exists when a conclusion that X causes Y is in reality correct.
Time-order
for A to cause B, A must come before B
correlation
Must have a + or - correlation
Nonspurious relationships
the A & B that are reflected can't have a 3rd factor that could impact the relationship
Hypothesis
a tentative statement about empirical reality, involving a relationship between two or more variables.
Empirical generalizations
a statement that describes patterns found in the data
Deductive research
refers to research where a specific theory is tested
Theory--> hypothesis--> data
Inductive research
refers to research where data are examined with no specific expectation in an attempt to develop conclusions/theory
Data--> empirical patterns--> theory
Validity
is achieved when statements or conclusions about empirical reality are correct (do our scientific findings represent reality well?)
Correlation
Do two (or more) variables vary together
Correlations can be positive or negative (or non-existent)
Correlation DOES NOT equal causation
Causality
Does X (IV) cause Y (DV)?
Causality requires correlation
Unit of analysis
where we get our data from (the who or
what that possess the attributes of the hypotheses)
Participants in the study/things that have the attributes
Nominal
simply categories, cannot be ordered in any meaningful way
(e.x. Gender, Race, Religion)
Ordinal
categories can be ordered, but we cannot specify differences/distances between them
(e.x. First/second/third place, Political leanings (very liberal vs. Slightly liberal vs. Moderate))
Interval
specifies differences between values, but lacks a "true zero"
Can tell how much different numerically but no absolute 0 - can get less than 0
(e.x. Temperature (Fahrenheit and Celsius))
Ratio
like interval, but has a "true zero" so that we can compare scores to each other, not just the differences/distance between them
(e.x. Kelvin (0 kelvin is the absolute lowest, complete absence of heat), Age (can't be younger than 0))
Dichotomy
A special case - yes/no questions
Has only two categories
Can be used as if it were a "higher" level of measurement (including ordinal or interval)
Useful in various forms of statistical analysis
Reliability
when an instrument is applied to the same type of phenomenon on different occasions, and if the thing has not changed, then the instrument will be reliable to the extent that it gives the same result
Content validity
when we ask whether the question we are asking is going to measure what we want to measure
Criterion validity
is the measure of interest related to the measure of the same thing from other sources
Construct validity
is the measure of interest related to measures of other variables
Probability sampling
we can specify the probability for each member of the population being selected into a sample
Non-probability sampling
we cannot specify the probability for each member of the population being selected into a sample.
test-retest reliability
do values change from test to test at different points in time?
If what we're studying hasn't changed, results shouldn't either if the test is reliable.
Interitem reliability
Calculates reliability based on the correlation among multiple items used to measure a single concept
Mean
Average obtained by adding up all the scores and dividing by the total number of scores.
Median
the score that divides the distribution into equal parts so that half the cases are above and half below.
Mode
the category or score with the highest frequency in the distribution
Negative skew
median > mean
Positive skew
mean > median
non-skewed distribution
Median=mean
Range
spread of values i.e. from lowest to highest (10-2= 8). minimum and maximum are what their names suggest
Variance & Standard Deviation
ways we measure how scores are dispersed. We usually use the mean as an anchor. The standard deviation is simply the variance "standardized" (square root)
Process for calculating the variance and standard deviation?
Find the mean
Find the deviation: difference between observation and the mean (x-mean)
Square the deviation (avoid negatives)
Sum the squared deviations
Divide by N (total number of cases)
Result is the variance
Take the square root of the variance
Result is the standard deviation
standard deviation (variation²) equation
√∑(x₁ - x)² / N
Yule's Q
A measure of association between two dichotomous variables
Yule's Q equation
Q = (ad) - (cb) / (ad) + (cb)
1a 2b
3c 4d
Pearson correlation coefficient (r)
A measure of association between two interval or ratio-level variables
Bivariate relationship
comparing two different things (temperature & ice cream sales)
measures of association
Yule's Q and Pearsons r
between -1.0 and +1.0
Calculating r
R = covariance (X, Y) / Standard deviation (X) x SD (Y)
Covariance (X,Y) = Σ(Xi - X)(Yi - Y) / N
R = (Σ(Xi - X)(Yi - Y) / N) / (Standard deviation (X) x SD (Y))
SD = sq(Σ(Xi - X)2 / N)
negligible
.01-.09
low
.10-.29
moderate
.30-.49
substantial
.50-.69
very strong
.70-1.00
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