Miscellaneous Quiz / Social Research Methods

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Can you name the Social Research Methods

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The test covers the content it set out to test
The results can be used to infer real-world suggestions
Collection of units we want to generalise findings for
Smaller collection of units used to determine truths
Discrepancy between actual value and number being used to measure it
Theory of cause and effect
Proposed cause and predictor variable
Proposed effect and outcome variable
The ability to produce the same results under the same conditions
The ability to produce consistent results that are the same when done at different times
There is no effect - H(0)
There is some effect - H(1)
Difference between the mean and actual data point
Square deviations to stop + and - cancelling out each other
Average variability in a sample- S(2)
Square root value of the variance
Level of fit of the mean of data, variability of data, How well the mean represents the data
Data on graph grouped relatively close together
Data on graph much more spread out
Mean and Standard Deviation only reflect sample
Mean and Standard Deviation used to estimate population mean etc
Bell Curve
Smallest score subtracted from the largest
Three values that split the data into four equal groups
Middle Value
Occurs when we think there's an effect on the population when there isn't
Occurs when we don't think there's an effect on the population when there actually is
Variables eg gender, hair colour
Variables that are in order eg education status
Variables that are equally spaced in order eg pounds sterling
If the p value is less than this number it is significant
Each person in the population has an equal chance of being chosen for the sample
Division of the population into characteristics that are then replicated in division of sample
Dividing population into groups, and taking samples from these eg geographic areas
probability sampling is
samples that don't stem from a random choice
Choosing the nth number for samples
Researcher sets a quota of people that need asking
Each respondent leads to the next
Asking respondents who are easy to contact
Deriving a theory from data
looking for data to prove/disprove a theory
Describes a case in terms of a single variable eg Descibes a case in terms of the ditribution of attributes that comprise it such as Gender - number of women,number of men and prop
Subgroup comparison - describes a case in terms of two variables simulaneously
analysis of three or more variables simultaneously
Data collection of different entities in experimental conditions
Data collection of the same entities take part in all experimental conditions
These represent: The level of fit of the mean to the data, the variability in the data, how well the mean represents the observed data
Observed values can be used to test hypotheses using test statistics to calculate whether a result is...
Looks at the frequency with which cases fall into each combination of categories - relationships between categorical variables- looks at whether there is any systematic relationshi
Looks at the difference between what we observe and what would be likely if there were no difference except that generated by chance
Can only use chi-square tests where each cell in the table has an expected value of at least
Tells you how strong associations are
(Cramer's V) Values between 0 to +/- 0.25
(Cramer's V) Values between +/- 0.26 and +/- 0.50
(Cramer's V) Values between +/- 0.51 and +/- 0.75
(Cramer's V) Values between +/- 0.76 and +/- 1
Test to explain whether there is an effect
Test to tell you the size of the effect
Chi Square name
r =0.1, d =0.2 (1% of the total variance)
r = 0.3, d = 0.5 (Effect accounts for 9% of the total variance)
r = 0.5, d = 0.8 (the effect accounts for 25% of the variance)
Cohen's d, Pearson's r etc
A measure of association between two numerical variables, tracks whether deviations from the mean 'covary' in a systematic way,
Standardises the raw covariance detected into a comparable correlation coefficient
Correlation does not prove causality but merely
To measure the direction and the strength of the linear association between two numerical paired variables we use
Perfect positive linear relationship
No linear relationship
Perfect negative linear relationship
Coefficient of determination - tells the percentage of the variation in the response variable that is explained by the model and the explanatory variable
R (squared) - Around 0.1
R (squared) - Around 0.3
R (squared) - Around 0.5
R(squared) result interprets to 96% of the variability in he accuracy of spelling is explained by the numer of books read
Measuring relationships
Problem: Covariance depends on the scales of measurement. When two variables are measured on different units eg Age and Memory. Solution: _________ : converting covariance into a s
Assumptions that need to be made for the data to be __________________ 1. Normally distributed data, Homogeneity of variance, interval data, independence

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