Quantitative and Qualitative Research Techniques in the Social Sciences (YSS20306)
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Clear and comprehensive summary YSS20306
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Course
Quantitative and Qualitative Research Techniques in the Social Sciences (YSS20306)
Institution
Wageningen University (WUR)
This summary summarizes the course Research Methods in the most comprehensive way possible. Both qualitative and quantitative parts are described. The material comes from the lectures, sometimes supplemented with some extra self-requested information.
Quantitative and Qualitative Research Techniques in the Social Sciences (YSS20306)
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Quantitative samenvatting
Background information
T-test
Consists of:
Name of test
Variable: y = what you want to measure (so, optimism about environment for example)
Parameter of interest: difference between two population means. Therefore, you have to
measure both means
Null hypotheses: difference in means = 0
Alternative hypotheses: difference in means > 0
Test statistic: t-value: difference between means / standard error of difference
P-value: significance of result
Confidence interval: how much % the difference in mean would be if study was conducted
over and over again
Measure of effect size: Cohen’s d:
o 0.2 is small
o 0.5 is medium
o 0.8 is large
(Inter)dependent techniques
Dependent techniques: measuring the effect of predictors on the outcome.
Interval: simple/multiple regression
o Interval: equally spaced units, without a zero point. Date of birth.
o Predictors
One: simple regression
Multiple: multiple regression
Categorical/nominal: (factorial) ANOVA / t-test
o Categorical/nominal = Values are categories, without ranking. Alive or dead, ill or
well, vaccinated or unvaccinated
o Predictors:
One: ANOVA/t-test
Multiple: Factorial ANOVA
,Simple regression
Predict outcome x by predictor variable y.
Model
Simple model: using method of least squares. Straight line that is as close as possible to all of the
points. For calculating a point, just look at the function of the model + the error.
Line is made by regression coefficient and intercept.
Variance: variability from model per point.
Overall statistics
Effect size: difference between predicted and observed scores.
R: multiple correlation coefficient.
R-squared: coefficient of determination: proportion of variance of outcome Y can be explained by the
model.
Degrees of freedom in simple regression: N-2 with n being the amount of observations
Statistical significance: H0: R=0, Ha: R> of < 0
Difference statistical significance and effect size:
Effect size is difference between observed and predicted scores
Statistical significance is examining if findings are true due to chance
Important terms
Sum of squares: mean score * observed score
Mean square: sum of squares/degrees of freedom
F-value: mean squares regression / mean squares residuals
Mean squares residuals = estimate of variance of error terms
Detailed statistics
Standardized and unstandardized coefficients
Standardized: obtained after running a regression model measured on standardized variables
Unstandardized: obtained after running a regression model on variables measured in their
original scales
Interdependent techniques: investigate interrelations. No distinction between outcome and
predictors
Interval: only necessary
o Predictors:
Two: correlation
More than 2: Exploratory factor analysis
Correlation
Covariance: measures to which extent deviations from mean of variable 1 go together with
deviations from mean of variable 2.
Formula: (sum of (product from deviations from means1&2) across all observations)/no.
observations -1
, Problems: Covariance depends on units of measurement, just like maximum and minimum
values
Solution: Pearson correlation : Covariance/product of standard deviations from 1 & 2. Gives a
correlation which:
Measures strength of linear relationship
Maximum value is 1 and -1
Standardizable
Measurement of linear relation in correlation
All points on straight positive line (close to one)/negative line (close to -1): strong positive/negative
correlation.
Not straight but close to each other > spread out but straight line
Statistical significance
T-test: testing if r = 0
Z-test: testing if r = rhypothesized
Core assumptions (later)
Independent
Normally distributed
Obtained by simple random sampling
Size of effect (Cohen)
0.1 = small
0.9 = medium
0.25 = large
Multiple regression
Difference with simple regression: with multiple, there is more-than-one predictor
Research question:
Does this and this and this have an effect on …
Or, : Which of these three factors has the highest influence?
Model:
Looking for the linear contributions of the predictors. Quite the same as simple, only with more
dimensions.
Equation (to find a point): intercept + contribution of predictor 1 + “””””predictor n + error term.
The method of least squares is used again to draw a line.
R (-squared)
R = Multiple correlation coefficient. Here, also the effect size.
R-squared = coefficient of determination = measures how difference in one variable can be
explained by difference in other one. Other name: VAF (variance accounted for)
Cohen’s values again.
Adjusted R-squared: Estimate of r-squared measured in population, instead of sample.
Always < than R squared
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