Samenvatting
Summary Begrippenlijst quantitative research methods
- Instelling
- Wageningen University (WUR)
Begrippenlijst van het boek van Andy field.
[Meer zien]Voorbeeld 3 van de 18 pagina's
Voorbeeld 3 van de 18 pagina's
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Voorbeeld van de inhoud
Glossary Andy Field, 4th edition:Chapter 1:
- Independent variable = Predictor variable: its value does not depend on any
other variables
- Dependent variable = Outcome variable: the value of this variable depends
on the cause (independent variable)
- Level of measurement: the relationship between what is being measured and
the number that represent what is being measured
- Categorical variable: made up of categories (humans, animals, plants)
-Binary variable: male, female, yes or no, etc.
-Nominal variable: unordered. no order/rack of numbers
- Ordinal variable: when categories are ordered (tells you nothing about the
difference between the values)
- Continuous Variable: one that gives us a score for each person and can take on
any value on the measurement scale that we are using;
- Interval variable: more useful than ordinal. Equal interval on the scale.
- Ratio variable: interval + meaningful zero point
- Measurement error: the discrepancy between the numbers we use to represent
the thing we’re measuring and the actual value of the thing we’re measuring (you
weigh 80 kg, but the scale says 83)
- Validity: whether an instrument actually measures what is sets out to measure
- Reliability: whether an instrument can be interpreted consistently across
different situations.
- Criterion validity: whether you can establish that an instrument measures what
it claims to measure through comparison to objective criteria.
- Concurrent validity: when data are recorded simultaneously using the new
instrument and existing criteria.
- Predictive validity: when data from the new instrument are used to predict
observations at a later point in time.
- Content validity:
- Test-retest reliability: if an instrument will produce similar scores at both
points in time
- Correlation & cross-correlation: observe what naturally goes on in the world
without directly interfering with it
- Experimental research: manipulate one variable to see its effect on another.
- Longitudinal research: measuring variables repeatedly at different time points
- Confounding variable:
- Manipulate the independent variable using different entities: between-groups,
between-subjects, independent design
- EManipulate the independent variable using the same entities: within-subject,
repeated-measure design
- Systematic variation: experiment doing something in one condition but not in
the other condition
- Unsystematic variation: random factors that exist between the experimental
conditions
- Practice effects: participants may perform differently in the second condition
because of familiarity with the experimental situation and/ or the measures being
used.
, - Boredom effects: participants may perform differently in the second condition
because they are tired or bored from having completed the first condition.
- Mode: score that occurs most frequently
- Median: the middle score (second quartile)
- Mean: the average score
- Interquartile range: cut of the top and bottom 25% and calculate the range of
the middle 50%
- Deviance = Xi - 𝑋 (score - mean)
𝑛
2
- Sum of squared errors (ss): ∑ (𝑥𝑖 − 𝑥)
𝑖=1
- Variance (s^2) = ss / (N-1) = Variance tells you the degree of spread in your
data set. The more spread the data, the larger the variance is in relation to the
mean.
- Standard deviation (s): √variance (p. 27) = The standard deviation is derived
from variance and tells you, on average, how far each value lies from the mean.
It’s the square root of variance.
- Both measures reflect variability in a distribution, but their units differ: Standard
deviation is expressed in the same units as the original values (e.g., meters).
Variance is expressed in much larger units (e.g., meters squared)
- z-scores: (X - 𝑋 ) / s = take each score - the mean of all scores / standard
deviation.
95% of z-scores: -1.96 & +1.96
99% of z-scores: -2.58 & +2.58
99.9% of z-scores: -3.29 & +3.29
A data set with large numbers can be converted into a data set with a mean of 0
and a standard deviation of 1.
Chapter 2:
- Parameters: are estimated from the data (rather than being measured) and are
(usually) constants believed to represent some fundamental truth about the
relations between variables in the model (mean, median)
- Degrees of freedom: the number of scores used to compute the total adjusted
for the fact that we’re trying to estimate the population value.
𝑛
2
∑𝑖=1(𝑋𝑖−𝑋)
- Mean squared errors: SS / df = 𝑁−1
- Method of least squared: (p. 51)
- Sampling variation: samples will vary because they contain different members
of the population
- Sample distribution: the frequency distribution of sample means from the same
population
- Standard error of the mean (SE): the standard deviation of sample means
- Central limit theorem: as samples get large (+30), the sampling distribution
has a normal distribution with a mean equal to the population mean and a
𝑠
standard deviation of σ𝑥 =
𝑁
- Confidence intervals: calculated boundaries within which we believe the
population will fall.
, - Alternative hypothesis (Experimental hypothesis): H1, prediction from your
theory that an effect will be present
- Null hypothesis: H0, states that an effect is absent
- If the p-value is lower than the value of type I alpha, then the test is significant
so reject H0
- Test statistic: compare how good the model/hypothesis is against how bad it is
𝑠𝑖𝑔𝑛𝑎𝑙 𝑣𝑎𝑟𝑖𝑎𝑛𝑐𝑒 𝑒𝑥𝑝𝑙𝑎𝑖𝑛𝑒𝑑 𝑏𝑦 𝑡ℎ𝑒 𝑚𝑜𝑑𝑒𝑙 𝑒𝑓𝑓𝑒𝑐𝑡
(the error) =
𝑛𝑜𝑖𝑠𝑒
= 𝑣𝑎𝑟𝑖𝑎𝑛𝑐𝑒 𝑛𝑜𝑡 𝑒𝑥𝑝𝑙𝑎𝑖𝑛𝑒𝑑 𝑏𝑦 𝑡ℎ𝑒 𝑚𝑜𝑑𝑒𝑙
= 𝑒𝑟𝑟𝑜𝑟
2
(e.g. t, F, 𝑋 )
- T-value: higher values indicate that a large difference exists between the
two-sample tests. The smaller the value, the more similarity exists between the
two sample sets. The greater the t, the more evidence against the 0 hypotheses
→ greater the evidence there is a significant difference.
- t-value (twee zijdige toets) → hoe groter, hoe verder weg van 0 → hoe
significanter
- One-tailed test: a statistical model that tests a directional hypothesis
- Two-tailed test: a statistical model that tests a non-directional hypothesis
- Type I error & Type II error:
- α − 𝑙𝑒𝑣𝑒𝑙: if we use the conventional criterion then the probability of this type I
error is .05 (5%) when there is no effect in the population
- β − 𝑙𝑒𝑣𝑒𝑙:the maximum acceptable probability of a type II error would be .2
(20%)
- Effect size: the size of an effect. It is simply an objective and usually
standardized measure of the magnitude of the observed effect.
- Cohen’s d: p.80
- Meta-analysis: use several studies to get a definite estimate of the effect in the
population. It involves computing effect sizes for a series of studies that
investigated the same research question, and taking an average of those effect
sizes.
Chapter 5:
- X = predictor
- b = parameter
- error = the model will not predict the outcome perfectly
- Outlier: score very different from the data set: can bias a parameter such as the
mean
- SUm of squared errors → standard deviation → standard error → confidence
intervals around the parameter estimate
1. Additivity and linearity: the outcome variable is, in reality linearly related to
any predictors, and that if you have several predictors then their combined effect
is best described by adding their effects together → your model needs to be right.
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