Statistische Modellen voor Communicatieonderzoek (77522101AY)
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Samenvatting - Statistische Modellen voor Communicatieonderzoek
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Course
Statistische Modellen voor Communicatieonderzoek (77522101AY)
Institution
Universiteit Van Amsterdam (UvA)
Samenvatting van hoorcolleges, literatuur en microlecture van het vak SMCO. Bevat ook stappenplannen voor de verschillende toetsen hoe je die in SPSS moet uitvoeren. Grootste gedeelte is Engels, een aantal aantekeningen zijn Nederlands.
Statistische Modellen voor Communicatieonderzoek (77522101AY)
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STATISTISCHE MODELLEN VOOR
COMMUNICATIEONDERZOEK
week 1
micro lecture 2.1
★ empirical cycle → 5 phases
○ hypothetic-deductive approach
1. observation: sparks idea for hypothesis pattern, unexpected event,
interesting relation we want to explain, source not important
(personal, shared, imagined, previous research) → observing relation
in one or more instances
2. induction: general rule → with inductive reasoning relation in
specific instances is transformed into general rule or hypothesis
○ inductive inference: relations holds in specific cases ⇒
relations holds in all cases
3. deduction: relation should hold in new instances,
expectation/prediction is deduced about new observations →
hypothesis is transformed with deductive reasoning and
specification of research setup into prediction about new
observations
○ determine research setup
○ define concepts, measurement instruments, procedures,
sample
4. testing: data collection, compare data to prediction, statistical
processing → new data collection and - with aid of statistics -
compared to predictions
○ descriptive: summarize
○ inferential: decide
5. evaluation: interpret results in terms of hypothesis → hypothesis
supported, adjusted or rejected
○ prediction confirmed ⇒ hypothesis provisionally supported,
n ot pr oven
○ prediction disconfirmed ⇒ hypothesis not automatically
rejected → repeat with better research set up/adjust
hypothesis/reject hypothesis
,literatuur hoofdstuk 1
★ expected value = mean of sampling distribution
1. draw thousands of samples
2. calculate the mean
3. and you have the true population value
★ musts:
1. random samples
2. unbiased estimator
3. continuous versus discrete: probability density vs. probabilities
4. impractical
★ sample statistic: a number describing a characteristic of a sample
★ sampling space: all possible sample statistic values
★ sampling distribution: all possible sample statistic values and their
probabilities or probability densities
★ probability density: a means of getting the probability that a continuous
random variable (like a sample statistic) falls within a particular range
★ random variable: a variable with values that depend on chance
★ expected value/expectation: the mean of a probability distribution, such as a
sampling distribution
★ unbiased estimator: a sample statistic for which the expected value equals the
population value
★ statistical inference = about estimation and null hypothesis testing
★ simulation: means that we let a computer draw many random samples from a
population
★ inferential statistics: offers techniques for making statements about a larger
set of observations from data collected for a smaller set of observations
○ population = the large set of observations about which we want to make
a statement
○ sample = the smaller set
○ we want to generalize a statement about the sample to a statement
about the population from which the sample was drawn
★ the sample statistic is called a random variable; it is a variable because
different sample can have different scores; the value of a variable may vary
from sample to sample; it is a random variable because the score depends on
chance, namely the chance that a particular sample is drawn.
★ probability distribution of the sample statistic: if we change the frequencies in
the sampling distributions into proportions.
★ discrete probability distribution ⇒ only a limited number of outcomes are possible
, ★ expected values = the average of the sampling distribution of a random
variable (also called: the expectation of a probability distribution)
★ unbiased estimator ⇒ if the expected value is equal to the population statistic
○ we usually refer to the population statistic as a parameter
★ downward biased ⇒ underestimate the number in the population
★ a sample is representative of a population (in the strict sense) if variables in
the sample are distributed in the same way as in the population
○ in principle representative/representative in the statistical sense
(because it is likely to differ from the real population)
★ continuous variable: we can always think of a new value in between two values
★ probability density function: if there is a label to the vertical axis of a
continuous probability distribution, it is ‘probability density’ instead of
‘probability’. a probability density function can give us the probability of
values between two thresholds.
○ left-hand probability: the probability of values up to (and including) a
threshold value → used to calculate p values
○ right-hand probability: the probability of values above (and including ) a
threshold value → used to calculate p values
★ we can use probability distributions in two ways:
○ we can use them to say how likely or unlikely we are to draw a sample
with the sample statistic value in a particular range
○ we can use them to find the threshold values that separate the top ten
percent or the bottom five per cent in a distribution
hoorcollege 1
★ statistical literacy
○ knowledge (basic understanding of concepts)
■ identify
■ describe
○ skills (ability to work with statistical tools)
■ translate
■ interpret
■ read
■ compute
★ statistical reasoning
○ understanding
■ explain why
■ explain how
★ statistical thinking
, ○ apply
■ what methods to use in a specific situation
○ critique
■ comment and reflect on work of others
○ evaluate
■ assigning value to work
○ generalize
■ what does variation mean in the large scheme of life
★ verschil binomiale verdeling vs. normale verdeling:
○ bij een normale verdeling kunnen alle waarden er zijn (bijv. ook 2,99)
○ bij een binomiale verdeling niet
★ het gemiddelde van de steekproevenverdeling is hetzelfde als de populatie
proportie
★ verwachte waarde altijd hetzelfde als het gemiddelde in de
steekproevenverdeling en het gemiddelde in de populatie
★ parameter is het gemiddelde in de populatie
★ sampling distribution ⇒ cases zijn daar de steekproeven
★ in strikte zin = identiek
★ het gemiddelde van de steekproef verdeling is een zuivere schatter (unbiased
estimator)
★ een discrete variabele heeft vaste uitkomsten, dus je gebruikt probabilities ipv
probability density
○ probability density bij continue variabele
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