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Summary Exam notes for Methods of Communication Research and Statistics (MCRS) second exam €14,46
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Summary Exam notes for Methods of Communication Research and Statistics (MCRS) second exam

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Providing all relevant and comprehensive content for Methods of Communication Research and Statistics second exam (week 8-week 12). Content covers the book, lectures, and all other relevant material.

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  • 4 november 2024
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MCRS Exam Part 2

Week 8
● Survey study design - capture public opinion at a point in time; accurately
describes a population
-several surveys in a row can capture an opinion over time
-CAN’T assess CAUSAL relationships!
-external validity increases population validity
-must be carefully worded and pre-tested; can be fast and cost-effective
Types of surveys:
1. Cross-sectional- things measured once at one point in time, less expensive, not good
for determining causality (can't determine cause and effect)
2. Longitudinal-asking questions over time, more expensive, better for determining
causality
a) Trend study- examines changes in population over time, each study collects data
from different individuals from the SAME population; we don’t know why has
change occurred because we are not collecting data from the same individuals;
examining trends in public opinion
b) Cohort studies- examines changes in the cohort; cohorts are groups of people
with shared characteristics; data is collected from DIFFERENT individuals from
the same cohort; provides more detail (people that graduated at the same time…)
c) Panel studies- examines changes in individuals; collects data from the same
individuals from the same population,
d) Cross-lagged panel survey- best survey research design for assessing causality,
but it is only an indicator; reciprocal relationships; measures I.V and D.V at
several time points (uses same questions at each time point); meets 2 criteria for
causality (time order, co-variance) combination of cross-sectional and
longitudinal
-to what extent I.V at one point predicts D.V at the second point
-using panel data




x1 study technique ,y1 grade one time point x2 study
tecnique ,y2 grade second time point

, Population- contains every one of the units the researcher has elected to study; every
person in a group we are studying; Element- single entity of a population
Sample- a selected segment of a population presumed to represent that population
Census- study of entire population
Parameters- describe a population; Statistics- describe a sample
Sampling frame- list from which a sample is drawn
Stratum- a subset of elements from a population that share a characteristic
Selection threat- concerns the generalizations of findings to other reasons
-with appropriate sampling, we can generalize with statistical confidence, from a sample
to a wider population
-to determine causality we need true experiment

Sampling methods:
1. Probability Sampling:
a) (Simple) Random sample- everyone has an equal chance of being selected; the
computer generates from an entire population
b) Cluster sampling- random sampling from a larger population
c) Systematic sampling- random number generator (e.g. every 6th person)
d) Stratified random sampling- performing random sample from specific groups
(strata- groups sharing characteristics)
2. Non-probability sampling:
a) Convenience sampling- studying elements that are easily accessible
b) Purposive sampling- participants are chosen based on judgments of a researcher
c) Snowball sampling (type of convenience sampling)-when recruiting small group
of participants that provide us with contacts of possible new participants (people
share characteristics)
d) Quota sampling- elements for research are distinguished due to characteristics;
participants for each category are recruited using convenience sampling; purpose
to reach a quota (needed number)

, Social desirability bias
-indirect questioning (third party “what would your friend answer”)
-when you are afraid that your answers do not align with the norm
- solution: showing its okay to answer in a way that is not socially desirable
Item non-response bias: respondents don’t understand questions, don’t want to answer
questions; consequences-low external validity(reason not to answer questions due to
personal characteristics); solution: pre-test question, change question, change answer
format
Pre-testing in survey design: wording, aesthetic, logical wording, time to complete the
survey; after respondents fill in questions, ask for evaluation; cognitive answering (out
loud); feedback

Types of Validity: Did we measure what was intended in the first place (systematic and
random error)
1. External validity: the extent to which the results of the study can be generalized to a
context other than the study itself (is the causal relationship applicable to other
situations); to what extent is your research telling you something about the population
you are interested in
a) Population validity- results can be generalized to the population, and
characteristics of the groups shouldn’t be similar (if they are, it affects external
validity)
b) Ecological validity- the extent to which the results can be generalized to
real-world conditions and circumstances;
2. Internal validity: the certainty with which you can test casual relationships in the
research (only experiments test causality; random selection and assignment); is the effect we
found caused by our independent variable or something else

What affects external validity:
-is low when there are similar characteristics in groups
-is low when item non-response bias
-lower in (true) experimental design
-low ecological validity in lab setting
-population validity is low when we choose a specific group of people
Threaths:
a) History threat: observed effect doesnt generalize to other time periods
b) Setting threat: observed effect only holds in a specific setting
-association with artificiality: Pretesting threat: observed effect is found only when a
pretest is performed; Reactivity: when participants or researcher react to the fact that they
are participating in a research study

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