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The Meaning of Method: Numbers and their
(mis)use
Jonas Heide Smith (jonas@autofire.dk)
30-09-2008
Jonas Heide Smith

MA, Media Studies (KU, 2002)
PhD, ITU (2006)
Has taught online communication,
media theory etc.
jonas@autofire.dk
My research

Set out to test the core design
assumption that players want to
win
Tried hard to connect video games
and (economic) game theory
Had players play games that were
cooperative, semi-cooperative and
competitive
Analyzed their in-game behaviour
and their verbal communication
Guess the result...?
Method (what is it?)
Quantitative and qualitative
The Research Process
In a perfect world

An observation or curiosity leads
to a hypothesis (research
question)
The hypothesis is tested with
whichever method is best suited
for the job
The results, modestly interpreted,
enable us to either confirm or
reject the hypothesis
(these simplified minimum
requirements do not themselves
ensure quality research, which also
requires...?)
Objects of study

Game (textual analysis)
Player (observation, interviews,
surveys)
Culture (interviews, textual
analysis)
Ontology (philosophical enquiry)
Quantitative                                    Qualitative
Quantifies results to enable                   Never quantifies results
statistical analysis
                                             Says quot;a lot about a littlequot;
Says quot;a little about a lotquot;
                                             Is hermeneutic (requires
Is hermeneutic (requires                              interpretation)
interpretation)
                                      Often builds on the assumption
Often builds on the assumption      that the researcher is trustworthy
that the researcher should not be
                                      Often builds on the assumption
trusted
                                          that understanding a social
Often builds on the assumption                 phenomenon requires
that subjects are not trustworthy   understanding the perspectives of
(or unable to verbalise answers                   the agents involved
directly)
Often tests the relationship
between concrete variables
Experiments

Strength: Offers control over
variables
Weakness: Is itself a quot;variablequot; as
the environment is set apart from
real life (validity issues)
Generally best suited to measure
differences between conditions
(e.g. says little about quot;media usequot;
as suchquot;)
Variable X is varied and change in
Y is attributed to this variation
Multi-server MMOs may facilitate
naturalistic experiments
quot;The researcher
should not be trustedquot;

Living up to taxing requirements
eliminates possibilities for cheating
etc.
The researcher is not considered
quot;objectivequot;, but highly subjective
quot;Subjects are not
trustworthyquot;

Subjects may answer strategically
Introspection may be limited;
people may not know what they
do
The relationship
between variables

X --> Y (X yields or affects Y)
An independent variable affects a
dependent variable to a certain
degree
E.g. playing violent video games
increase player aggression
You test if a change in X affects Y
predictibly
Sampling

A sample is drawn (recruited) from
a population
The sample should be
representative (i.e. unbiased)
True random sampling is usually
the best technique
Self-selected samples (or
convenience samples) are often
used
All statistical testing assumes
perfect sampling
Statistical significance

quot;Significantquot; here means quot;actualquot;
Statistics can be either
descriptive or analytical
Descriptive statistics simply
describe the data (e.g. 58% of the
male subjects played racing
games as opposed to 42% of the
female subjects)
Analytical statistics build on a
statistical model
The model tells us what to expect
given that there were no significant
differences
Outcomes such as this was considered potentially significant if they were asymmetrical
around quot;Sometimesquot;. Any actual distribution (difference) was statistically significant if it
was less than 5% likely to have occurred by chance.
Statistical significance

A significance value (P) tells us
how likely are actual data is to
occur given that there are no
differences in the population.
Assume that there are actually no
differences; how likely is your
result?
A given result is statistically
significant if P is smaller than the
significance value (Alpha, typically
5%).
Correlation

Two variables (e.g. violent games
and aggression) may be correlated
Positive correlation: One variable
increases as the other increases
Negative correlation: One variable
increases as the other decreases
The correlation coefficient (r) varies
from -1 to +1. The closer to +1 or
-1 the stronger the relationship.
Correlation coefficients
Correlation

For instance: People's height and
weight correlate but r is not +1
r to the 2nd power gives the
percentage of variation for one
variable which is related to
variation in the other: r=0.5 means
that 25% (0.5 X 0.5 = 0.25) of the
variation is related
Correlation does not show
causation (aggressive players
might prefer aggressive games)
Summary

Questions...?

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Method

  • 1. The Meaning of Method: Numbers and their (mis)use Jonas Heide Smith (jonas@autofire.dk) 30-09-2008
  • 2. Jonas Heide Smith MA, Media Studies (KU, 2002) PhD, ITU (2006) Has taught online communication, media theory etc. jonas@autofire.dk
  • 3. My research Set out to test the core design assumption that players want to win Tried hard to connect video games and (economic) game theory Had players play games that were cooperative, semi-cooperative and competitive Analyzed their in-game behaviour and their verbal communication Guess the result...?
  • 7. In a perfect world An observation or curiosity leads to a hypothesis (research question) The hypothesis is tested with whichever method is best suited for the job The results, modestly interpreted, enable us to either confirm or reject the hypothesis (these simplified minimum requirements do not themselves ensure quality research, which also requires...?)
  • 8. Objects of study Game (textual analysis) Player (observation, interviews, surveys) Culture (interviews, textual analysis) Ontology (philosophical enquiry)
  • 9. Quantitative Qualitative Quantifies results to enable Never quantifies results statistical analysis Says quot;a lot about a littlequot; Says quot;a little about a lotquot; Is hermeneutic (requires Is hermeneutic (requires interpretation) interpretation) Often builds on the assumption Often builds on the assumption that the researcher is trustworthy that the researcher should not be Often builds on the assumption trusted that understanding a social Often builds on the assumption phenomenon requires that subjects are not trustworthy understanding the perspectives of (or unable to verbalise answers the agents involved directly) Often tests the relationship between concrete variables
  • 10. Experiments Strength: Offers control over variables Weakness: Is itself a quot;variablequot; as the environment is set apart from real life (validity issues) Generally best suited to measure differences between conditions (e.g. says little about quot;media usequot; as suchquot;) Variable X is varied and change in Y is attributed to this variation Multi-server MMOs may facilitate naturalistic experiments
  • 11. quot;The researcher should not be trustedquot; Living up to taxing requirements eliminates possibilities for cheating etc. The researcher is not considered quot;objectivequot;, but highly subjective
  • 12. quot;Subjects are not trustworthyquot; Subjects may answer strategically Introspection may be limited; people may not know what they do
  • 13. The relationship between variables X --> Y (X yields or affects Y) An independent variable affects a dependent variable to a certain degree E.g. playing violent video games increase player aggression You test if a change in X affects Y predictibly
  • 14. Sampling A sample is drawn (recruited) from a population The sample should be representative (i.e. unbiased) True random sampling is usually the best technique Self-selected samples (or convenience samples) are often used All statistical testing assumes perfect sampling
  • 15. Statistical significance quot;Significantquot; here means quot;actualquot; Statistics can be either descriptive or analytical Descriptive statistics simply describe the data (e.g. 58% of the male subjects played racing games as opposed to 42% of the female subjects) Analytical statistics build on a statistical model The model tells us what to expect given that there were no significant differences
  • 16. Outcomes such as this was considered potentially significant if they were asymmetrical around quot;Sometimesquot;. Any actual distribution (difference) was statistically significant if it was less than 5% likely to have occurred by chance.
  • 17. Statistical significance A significance value (P) tells us how likely are actual data is to occur given that there are no differences in the population. Assume that there are actually no differences; how likely is your result? A given result is statistically significant if P is smaller than the significance value (Alpha, typically 5%).
  • 18. Correlation Two variables (e.g. violent games and aggression) may be correlated Positive correlation: One variable increases as the other increases Negative correlation: One variable increases as the other decreases The correlation coefficient (r) varies from -1 to +1. The closer to +1 or -1 the stronger the relationship.
  • 20. Correlation For instance: People's height and weight correlate but r is not +1 r to the 2nd power gives the percentage of variation for one variable which is related to variation in the other: r=0.5 means that 25% (0.5 X 0.5 = 0.25) of the variation is related Correlation does not show causation (aggressive players might prefer aggressive games)