Lie is to be one-faced By Prof Dr Sohail Ansari & Variables 8
After
a few years of marriage a man can look right at a woman without seeing her and
a woman can see right through a man without looking at him.
Craftsman
of destruction
A journalist cannot be the true craftsman of destruction if he
does not exercise the art of lying with dexterity. Truth will, anyhow, put on
its shoes, and sooner or later will catch up with a lie. A journalist makes
sure that truth will never, not because it is slow, but because it fails to find a lie as
lie has portions of truth to justify its portions of falsehood.
A journalist makes sure that lie is
indistinguishable from truth. Lie, hence, is nowhere to be seen.
True face of
two-faced individual is known when the real one is discovered. A true lie can
never be known as it has no other face.
A journalist tells truth as well as lies; but
makes sure that latter overshadows former so subtly that people knowing what
truth is, cannot recognize a lie and takes it a part of truth.
“There is nothing true anywhere, The true is nowhere to be seen; If you say you see the true, This seeing is not the true one.” » Abraham Lincoln Kickvick.com
“If you’re going to be two-faced at least make one of them
pretty.” » Marilyn Monroe
“Just
because something isn't a lie does not mean that it isn't deceptive. A liar knows
that he is a liar, but one who speaks mere portions of truth in order to
deceive is a craftsman of destruction.” ― Criss Jami
"Don’t lie, but don’t tell the whole truth." Baltasar Gracián:
"The art of living is the art of knowing how to believe lies. The fearful thing about it is that not knowing what truth may be, we can still recognize lies." Cesare Pavese
After a few years of marriage a man can look right at a woman without
seeing her and a woman can see right through a man without looking at him.
I don't mean inattentional
blindness, where something is right in front of you
but because of your attention being elsewhere you don't notice it at all and
might deny it was even there if asked later.
I mean, when you idly, passively see
something, and you do minimally
notice it (if someone later asks "did you see X" you'll say yes), but
it doesn't register, you don't
process it properly or think about what you're seeing, you process it on
autopilot.
Eye Direction. There are a number of studies
that talk about the direction of eyes during lies. Typically when people look up or to
the right they are
lying or tapping into their imagination. When they look up to the left they are remembering or recalling
something, tapping into the memory part of the brain.)
Variation
Variables
Each person/thing we collect data on is called an OBSERVATION (in
our work these are usually people/subjects. Currently, the term participant
rather than subject is used when describing the people from
whom we collect data).
OBSERVATIONS (participants) possess a variety
of CHARACTERISTICS.
If a CHARACTERISTIC of an OBSERVATION (participant) is the same
for every member of the group (doesn’t vary) it is called a CONSTANT.
If a CHARACTERISTIC of an OBSERVATION (participant) differs for
group members it is called a VARIABLE. In research we don’t
get excited about CONSTANTS (since everyone is the same on that
characteristic); we’re more interested in VARIABLES. Variables can be
classified as QUANTITATIVE or QUALITATIVE (also known as CATEGORICAL).
QUANTITATIVE variables are ones that exist along a continuum
that runs from low to high. Ordinal,
interval, and ratio variables are quantitative. QUANTITATIVE variables
are sometimes called CONTINUOUS VARIABLES because they have a
variety (continuum) of characteristics.
Height
in inches and scores on a test would be examples of quantitative variables.
QUALITATIVE variables do not express differences in
amount, only differences.
They are sometimes referred to as CATEGORICAL variables because
they classify by categories.
Nominal variables such
as gender, religion, or eye color are CATEGORICAL variables. Generally
speaking, categorical variables
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Categorical
variables are groups…such as gender or type of
degree sought.
Quantitative
variables are numbers that have a range…like weight in pounds or baskets made during a ball
game. When we analyze data we do turn the categorical variables into numbers but only
for identification purposes…e.g. 1 = male and 2 =
female. Just because 2 = female does not mean that females are
better than males who are only 1. With quantitative data having a
higher number means you have more of something. So higher values have
meaning.
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A special case of a CATEGORICAL
variable is a DICHOTOMOUS VARIABLE. DICHOTOMOUS variables have
only two CHARACTERISTICS (male or female). When naming QUALITATIVE variables,
it is important to name the category rather than the levels (i.e., gender is
the variable name, not male and female).
Variables
have different purposes or roles…
Independent (Experimental, Manipulated, Treatment, Grouping)
Variable-
That factor which
is measured, manipulated, or selected by the experimenter to determine its
relationship to an observed phenomenon. “In a research study, independent variables are antecedent conditions that are presumed to affect a dependent
variable. They are either
manipulated by the
researcher or are observed by the researcher so that their values can be
related to that of the dependent
variable.
For
example, in a research study on the relationship between mosquitoes and mosquito bites, the number of mosquitoes per acre of
ground would be an
independent
variable” (Jaeger, 1990, p. 373)
While the independent variable is often manipulated by the
researcher, it can also be a classification where subjects are assigned to
groups. In a study where one variable causes the other, the independent
variable is the cause. In a study where groups are being compared, the independent
variable is the group classification.
Dependent
(Outcome) Variable-
That factor which is observed and measured to determine the
effect of the independent variable, i.e., that factor that appears, disappears,
or varies as the experimenter introduces, removes, or varies the independent
variable.
“In a research study, the independent
variable defines a principal focus of research interest. It is the consequent
variable that is presumably affected by one or more independent
variables that are either manipulated by the researcher or observed by the
researcher and regarded as antecedent conditions that determine the value of
the dependent variable.
For example, in a study
of the relationship between mosquitoes and mosquito bites, the number of
mosquito bites per hour would be the independent variable” (Jaeger, 1990, p.
370). The dependent variable is the participant’s response.
The dependent variable is the outcome. In an experiment, it
may be what was caused or what changed as a result of the study. In a
comparison of groups, it is what they differ on.
Moderator
Variable- That factor which
is measured, manipulated, or selected by the experimenter to discover whether
it modifies the relationship of the independent variable to an observed
phenomenon. It is a special
type of independent variable.
The independent variable’s relationship with the dependent
variable may change under different conditions.
That condition is the moderator variable.
In a study of two methods of teaching reading, one of the
methods of teaching reading may work better with boys than girls. Method of
teaching reading is the independent variable and reading achievement is the dependent
variable. Gender is the moderator variable
because it moderates or changes the relationship between the independent
variable (teaching method) and the dependent variable (reading achievement).
Suppose we do a study of reading achievement where
we compare whole language with phonics, and we also include students’
social economic status (SES) as a variable. The
students are randomly assigned to either whole language instruction or phonics
instruction. There are students of high and low SES in each group.
Let’s assume that we found that whole language instruction
worked better than phonics instruction with the high SES students, but phonics
instruction worked better than whole language instruction with the low SES
students. Later you will learn in statistics that this is an interaction
effect. In this study, language instruction was the independent variable (with
two levels: phonics and whole language). SES was the moderator variable
(with two levels: high and low). Reading achievement was the
dependent variable (measured on a continuous scale so there aren’t levels).
With a moderator variable, we find the type of
instruction did make a difference, but it worked differently for
the two groups on the moderator variable. We select this moderator variable
because we think it is a variable that will moderate the effect of the
independent on the dependent. We make this decision before we start the study.
If the moderator had not been in the study above, we would have
said that there was no difference in reading achievement between the two types
of reading instruction. This would have happened because the average of the
high and low scores of each SES group within a reading instruction group would
cancel each other an produce what appears to be average reading achievement in
each instruction group (i.e., Phonics: Low—6 and High—2; Whole Language: Low—2
and High—6; Phonics has an average of 4 and Whole Language has an average of 4. If we
just look at the averages (without regard to the moderator), it appears that
the instruction types produced similar results).
Extraneous
Variable- Those factors which cannot be
controlled.
Extraneous variables are independent variables that have not been controlled. They may or may not influence the results. One way to control an extraneous variable which might influence the results is to make it a constant (keep everyone in the study alike on that characteristic). If SES were thought to influence achievement, then restricting the study to one SES level would eliminate SES as an extraneous variable.
Extraneous variables are independent variables that have not been controlled. They may or may not influence the results. One way to control an extraneous variable which might influence the results is to make it a constant (keep everyone in the study alike on that characteristic). If SES were thought to influence achievement, then restricting the study to one SES level would eliminate SES as an extraneous variable.
Here are some examples similar to your homework:
Null Hypothesis:
Students who receive
pizza coupons as a reward do not read more books than students who do not
receive pizza coupon rewards.
Independent Variable: Reward Status
Dependent Variable: Number of Books Read
High achieving
students do not perform better than low achieving student when writing stories
regardless of whether they use paper and pencil or a word processor.
Independent Variable: Instrument Used for Writing
Moderator Variable: Ability Level of the Students
Dependent Variable: Quality of Stories
Written
When we are comparing two groups, the groups
are the independent variable. When we are testing whether something
influences something else, the influence (cause) is the independent
variable.
The independent variable is also the one we manipulate.
For example,
Consider the hypothesis
“Teachers given
higher pay will have more positive attitudes toward children than teachers
given lower pay.”
One approach is to ask ourselves “Are there two or more
groups being compared?” The answer is “Yes.” “What are the groups?”
Teachers who are given higher pay and teachers who are given
lower pay. Therefore, the independent variable is teacher pay (it has
two levels– high pay and low pay). The dependent
variable (what the groups differ on) is attitude
towards school.
We could also approach this another way.
“Is something causing something else?”
The answer is “Yes.”
“What is causing what?”
Teacher pay is causing attitude towards school.
Therefore, teacher pay is the independent variable (cause) and
attitude towards school is the dependent variable (outcome).
Research Variables
Independent, Dependent and
Extraneous
Saul
McLeod published 2008
A variable is anything that can vary, i.e. changed
or be changed, such as memory, attention, time
taken to perform a task, etc.
Variable are given a special name that
only applies to experimental
investigations. One is called the dependent variable and the other the
independent variable.
In
an experiment, the researcher is looking for the possible effect on
the dependent variable that might be caused by changing the
independent variable.
• Independent variable (IV):
Variable the experimenter manipulates (i.e. changes) – assumed to have a direct
effect on the dependent variable.
• Dependent variable (DV):
Variable the experimenter measures, after making changes to the IV that are
assumed to affect the DV.
For
example, we might change the type of information (e.g. organized or random)
given to participants to see what effect this might have on the amount of
information remembered.
In
this particular example the type of information is the independent
variable (because it changes) and the amount of information remembered
is the dependent variable (because this is being
measured).
Operationalising
Variables
It
is very important in research to clearly define what you mean by both
your IV and DV.
Operational variables (or
operationalizing definitions) refer to how you will define and measure a
specific variable as it is used in your study.
For
example, if we are concerned with the effect of media violence on aggression,
then we need to be very clear what we mean by the different terms.
In this case, we must state what we mean by the terms “media
violence" and “aggression" as we will study them.
Therefore,
you could state that “media violence" is operationally defined (in
your experiment) as ‘exposure to a 15 minute film showing scenes of physical
assault’; “aggression" is operationally defined as ‘levels
of electrical shocks administered to a second ‘participant’ in
another room’.
In
another example,
The hypothesis:
“Young
participants will have significantly better memories than older participants"
is not operationalized.
How
do we define "young", “old" or
"memory"?
"Participants
aged between 16 - 30 will recall significantly
more nouns from a list if twenty than participants aged between
55 - 70" is operationalized.
The
key point here is
that we have made it absolutely clear what we mean by the terms as this then it would be very difficult (if not
impossible) to compare the findings of different studies into the same
behavior.
Operationalization
has the great advantage that it generally provides a clear
and objective definition of even complex variables. It also makes it easier for other
researchers to replicate a study and check for reliability.
Extraneous Variables
When
we conduct experiments there are other variables that can affect our results,
if we do not control them. The researcher wants to make
sure that it is the manipulation of the independent variable that
has changed the changes in the dependent variable.
Hence,
all the other variables that could affect the DV to change must be controlled.
These other variables are called extraneous or confounding
variables.
Extraneous variables – These
are all variables, which are not the independent variable, but could affect the
results (e.g. dependent variable) of the experiment.
Extraneous
variables should be controlled were possible. They might be important
enough to provide alternative explanations for the effects.
There are four types of extraneous
variables:
1. Situational Variables
These
are aspects of the environment that might affect the participant’s behavior,
e.g. noise, temperature, lighting conditions, etc. Situational
variables should be controlled so
they are the same for all participants.
Standardized procedures are used to ensure
that conditions are the same for all participants. This includes the use of
standardized instructions
This
refers to the ways in which each participant varies from
the other, and how this could affect the results e.g. mood,
intelligence, anxiety, nerves, concentration etc.
For
example,
If
a participant that has performed a memory test was tired,
dyslexic or had poor eyesight, this could effect their performance and
the results of the experiment. The experimental
design chosen can have an affect on participant variables.
Situational
variables also include order effects that can be controlled using counterbalancing,
such as giving half the participants condition 'A' first, while the
other half get condition 'B' first. This prevents improvement due to practice,
or poorer performance due to boredom.
Participant
variables can be controlled using random allocation to the conditions of the
independent variable.
3. Experimenter / Investigator Effects
The
experimenter unconsciously conveys to participants how they
should behave - this is called experimenter bias.
The
experiment might do this by giving unintentional clues to the
participants about what the experiment is about and how they expect
them to behave. This affects the participants’ behavior.
The
experimenter is often totally unaware of the influence which s/he is exerting
and the cues may be very subtle but they may have an influence nevertheless.
Also,
the personal attributes (e.g. age, gender, accent,
manner etc.) of the experiment can affect the behavior of the participants.
4. Demand Characteristics
These
are all the clues in an experiment which convey to the participant the purpose
of the research.
Participants
will be affected by: (i) their surroundings; (ii) the researcher’s characteristics;
(iii) the researcher’s behavior (e.g. non-verbal communication), and
(iv) their interpretation of what is going on in the situation.
Experimenters
should attempt to minimize these factors by keeping the environment as
natural as possible, carefully following standardized procedures. Finally,
perhaps different experimenters should be used to see if they obtain similar
results.
Suppose
we wanted to measure the effects of Alcohol (IV) on driving ability
(DV) we would have to try to ensure that extraneous variables did
not affect the results.
These
variables could include:
•
Familiarity with the car: Some people may drive better because they have driven
this make of car before.
•
Familiarity with the test: Some people may do better than others because they
know what to expect on the test.
•
Used to drinking. The effects of alcohol on some people may be less than on
others because they are used to drinking.
• Full stomach. The effect of
alcohol on some subjects may be less than on others because they have just had
a big meal.
If
these extraneous variables are not controlled they may become
confounding variables, because they could go on to affect the results of the
experiment.
Extraneous and confounding variables.
What happens when something other than your
independent variable is influencing the outcome of your study? In this lesson,
we'll look at two types of variables that can affect an experiment: extraneous
and confounding variables.
Internal Validity
Josh is in love. He's been with
his girlfriend a while now and wants to propose. But he doesn't know how he
should do it. Should he propose in a crowd? When they're alone? At the place
where they went for their first date? After he whisks her off to Paris or the
Bahamas?
Josh is a psychologist and does
research for a living, so he decides to do a study on marriage proposals and
figure out which one women like best. That's how he'll decide how to propose.
He gathers a bunch of women, shows them videos of marriage proposals, and then
measures their reactions: whether they cry or if their heart races or if they
just watch it and go, 'Eh.'
In research, internal
validity is when a researcher can say that only the independent variable caused changes in the dependent variable.
For example, in Josh's study, the
videos are the independent variable and the women's reactions are the dependent
variable. If Josh changes which videos he shows the women, he sees different
reactions. If his internal validity is high, he can say that the difference
in videos caused the changes in the reactions.
If most women who watch video A
say, 'Aw, how sweet!' and most women watching video B say, 'Well, that's an
epic fail,' then Josh wants to know for sure that it's actually the video
that's causing the reactions, not something else. Let's look closer at
variables that might affect the dependent variable besides the independent
variable: extraneous and confounding.
Extraneous Variables
Okay. So, let's imagine that Josh
has set up his experiment. Each subject is brought into a little room and is
shown two of six different videos. Josh measures their reaction to each video
and then their reaction overall.
Josh expects that he will see the
women react more positively to the videos they believe are most romantic. Not only that, but he believes
that if he shows a woman two proposals that most women believe are
really romantic, then she'll have a higher reaction level overall than
someone who is shown only one really romantic video and one that's, well, sort
of romantic. But what happens if the women who are shown two really romantic
proposal videos are put in a room that's much warmer than the other women? Or what if they are given a red rose before going into the room but
the other women aren't?
Both of these are examples
of extraneous variables, or variables present in the experiment that aren't
being studied. If all of the women who are shown the two most romantic proposals are tall and all the
other women are short, will that make a difference?
What about the examples we gave above of room temperature or the rose? How will
those affect the outcomes of the study?
The problem with extraneous
variables is that they might affect the dependent variable but they might not.
There's no way to tell until after the experiment is done.
Extraneous variables are usually
grouped into three categories:
1.
Physical or Situational Variables: These occur when the physical situation of subjects changes for certain groups, like
the fact that women shown the most romantic proposals are in a warmer room.
2.
Personal Variables: These are when one group has
personality or other traits that members of the other group don't. For example,
what if the women shown the most romantic video clips are also more romantic in nature than the other women?
3.
Researcher Variables: These are when the researcher,
himself, does something different for the various groups of the experiment. For
example, what if Josh was really nice to the women who saw the two romantic videos,
and he was very gruff with the other
groups?
Notice that extraneous variables are only important if they are
present for one group and not the other. If all of your subjects are exposed to the same extraneous
variable (like if Josh was nice to all the subjects), then it won't change your
dependent variable, and it's not
considered an extraneous variable.
Confounding Variables
So, extraneous variables affect
your dependent variable in some way, and what you really want is for the
independent variable to be the only one affecting your dependent variable. But
what if you have an extraneous variable that is related to your independent
variable, which in turn affects your dependent variable?
A confounding variable is an outside
influence that changes the effect of a dependent and independent variable. This
extraneous influence is used to influence the outcome of an experimental
design.
Confounding Variable
A confounding variable
is an outside influence that changes the effect of a dependent and independent
variable. This extraneous influence is used to influence the outcome of an
experimental design. Simply, a
confounding variable is an extra variable entered into the equation that was
not accounted for. Confounding variables
can ruin an experiment and produce
useless results. They suggest that
there are correlations when there really are not. In an experiment,
the independent variable generally has an effect on the dependent variable.
For example, if you are researching whether a lack of exercise has an
effect on weight gain, the lack of exercise is the independent variable and weight gain
is the dependent variable.
A confounding variable would be any other influence that has an effect on weight gain.
Amount of food
consumption is a confounding
variable, a placebo is a confounding variable, or weather could be a confounding variable. Each may change the effect of
the experiment design.
In order to reduce confounding variables, make sure all the
confounding variables are identified in the study. Make a list of
everything thought of, one by one, and consider whether those listed items
might influence the outcome of the study. Understanding the confounding
variables will result in more accurate results.
Examples
of Confounding Variable:
1. A mother's education
Suppose a study is
done to reveal whether bottle-feeding is related to an increase of diarrhea in
infants. It would appear logical that the bottle-fed infants are more prone to
diarrhea since water and bottles could easily get contaminated, or the milk could
go bad. However, the facts are that bottle-fed infants are less likely to get
diarrhea than breast-fed infants. Bottle feeding actually protects against
illness. The confounding
variable would be the extent
of the mother's education on the matter. If you take the mother's education
into account, you would learn that better educated mothers are more likely to
bottle-feed infants.
2. Weather
Another example is the correlation between murder rate and the
sale of ice-cream. As the murder rate raises so does the sale of ice-cream. One
suggestion for this could be that murderers cause people to buy ice-cream. This
is highly unlikely. A second suggestion is that purchasing
ice-cream causes people to commit murder, also highly unlikely. Then
there is a third variable which includes a confounding variable. It is
distinctly possible that the weather causes the correlation. While
the weather is icy cold, fewer people are out interacting with others and less likely to
purchase ice-cream. Conversely, when it is hot outside, there is more
social interaction and more ice-cream being purchased. In
this example, the weather is the variable that confounds the
relationship between ice-cream sales and murder.
3. Slanted wood
Another example is the relationship between the force
applied to a ball and the distance the ball travels. The natural
prediction would be that the ball given the most force would
travel furthest. However, if the confounding variable is a downward slanted
piece of wood to help propel the ball, the results would be dramatically
different. The slanted wood is the confounding variable that changes the
outcome of the experiment.
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