Frequency claim:merely gives a percentage on the research. It describesa particular rate or degree of a single variable. For example, “1/25 U.Steens attempts suicide”. “44% (proportion) of Americans struggle to stayhappy”. These headlines claim how frequent or common something is. The differences from other two claims are: they only focus on onevariablesuch as depression, happiness, and rate of exercise. They arealways measuredbut manipulated. Association claim (types of associations): argues that one level of avariableis likely to be associated with a particular level of anothervariable.(Correlate: one variable changes, the other variable tends tochange.) Also, association claim must involve at least 2 variables (two ormore), and those are measuredbut manipulated. For example, “Shypeople are better at reading facial expression.” The 2 variables are theamount of shyness and the ability to read facial expressions: Being moreshy is associated with a greater ability to read facial expressions (andtherefore being less shy goes with a lower ability to read facialexpressions.) Association claim contains 3 different types of association: Positive association: For example, “Shy people are better at readingfacial expressions.” The more shy people are, the better they can readfacial expressions. The type of association in this example, in which highgoes with high, low goes with low, is called Positive association. Negative Association: For example, “Heavy cell phone use tied to poorsperm quality.” This could be restated as “Men who spend more timeusing cell phone have lower quality of sperm.” The type of association inthis example, in which high goes with low, low goes with high, is callednegative association. Zero Association: For example, “Screen time not linked to physicalactivity in kids.” There is no association between the variables. In ascatterplot of this association, the points have no slop. People can make predictions based on associations.For example,when we see 2 people, one is shy and the other one is outgoing, we canpredict that the shy person can read facial expressions better than theoutgoing person. Causal claims概概概概概: argues that one of those variables is responsible forchanging the other. Causal claim can be positive, negative association.Also contains tentative language(could, may, seem, sometimes…).Advices(if you do X, then Y will get better) are also causal claims. Not all claims are based on research. It can be based on someone’sexperience, an ironic story, or an expert’s advice.
3.How are causal claims special, compared with the other two claims?Because theyspecify the relationship with the two variables and imply you cannot have one without theother.4.What are the three criteria that causal claims must satisfy?First it must establishthat the two variables are correlated; the relationship cannot be zero. Second, it mustshow that the casual variable came first and the outcome variable came later. Third, itmust establish that no other explanations exist for the relationship.INTERROGATING THE THREE CLAIMS USING THE FOUR BIGVALIDITIES; PRIORITIZING VALIDITIES1.Which of the four big validities should you apply to a frequency claim? Anassociation claim? A causal claim?See chart.2.What question(s) would you ask to interrogate a study’s construct validity?3.In your own words, describe at least three things that statistical validity addresses.4.Define external validity, using the term generalize in your definition.5.What is internal validity? Why is it mostly relevant for causal claims?6.Why don’t researchers usually aim to achieve all four of the big validities at once?
PSYCH 1000
WEEK 3
CLAIMS THAT HAVE EVIDENCE
THREE CLAIMS
A claim is the argument someone is trying to make.
-Frequency Claims - describe a rate or degree of a single variable
-Association Claims (types of associations) - 2 variables- claim the 2 are associated with
each other. Even if there is no association between the 2 it still says it has an association
-Causal Claims - state that 1 variable causes change in the other. One is the cause and the
other variable is affected.
Scientists are very careful about the words they use when making a claim.
CASUAL CLAIMS - casual claim verbs tend to be used where an association claim verb
should be used to make the article more powerful and direct.
PRACTICE IDENTIFYING CLAIMS
a.Indicate if the claim is frequency, association, or cause.
b.For each claim, identify the variable(s). One variable - frequency
c.For each variable, is it manipulated or measured?Imagine what would a study look like, would it
be measured and only observe the variable or would it be manipulated with a variable being
controlled by the researcher
d.State each variable at the conceptual level. Variable - what do they want to measure and what
they have actually measured
e.State each variable in terms of its operational definition: How might it have been operationalized?
ASSOCIATION CLAIM
An association claim argues that one level of a variable is likely to be associated with a
particular level of another variable.
-Association claims must involve at least two variables, and the variables are measured not
manipulated (one feature to help distinguish from a causal claim)
-Both positive and negative associations can help us make predictions, if we know the level
of the variable we can more accurately guess or predict the level of other variables.
-In contrast, a Zero association cannot predictions as the variables aren’t correlated
LANGUAGE USED
●Is linked to
●Is at a higher risk for
●Is associated with
●Is correlated with
●Prefers
●Are more/less likely to
●May predict
●Is tied to
●Goes with