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CRM 1301 Lecture Notes - Lecture 2: Spectral Evidence, Seed, Spurious Relationship

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rubykoala20
19 May 2016
School
University of Ottawa
Department
Criminology
Course
CRM 1301
Professor
Carolyn Gordon

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Document Summary

Systematically measuring two or more variables and assessing the relationship between them. The underlying purpose is how much one can be predicted by the other. Positive correlation is when both variables are either increasing or decreasing. A negative correlation means that the variables are not going in the same directions so like when one increases and the other decreases. A apparent but false relationship between 2 or more variable that is caused by some other variable. In other words there is another element that could be the explanation of a certain thing. More ice cream is being sold since the murders started. The real explanation would probably be that it"s summer and its hot not because when murder happens people want ice cream. Time: some things were illegal and now are not, some things were legal and now they"re not. Testability: reliability ex freud"s theory of the id ego and super ego, you can"t test it.

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Related Questions

Identify the true statements about the correlation coefficient, r.

a.) The correlation coefficient is not affected by outliers.

b.)The value of r ranges from negative one to positive one.

c.) When the data points in a scatter plot fall closely around a straight line that is either increasing or decreasing, the correlation between the two variables is strong.

d.) A correlation coefficient of zero means that no relationship exists between the two variables.

e.) If two variables are positively correlated, when one variable increases, the other variable decreases.

f.) The sign of r describes the direction of the association between two variables.

1. Evidence that may come from narrative accounts of historical or contemporary events is called ____________ evidence.

a. Quantifiable evidence

b. Quantitative evidence

c. Qualitative evidence

d. Quasi evidence

2. Why is correlation not always the explanation of a cause-and-effect argument?

a. Cause-and-effect argument doesn't happen in comparative politics.

b. Only positive correlation is explanatory.

c. Correlative links do not necessarily imply causation.

d. Negative correlation necessarily negates association.

3. Endogeneity refers to:

a. A circumstance when a variable creates its own correlation

b. A circumstance when a theory can be tested with evidence

c. A circumstance when two variables exhibit mutual or reciprocal effects

d. A circumstance when two variables exhibit correlation without a causal relationship

Psychology of adulthood and aging;

Here is a question related to adulthood/aging that you think science can’t answer:

Does age determine adulthood?

Then you will try to demonstrate how science could answer a related question. (Change the question to): When should a person be considered an adult?

Objectives:

Students should be able to speculate as to why two variables might be correlated, identify what a correlation between variables is not telling us, and anticipate what kind of research studies could further clarify a correlation between variables.

Students should be able to define operationalization, hypothesis, and be able to describe the scientific method by which psychologists study adulthood and aging.

a-First psychological variable

b-Give it a label

c-Briefly describe this variable.

Describe how this variable can be operationalized (that is, describe exactly how you determine which category or which value of the variable applies to a particular person or situation).

Second variable that you believe is related to your first variable.

a-Give it a label.

b-Briefly describe this variable.

c-Describe how this variable can be operationalized.

Once the variables are defined, the next step is to predict the relationship between these two variables by writing a hypothesis. A hypothesis is a prediction about the relationship between two or more variables. For now, assume that there is a causal relationship between the two variables even if it would be impossible or unethical to test a causal hypothesis. This means that changing a person’s score on one of the variables (the independent variable) would cause the person’s score on the other variable (the dependent variable) to change. For example, you might say, “Increasing the amount of violent television that a person watches will cause that person to exhibit more aggressive behaviors.”