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- two variables (bi = two)
- either categorical or quantitative
- categorical = bar graph = difference between two group means
- quantitative = scatter plot = correlation coefficient r
- both variables have to be assessed to make the study valid

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In this chapter, it discusses the importance of validities (construct, internal, external, and statistical), measured variables, and statistics as a whole.

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probability sampling leads to higher external validity than non-probability sampling

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Variables: A manipulated variable is controlled by the researcher, while a measured variable is just measured.

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Variable x causes variable y
associations: positive, negative, zero
positive: both variables increase
negative: one variable increases while the other variable decreases
zero: the variables are not associated

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These methods of probability sampling are some of the most common

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- effect size (strength of r)
- precision (confidence interval of r)
- replication of the study
- outliers
- restriction of range
- is association curvilinear

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- does the sample represent some population
- random samples
- has to generalize population

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- how well is the causal claim supported?

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Categorical vs Quantitative variables Chp 5

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Categorical vs Quantitative variables Chp 5

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Categorical - A variable whose levels are categories (e.g., male and female).

Quantitative - Height and weight are quantitative because they are measured in numbers, such as 170 centimeters or 65 kilograms.

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- two variables are connected, but they do not cause each other
- supported by correlational studies
- if both variables are measured, that means the study is correlational

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This chapter identifies the three main types of measurement (self-report, observational and physiological) and their applications. Self-report is best used when measuring things that relate directly to the self (like stress levels). Observational is used when recording observable behaviors, such as smiling or the time it takes to finish a puzzle. Finally, physiological data refers to things that are measured with biological data, like an MRI or hormone levels.

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Frequency: describes a rate or degree of a variable. (15% of these people....)
Association: Argues that one level or a variable is linked to a level of another variable. there are also positive (+ slope) and negative (- slope) associations.
Casual: Argues that one of the variables in association claim is responsible for a change in the other variable.

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Cluster: when you divide a population into clusters (groups) and randomly select a certain number of these clusters as your sample.

Stratified random sampling: A random sampling technique where researchers identify a particular demographic category and randomly selects from each strata.

Simple random sampling: each member of the population of interest is assigned a letter/number, then using a software researchers generate random numbers. The individuals with those numbers are now the sample.

Systematic - this is when sample members are selected from a larger population according to a random starting point but with a fixed periodic interval

Oversampling:  a variation of Stratified random sampling, this is when the researcher intentionally overrepresents one or more groups in their sample to obtain more precise statistical estimates. Final results will be adjusted so that members of the oversampled group are weighted to their actual proportion in the population.

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Purposive sampling: when the population of interest is sampled in a non-random way, there are different kinds of purposive sampling.

Convenience: Choosing a sample that comes from easy to contact and readily available people. It is a biased form of sampling and isn’t representative of a population.

Snowball sampling - This is a form of sampling that has current participants in the study find or recruit more participants

Quota - this is where elements from the population are chosen on a non-random basis and all the members of the population don’t have an equal chance of being a part of the sample group

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Methods of sampling and external validity

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Methods of sampling and external validity

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-When you want your study to have good external validity and be generalizable to the population of interest, random/probability sampling is incredibly important.

-For external validity, the way the population was sampled is more important than the size of the sample.

-To support a frequency claim, external validity is one of the most important validities.

-In some cases you can have a non-random sample support a frequency claim if the sample's bias does not affect/is not relevant to the claim.

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Research should not be based on personal experience (E.X. Radical Mastectomy, removal of body parts which contained cancer)
Comparison Groups
Intuition is biased (Good story, what comes to mind, evidence we like, bias-bias)

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In the field of psychology, the goal of producers is more theoretical, spending their careers in a more lab-based environment, while consumers use research to augment their practice of psychology.

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Basic research focuses on a broad question, applied research focuses on very specific questions

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All psychologists follow norms of scientific evaluation, share their findings, not letting their personal feelings taint the truth of their findings, and requiring evidence for everything. Journalists sometimes misrepresent or excaudate facts, as seen with the "Mozart Effect"