These notes capture the key concepts, discussions, and important information from the class sessions. They are intended to provide a comprehensive summary of the material covered, including lecture highlights, significant topics, and any additional insights provided by the instructor.
Chapter 1
Individual – the object that is being described, who we are describing
Variable – what we are measuring, can vary
You will get data from variables that describe individuals
Categorical (qualitative) variables – different categories (major, race, etc.)
Numerical (quantitative) variable – numerical values (time, income, etc.)
Age can depend on what question you are asking (ex: are you 20s, 30-35, 35-40? vs what is your age in years?)
Observational study – observing, as a researcher you are NOT intervening, usually descriptive, opposite of
experiment
Response variable (dependent) – measures an outcome or results of a study
Experiments impose mechanisms (cause and effect) whereas observational is describing what is happening
Sample surveys – important kind of observational study, survey a group of individuals to represent a larger group
(using generalizations or inferences to conclude something about the larger group)
Population – entire group of individuals about which we want information
Sample – a group of individuals to represent a larger group, usually perform statistics on samples
Census – attempts to include the entire population in the “sample”. Takes a lot of time and resources, even a census
can miss people and be inaccurate
Experiments – researcher is actively involved to observe individual’s responses, purpose is to study whether the
treatment causes a change to the response
Be sure the variables in a study really do tell you what you want to know
Chapter 2
If people are really motivated to respond it can leave lesser opinions out of the sample (introduces bias)
Convenience sampling overrepresents some parts of the population and underrepresents others which can be very
different from the opinion of the population as a whole (mall samples = biased)
Depending on where the question is posed there can automatically be a convenience sampling bias (in a newspaper
column or on a certain website)
Simple random sampling (SRS) = “gold standard”
A specified size n
Consists of n individuals from the population
Chosen in such a way that every set of n individuals has an equal chance to be the sample actually selected
(every individual has an equal chance of being one in the sample)
Minimizes the chances of bias
Cannot guarantee that we will obtain info for separate groups of individuals
Table of random digits (all digit groups need to have the same number ex: 001 through 100)
1. Each entry in the table is equally likely to be any of the 10 digits, 0 through 9
2. The entries are independent of each other (unique elements)
Chapter 3
Parameters and statistics (hint: population and parameter start with p, whereas sample and statistics start with s)
(p) Parameter (in practice we don’t really know what it is)
A number that describes the population
A single number that we know “exists” (theoretically)
( - p hat) Statistic
A number that describes the sample
The value of a statistic is known when we have taken a sample, but it can change from sample to sample
We often use a statistic to estimate an unknown parameter
One parameter is called proportion
Number of people or individuals that share the common trait or interest/the number of people in the sample
Measures the percent of people who share a trait/opinion of interest
Use the statistic from our sample to estimate the parameter in the population
Sampling variability
If two samples are taken, they are likely to be different
Statistics won’t be the same across samples
Because all samples are going to be a little different from each other
However, if we have a good sampling scheme then the sample statistic will estimate the
population parameter fairly well
, Bias
Consistent, repeated deviation of the statistic from the parameter in the same direction when we take many
samples
To reduce bias: random sampling
Variability
How spread out the values of the statistic are when we take too many samples
To reduce variability: use a larger sample, larger samples less variability across samples
Large random samples are more likely to give an estimate that is close to the truth (population parameter)
Margin of error (MOE)
Quantifies the uncertainty in our estimate by showing how much the statistic would vary from sample to
sample
uncertainty - error attributable to sampling and other random effects
in psychology, this is more often referred to a confidence interval (for 95% confidence )
decrease sample size, increase margin of error
confidence statement interprets a confidence interval and has two parts
a margin of error : says how close the statistic lies in the parameter
a level of confidence : says what percentage of all possible samples results in a confidence interval contains
the true parameter
Chapter 4
How sample surveys go wrong
- Random sampling
o Eliminates bias in choosing a sample
o Allows control of variability
- However, more difficult to do in the real work
- Additionally, many different types of error that are possible beyond what factors into margin of error in
confidence interval statements
o MOE only include one error due to randomness sampling
o You can increase the sample size to reduce MOE
Sampling errors
- Errors caused by the act of taking a sample, happens because of sampling
o They cause sample results to be different from the results of the census
- Random sampling error: the difference between the sample statistic and the population parameter caused by
chance in selecting a random sample
o The margin of error in the confidence statement only includes random sampling error
o You can reduce this just by increasing your sample size
Non sampling errors
- Errors not related to the act of selecting a sample from the population
- They can be present even in a census
- Frame errors: happens because of issues with sampling frame
o A list of people from which one attempts to draw a sample
o Under coverage = one type of frame error
Results from an incomplete sampling frame
Certain groups of the population are left out
o Erroneous inclusions
The frame includes people who are not really part of the population
Ex: if someone contacts a person based on the area code of cell phone number, however
the subject no longer lives in the state with that area code
o Multiple inclusions
When someone has a chance of being contacted twice
Ex: listed twice in a phonebook
- Processing errors
o Mistakes in mechanical tasks such as math or data entry
o Less common with computers and automation
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