Stat 100 Lesson 1 & 2

ecological validity

The extent to which a study is realistic or representative of real life.

sampling unit

The individual person, animal, or object that has the measurement (observation) taken on them/it,

dependent variable

The outcome factor; the variable that may change in response to manipulations of the independent variable.

Probability

The rules of probability can tell us the likelihood of different types of samples that might arise from a particular population.

repeated measures design

The same participants are measured repeatedly

population

A group of individuals that belong to the same species and live in the same area,

interval variable

A measurement variable in which it makes sense to talk about differences, but not about ratios

systematic sampling

A procedure in which the selected sampling units are spaced regularly throughout the population; that is, every ninth unit is selected.

sample

A relatively small proportion of people who are chosen in a survey so as to be representative of the whole.

Validity/valid measurement

Actually measuring exactly what you intend to measure

double-blind study

An experiment in which neither the participant nor the researcher knows whether the participant has received the treatment or the placebo

independent variable

The variable that is varied or manipulated by the researcher.

Statistics

Numerical characteristics of the sample

Non-probability Sampling:

Sample does not have known probability of being selected as an inconvenience or voluntary response surveys, based on human choice rather than random selection,

Probability sampling

a known probability of being selected

bias

a measurement that is systematically off the mark in the same direction

multistage sampling

a probability sampling technique involving at least two stages: a random sample of clusters followed by a random sample of people within the selected clusters

meta-analysis

a quantitative review of collection of studies all done on a similar topic.

unit

a single individual or object to be measured

single-blind study

a study in which the participants are unaware of whether they are in the control group or the experimental group

confounding variable

a variable that affects the response variable and is also related to the explanatory variable, The effect of a confounding variable on the response variable cannot be separated from the effect of the explanatory variable, can't clearly determine that the explanatory variable is solely responsible for any effect on the response or outcome variable when a confounding variable is present.

response variable

a variable that measures an outcome or result of a study. example : height, weight, temperature, classification of symptom severity for an illness(the rate of acceleration)

discrete

can only take a set number of values examples: siblings

continous

can take any value within some interval example: height,weight

ordinal

categories have a logical order example: year,

nominal

categories having no logical ordering. example: smoke,gender

Observational study

collect data on participants in their naturally occurring settings/groupings, participants are observed in their naturally occurring groupings, only describe associations - they cannot demonstrate a cause-and-effect relationship, creates differences in the explanatory variable when assigning/randomly assigning treatments, allows for possible "cause and effect" conclusions if other precautions are taken with random assignment of the explanatory variable providing much stronger evidence for "cause and effect" conclusions, when the explanatory variable is randomly assigned, the researcher can minimize the effect of "confounding" variables

Reliability

consistency of measurement

Types of Nonprobability Samples

convenience, volunteer

randomized experiment

creates differences in the explanatory variable when assigning/randomly assigning treatments, allows for possible "cause and effect" conclusions if other precautions are taken with random assignment of the explanatory variable providing much stronger evidence for "cause and effect" conclusions, when the explanatory variable is randomly assigned, the researcher can minimize the effect of "confounding" variables example : flipping a coin, appropriate evidence can support cause and effect conclusions

descriptive statistics

describes the sample data numerically and visually.

cluster sampling

divide the population into groups,obtain a simple random sample of so many clusters from all possible clusters, obtain data on every sampling unit in each of the randomly selected clusters, cluster should be heterogeneous

categorical

divides cases into groups,place each case into one or more categories. Consist of group or category names.

matched pairs experiment

each case gets both treatments in random order (or cases get paired up in some other obvious way), and we examine individual differences in the response variable between the two treatments

matched paired designs

experiential designs that use either 2 matched individuals or the same person to receive 2 of each treatments

random assignment

explanatory variable(treatments) randomly assigned, removes confounding variables, possible to make association claim

how to decrease The margin of error ?

increase sample size

census

is an attempt to collect data from every member of the population,

treatment

is one or a combo of categories of the explanatory variables assigned by the experimenter.

sampling frame

list of individuals from which a sample is actually selected,

ratio variable

meaningful value of zero, and it makes sense to talk about the ratio of one value to another

measurement

measures a number quantity for each case. numbers or counts

Parameter

numerical characteristic of the population

experiment

participants are assigned to the groups being compared by the researcher.

stratified sampling

partition the population into groups based on a factor that may influence the variable that is being measured, obtain a simple random sample from each group, collect data on each sampling unit that was randomly sampled from each group, split into fairly homogeneous groups.

case-control study

people with the response of interest form a group of "cases" and are compared to a group of "controls" who are in similar circumstances except for the fact that they have the response,

catergorical variable

possible choices are "words" or "categories

measurement and discrete

possible choices are numbers, Discrete measurement variables cannot be subdivided into smaller and smaller fractional parts, example: no one can count all phone calls in one day

measurement and continous

possible choices are numbers, continuous variable because it can assume a range of values on a continuum, measurement variables that are continuous because it can be subdivided into into fractional parts, "an amount of " something.

random selection

procedure that ensures every person in a population has an equal chance of being chosen to participate

randomized comparative experiment

randomly assign cases to different treatment groups and then compare results on the response variables

random sampling

sample that was randomly chosen from the population , removes bias, can generalize to the population

randomized block design

similar experimental units are first placed together in groups , then treatments are randomly assigned separately within each block.

types of probability sampling

simple random sampling cluster stratified systematic

experimental units

smallest basic objects to which we can assign different treatments in a randomized experiment

case control study

studies people with the response of interest form a group of cases and are compared to a group of controls who are are in similar circumstances except for the fact that they have the response.

retrospective study

studies subjects with different levels of response variable are examined to see what levels of the response arise over time.

effect modifier

subgroup variable that modifies the effect of the explanatory variable on the outcome

Prospective study

subjects with different levels of an explanatory variables are followed to see what levels of response arise over time. people with different exposures or behaviors (the explanatory variables) are followed over time to see how many in each situation get the disease (the response variable)

inferential statistics

test a hypothesis, estimates a values or examines a relationship in the sample data to make inferences about the population

measurement error

the amount by which each measurement differs from the true value

Observational units (cases)

the objects or people measured in any study

placebo effect

the phenomenon in which the expectations of the participants in a study can influence their behavior

experimental unit

the smallest basic object to which one can assign different conditions (treatments), Examples of an experimental unit: person,animal,objects

explanatory variable

the variable used to form or define the different samples,variable that is used to explain differences in the groups in randomized experiments. Example: gender,type of plant or drug (net of force applied)

Quantitative variables

those we can record a numerical value and then order respondents according to those valuables

association

two variables that are associated if values of one variable tend to be related to values of the other

casusation

two variables that are causally associated if changing the value of one variable influences the value of the other variable

natural variability

variability that cannot be explained or predicted

The margin of error

1.measures the reliability of the percent or other estimate based on the survey data 2. is smaller when the sample size (n) is larger 3.does not provide information about bias or other errors in a survey

sample survey

a collection of data from a subset of the population chosen by the researcher, is also a type of observational study.