Ch 1 Flashcards
Population & Samples
Population-whole group (the set of all the individuals of interest in a particular study, characteristics are parameters PP)
Samples-small representative group (set of individuals must be selected from the population, characteristics are statistics SS)
Variables
measurable or recordable characteristic/condition that changes or has different value/level for different individuals (ex: age, gender, temp of room)(ways to describe: independent, dependent, extraneous, confounding, quasi-independant)
Data/ Datum
A data set is a collection of measurements or observations. A datum (singular) is a single measurement or observation and is commonly called a score or raw score.
Parameters & Statistics
A parameter is a value, usually numerical, that describes a population usually derived from measurements of the individuals in the population.
A statistic is a value, usually numerical value, that describes a sample usually derived from measurements of the individuals in the sample
Descriptive Statistics
Statistical procedures used to summarize, organize, and
simplify data.
-only about sample data (%, range, standard dev)
Sampling Error
the naturally occurring discrepancy, or error, that exists between
a sample statistic and the corresponding population parameter
Inferential statistics
consist of techniques that allow us to study samples and then
make generalizations about the populations from which they were selected.
-use sample to apply to population
(z- and t- scores)
Correlational Method
two different variables are observed to determine
whether there is a relationship between them.
-unlike experimental, cannot demonstrate cause and effect
correlation
- measured natural relationship between two variables (moving up and down together- must be more or less variables not variables like gender)
- AS THEY ARE does NOT establish cause and effect
- does not easily explain relationship
experimental method
goal of an experimental
study is to demonstrate a cause-and-effect relationship between two variables (using 1-manipulation and 2-control of extraneous variables)
experimental method
one variable is manipulated while another variable
is observed and measured. To establish a cause-and-effect relationship between the
two variables, an experiment attempts to control all other variables to prevent them
from influencing the results.
Independent Variable (cause)
the variable that is manipulated by the researcher. In
behavioral research, the independent variable usually consists of the two (or more) treatment conditions to which subjects are exposed. The independent variable consists of the
antecedent conditions that were manipulated prior to observing the dependent variable.
-we can change it independently of the DV
Dependent Variable (effect)
one that is observed to assess the effect of the treatment.
- the variable we measure
- this variable is dependent on our IV
Random assignment
-each participant has an equal
chance of being assigned to each of the treatment conditions.-equalizes extraneous variables by ensuring extraneous variables do not vary systematically…. they will vary unsystematically in an unbiased sample
extraneous variables should be controlled (control variables)
participant variables, environmental variables, okay as long as they do not vary systematically across groups which would be confounding variables
how to control extraneous variables
using 3 techniques: Random Assignment( each participant has equal chance of being assigned to each of treatment conditions), Matching(ensure equivalent groups or environments), Holding variables constant
confounding variables
An extraneous variable that varies systematically (not consistent) across groups
ex :color (environmental- can influence mood)
-we don’t want these they skew conclusion of the experiment by offering more than one explanation for the results
participant variables
- These are characteristics such as age, gender, and intelligence that vary from one individual to another. Individual differences relevant to our outcome variable
- ex: ability in the subject, personality traits like depression score
- type of extraneous
environmental variables
These are characteristics of the environment/situation such as
lighting, time of day, and weather conditions. A researcher must ensure that the
individuals in treatment A are tested in the same environment as the individuals
in treatment B.
-type of extraneous
ex: coffee, professor present, room temp, exam content
experimental conditions
aka Treatment condition-receive the experimental treatment (administered drug and depressive symptom measured)
control condition
individuals do not receive the experimental treatment.
Instead, they either receive no treatment or they receive a neutral, placebo treatment. The purpose of a control condition is to provide a baseline for comparison
with the experimental condition.
-group as it naturally occurs (not given drugs but depressive symptoms measured)
Constructs-Operational definitions
internal attributes or characteristics that cannot be directly
observed but are useful for describing and explaining behavior, so we measure what we cannot observe via an operational definition(did the students cram/ how many hours?/IQ)
-variables like intelligence, anxiety, and hunger are called constructs,
and because they are intangible and cannot be directly observed, they are often called
hypothetical constructs.
-An operational definition defines a construct in terms of external behaviors that can be observed and measured
Continuous Variables- Real Limits
continuous-Infinite number of possible values (falls between any two observed values)ex: time, height, weight
- A continuous variable is divisible into an infinite
number of fractional parts.
-2 points:Usually, no two participants will have the same value on a continuous variable, Sometimes we assign rounded numbers as values
(not the exact but range of values aka the real limits)
real limits- is top of interval or bottom of interval
Discrete Variables
Separate indivisible categories ex: dice values, number of children, days of the week
-consists of separate, indivisible categories. No values can exist
between two neighboring categories.
Scales of Measurement
Simplest to most complex:
Nominal-qualitatively different having to do with names, no directional relationship(can be ID numbers)
Ordinal-fixed order and quantitative relationship (S,M,L)
Interval-consistent size between values with arbitrary zero point (sat scores, degrees farenheight, addition and subtraction is possible)
Ratio- equal intervals between points and a meaningful zero point that represents “none” (weight, degrees kelvin, can add/subtract, and multiply/divide)
Qualitative v. Quantitative
Quantitative data is gathered by measuring and counting. Qualitative data is collected by interviewing and observing. Quantitative data is analyzed using statistical analysis, while qualitative data is analyzed by grouping it in terms of meaningful categories or themes.
Statistical Notation
Scores-X or Y-When we take a measurement, we obtain one value for each participant for each variable
Sample Size-n
Population Size-N
Summation- Σ (add all the set of scores from data) 2 points:
- The summation sign, Σ, is always followed by a symbol or mathematical
expression. 2. PEMDΣAS
Order of Operations
PEMDΣAS X-3,1,7,4 Xsquared-9,1,49,16 ΣX = 3 + 1 + 7 + 4 = 15 ΣXsquared = 9 + 1 + 49 + 16 = 75 (ΣX)squared = (15)2 = 225
Operational Definition
identifies a measurement procedure (a set of operations) for measuring an external behavior and uses the resulting measurements as
a definition and a measurement of a hypothetical construct. Note that an operational definition has two components. First, it describes a set of operations for
measuring a construct. Second, it defines the construct in terms of the resulting
measurements.
Real Limits (when measuring continuous variable)
are the boundaries of intervals for scores that are represented on a continuous number line. The real limit separating two adjacent scores is located exactly
halfway between the scores. Each score has two real limits. The upper real limit is
at the top of the interval, and the lower real limit is at the bottom.
Nominal scale of measurement
consists of a set of categories that have different names. Measurements on a nominal scale label and categorize observations, but do not make any
quantitative distinctions between observations.
Ordinal Scale of measurement
consists of a set of categories that are organized in an ordered
sequence. Measurements on an ordinal scale rank observations in terms of size or
magnitude.
Interval Scale of Measurement
consists of ordered categories that are all intervals of exactly the
same size. Equal differences between numbers on scale reflect equal differences in
magnitude. However, the zero point on an interval scale is arbitrary and does not
indicate a zero amount of the variable being measured.
Ratio scale of measurement
is an interval scale with the additional feature of an absolute zero
point. With a ratio scale, ratios of numbers do reflect ratios of magnitude.
