92 terms

Stats Midterm


Terms in this set (...)

the changing characteristic being measured
probability (p)
how likely it is for an outcome to occur
an estimate derived from a sample

ex: mean age of students in a class
an estimate for the whole group (population)

ex: mean age of students in the US
population (N)
group of objects or people that are alike

(determined by research)
sample (n)
a group of cases selected from the population for research

(population -- parameter)
(sample --statistic)
discrete variable
finite number of values; categories or counting units of whole numbers

ex: number of children in a family, types of insulin used
dichotomous variable
type of discrete variable with only two categories

ex: yes/no; male/female
continuous variable
infinite number of values between any two values, equal intervals, or any level of data capable of having a decimal

ex: height, weight, time in minutes, temperature
extraneous variable
variables that we may/may not understand; can influence findings
an example of an extraneous variable that is intrinsic to subjects or that cannot be changed due to research situation
independent variable (x)
variable measured or controlled by researcher; what you think is affecting the outcome
dependent variable (y)
outcome/final result; variable you wish to change due to experimental treatment
nominal data
lowest level of measurement; the numbers are just used as names showing sameness or differentness of a particular quality; discrete
ordinal data
we can rank things; discrete

ex: mild, moderate, or severe
interval/ratio data
highest level of measurement; exhaustive, exclusive, and ordered with numerically equal intervals

ex: temperature, scores on a test
visual analog scale
usually reported in categories or can be averaged

ex: rate your pain on a scale from 0-10
Likert-type scale
usually total and provide an average

ex: SA, A, U, D, SD
semantic differential scale
participants indicate their feelings about statistics in reference to the two opposing adjectives

ex: good/bad, smart/impaired, strong/weak
frequency distribution
frequency of each measure of a variable; summary of the numerical counts of the values or categories of a measurement
cumulative frequency
cumulative measure of frequency of a variable; number of observations with a value less than the maximum value of the variable interval
grouped frequency
a frequency distribution with the distinct intervals or groups to simplify the information; used when the entire frequency distribution would be too large to be meaningful; drawback is that some data may be lost
frequency table
presents a big-picture of your data; presents the values of the dependent variable, from lowest to highest, with a count of the frequency

(ie: how often each value occurred)
cumulative percentage distribution
summing of percentages from the first category of the table, ending with a cumulative percentage of 100%; same idea as cumulative frequency, but expressed as a percentage
diving data into different portions or bins
diving data into 100 equal parts

25% and 75% are quantiles
50% of the subjects will fall between the 25% and 75% because everything is equally divided
interquartile range
50% of the subjects will fall between the 25% and 75% because everything is equally divided
interval ratio
percentiles can be used with what types of data?
diving the data into four equal parts
what type of data can be used in bar charts?
bar charts
lines do not touch; each answer is distinct and in no particular order

x-axis = nominal variables
y-axis= frequencies or percentages
highest variable = mode
what type of data can be used in histograms?
shows the flow of data in ranked order; bars do touch

x-axis = ordinal or continuous data
y-axis = frequencies or percentages
highest variable = mode
continuous data
what type of data can be used in line graphs?
line graph
a figure that is developed by joining a series of points with a line to show how a continuous variable changes over time
scatter plot
each dot is subject and is placed where the score for variable x and the score for variable y are located

positive = upward slope
negative = downward slope
weak = scattered
moderate to strong = closely spaced dots creating a line
number of dots = sample size
can be seen on a scatter plot; can be an interest especially if most of your data has a strong correlation and the outliers are extreme
descriptive statistics
enable the researcher to describe the data; the level of measurement determines what analyses are possible; analysis often begins with central tendency
central tendency
mean, median, and mode; the best one to use depends on the level of measurement
can be used for nominal, ordinal, or interval/ratio data; it is the only option for nominal
number of peaks in a curve
one peak
two peaks
three peaks
can be used with ordinal or interval/ratio data
can be used with interval/ratio data
standard deviation
the average distance the values in your distribution fall from the mean

best measure of central tendency in interval/ratio
one standard deviation
two standard deviation
three standard deviations
more variability from a large range of scores
large standard deviation indicates:
less variability from a small range of scores
small standard deviation indicates:
population mean
mu (lowercase)
population standard deviation
sigma (lowercase)
simplest measure of deviation; SD is larger when the range is larger
what type of data can be used in ranges?
z scores
measure the distance between the mean and an observation
increasing the mean will shift the curve to the....
decreasing the mean will shift the curve to the...
flattent the curve
increasing variance (SD) will....
heighten the curve
decreasing the variance (SD) will.....
inferential stats
the ability to make inferences about populations based on sample measurements; involves associating probability with each variable
probability distribution
may be estimated by the normal distribution; uniform and never change
a group of objects or people that is alike on one or more dimensions as defined by the researcher
target population
the entire population in which the researcher is interested and would like to generalize the results of a study
accessible population
the population of subjects available for a particular study; subset of the target population
a group of cases selected from a population for the purpose of conducting your research
representative sample
contains all the attributes of the population in the same proportion that they occur in the population
inclusion criteria
required characteristics to be included in the sample based on characteristics of the population
exclusion criteria
factors which eliminate a subject from the sample
based on findings from the sample, we make "inferences" or statements of what we think is true for the population
enhances the generalizability of findings
supports inferences for the population
having a representative sample:
sampling method
process researches use to select subjects from the population being studied
random sampling
every member of the population has the same chance of being selected and the probability of being selected is known
simple random sampling
not feasible with large populations because the researcher has to have access to every member of the population; would work to draw a sample of students or RNs at a hospital
systemic random sampling
selecting subjects based on a standardized rule

ex: numbering the population, determining a starting point, and selecting every 4th person

not feasible with large populations
stratified random sampling
the population is divided into subpopulations (strata) based on characteristics of interest. the sample is then randomly selected from the subpopulations
cluster (random) sampling
uses a group, unit, or cluster rather than an individual; used when it is difficult to locate a list of the entire population
nonprobability sampling
subjects do not have the same chance of being selected for participation; it is not randomized
convenience sampling
most popular form of non probability sampling in healthcare research; subjects are in the right place at the right time; quantitative
quota sampling
select proportions of the sample for different subgroups; quantitative
network sampling
utilizes social networks to gather information; frequently used in groups that hesitate to participate in research; qualitative
purpose sampling
subjects selected because they have particularly strong bases of information
sampling error
differences between the sample and the population that occur due to chance
sampling bias
systemic error made in the sample selection that results in a nonrandom sample and therefor the findings are not representative of the population of interest
sampling distribution
all the possible values of a statistic from all the possible samples in a given population
central limit theorem
essentially with more and more samples. the resulting distribution of the averages tend to look more and more bell-shaped and normally distributed
null hypothesis (H0)
there is no relationship, no association, no effect, and no difference; we must presume the null is true until we can disprove it
alternative hypothesis (H1)
what the researcher actually believes to be true or present; there is a significant difference or a signifiant relationship
directional hypothesis
tells the specific direction of a relationship or in what way the populations will differ
non-directional hypothesis
simply tells whether there is a relationship or a difference