Week 3: Descriptive and Inferential Statistics Flashcards
What are descriptive statistics?
Descriptive statistics are used to summarize data in meaningful ways in health research
These statistics provide insight into the center or average of a data set.
What are the measures of central
tendency?
Mean: The arithmetic average of all data points.
Median: The middle value when data is sorted.
Mode: The most frequently occurring value in the data.
Frequently summarize data about variables such as
age, gender, and diagnosis.
What are the measures of dispersion or variability?
Range: The difference between the maximum and minimum values.
Variance: The average of squared differences from the mean.
Standard Deviation: The square root of the variance,
providing a measure of data spread.
What does it mean to operationalize a variable?
A concept is simply an idea. Examples of concepts in the
health sciences include health, illness, anxiety, and poverty.
Concepts are hard to study unless we formally define them
in some way. These definitions are called operational
definitions, and they often include a statement about how
the concept will be measured.
Example: Pain (concept), 1-10 rating scale (variable)
Example: Socioeconomic status (concept) yearly income
(variable)
How well does the variable describe the concept?
What does it mean to have a large standard deviation?
What does it mean to have a small standard deviation?
A large standard deviation means data points are more spread out from the mean, indicating greater
variability.
A small standard deviation indicates that data points are close to the mean, suggesting less variability.
Standard deviation essentially tells us how “tall and skinny” or “short and wide” the plot is.
What is Table 1?
Usually the first table in a research study
Describes the sample
Does the sample “look like” the population that the
researchers are trying to generalize their findings to?
What is inferential research?
Inferential statistics are used to make
inferences about a population from a sample
drawn from the population.
Is X associated with Y
Does X cause Y?
What are the differences between descriptive and inferential statistics?
Descriptive
Aims to summarize and describe data, providing
a clear and concise overview of the
characteristics of a dataset
Presented as a central tendency, variability or
graphs
Initial understanding of a topic, generate
hypotheses, or present data in a
comprehensible manner
Inferential
Seeks to make predictions, draw conclusions,
and test associations or causes about
populations based on sample data.
Hypothesis testing
Make informed decisions, assess the
significance of relationships or differences, and
draw actionable conclusions that extend to a
broader population.
What are independent variables?
The independent variable is the factor that researchers can manipulate or control in an experiment.
It is the cause or determinant that is believed to influence or have an effect on the dependent variable.
Researchers might deliberately change the independent variable to observe its impact on the dependent
variable (experimental research).
In observational research, the independent variable is the variable that is presumed to influence or cause
changes in another variable (the dependent variable), but it is not manipulated by the researcher.
What are dependent variables?
The dependent variable is the outcome or response that is
measured or observed as a result of changes in the
independent variable.
It is the effect or outcome that researchers are interested
in studying.
The dependent variable can change as a result of
variations in the independent variable.
In the medication effectiveness experiment, the
dependent variable might be the reduction in patient pain
levels after taking the medication.
Independent vs dependent variable
The independent variable is what is manipulated (experimental) or the variable of interest (observational).
The dependent variable is what we measure or observe as a result of changes in the independent variable.
Together, they allow us to investigate cause-and-effect relationships in nursing research.
Example: Does a exercise reduce the risk of heart disease
Independent Variable: How much you exercise
Dependent Variable: Heart disease.
The mean is another way of saying the average
a. True
b. False
a. true
It is possible to calculate a mean for a distribution of data measured at the nominal level.
a. True
b. False
b. false
Suppose a researcher noticed that although many universities were teaching most of their classes online, there were very few studies comparing learning outcomes in online teaching and face-to-face teaching. She decided to conduct a study about whether students who attended class in person had better grades than students who attended class on line. The independent variable in this study is:
Grades
Method of teaching
Class attendance (online vs. in person)
Learning outcomes
c. Class attendance (online vs. in person)
Suppose a researcher noticed that although many universities were teaching most of their classes online, there were very few studies comparing learning outcomes in online teaching and face-to-face teaching. She decided to conduct a study about whether students who attended class in person had better grades than students who attended class online. The dependent variable is:
a. Class attendance (online or face-to-face)
b. Method of teaching
c. Grades
d. University size
c. grades
