D514 Questions with CORRECT Answers (Grade A+)
Question 1:
ANOVA
Answer:
Analysis of variance (ANOVA) may be used in research studies where there are three or more groups
to compare. Stands for analysis of variance. The ANOVA test is similar to the independent-samples
t-test. It is used as a test to compare means between independent variables with similar variance and
normality of distribution. Whereas the t-test compares just two means, an ANOVA test can be used to
compare multiple groups. Example: Researchers are performing a randomized clinical trial evaluating
the effectiveness of three distinct treatments for painful TMJ clicking. 60 patients were randomly
assigned to 3 treatment options: (A) anterior positioning splint therapy; (B) physical therapy; (C)
physical therapy and splinting. Similar to the t-test in that patients are all from same type of distribution
(they have the same medical diagnosis) but there are more than 2 treatments in the experimental study.
Question 2:
Chi-Square Test
Answer:
Determines if an association exists between two categorical variables. Qualitative in nature. Tests for
relationships between categorical variables. Chi Square is used when you are comparing values you can
observe with those you expect. • Chi square is a group of tests generally used with qualitative or
non-normal data. There are chi-square tests that can be used to compare independent measures as well
as tests to look for differences or for relationships Example: The owner of a laboratory wants to keep
sick leave as low as possible by keeping employees healthy through disease prevention programs.
Many employees get flu in the winter months, leading to productivity problems due to sick leave from
the disease. There are many sources for flu vaccine today, and the owner believes that it is important to
get as many employees vaccinated as possible. Due to a regional shortage, there was only enough
vaccine for half the employees, last flu season. In effect, there are two groups; employees who received
the vaccine and employees who did not receive the vaccine. The company sent a nurse to every
employee who calls in sick with flu, to provide care if needed, and provide a nursing assessment. They
kept track of the number of employees who contracted flu and the severity of symptoms.
Question 3:
Control Group
Answer:
this group of patients does not receive the treatment that is being studied
,Question 4:
Experimental Group
Answer:
This group receives the treatment being studied with follow-up observation to determine the effect of
the treatment.
Question 5:
F-test
Answer:
The F-test is designed to test if two population variances are equal. The ratio of the two variances is
compared. If they are equal, the ratio of the variances will be 1. For equality of variance: It is most
often used when comparing statistical models that have been fitted to a data set, to identify the model
that best fits the population from which the data were sampled. Ensuring two samples are 'not
statistically different'. Two population variables are equal. Rules for tests F, z and t: dependent variable
must be measured on the interval scale; samples must come from normally distributed population; for
tests z and t, the variance of the samples must be equal.
Question 6:
Frequency
Answer:
measure how often a particular value occurs to assess the importance of a value or check the variation
of the values in a study.
Question 7:
Hypothesis
Answer:
A proposed explanation for an observation that leads to a prediction. Through investigation and the use
of statistical data, those doing the study will either confirm or reject the hypothesis. Testing the
hypothesis will show if there is a link (or not) between two or more variables.
Question 8:
Integrity
Answer:
Research always makes some assumptions, depending on the type of method used. Research
assumptions must be identified to determine possible breaches of integrity.
,Question 9:
Interval Data
Answer:
includes units of equal size, such as IQ results. There is no zero point. An example of interval scale is
time: Time is measured in 24 hours in each day; the time between each hour is the same, 60 minutes.
Question 10:
Mean
Answer:
The arithmetic average. Divide the sum of all scores by the total number of scores.
Question 11:
Median
Answer:
The midpoint of the distribution of values, or the point above or below which 50 % of the values fall.
Question 12:
Methods section components
Answer:
When analyzing the quality of a study, a careful evaluation of the research methods can reveal critical
details about population and sample, covariables and hypothesis, data presentation, statistical analysis,
and study limitations.
Question 13:
Misleading statistics components
Answer:
Interpreting and presenting the results of data analysis affords many opportunities for accidental or
deliberate misrepresentation of the data. Common examples include implying causation, extrapolating
beyond the reasonable, relying on a biased or incomplete sample, and using inappropriate graphical
representations.
Question 14:
Mode
Answer:
the value that occurs most frequently in the data. Think most
, Question 15:
Multivariate regression analyses
Answer:
Can be used to analyze and adjust risk. This analysis model contrasts each measured factor to the
patients risk of a particular outcome.
Question 16:
Nominal data
Answer:
Can be measured as a frequency or percentage, and the mean of these data cannot be calculated.
Nominal data in healthcare might include demographic data about patients. The work nominal means
"pertaining to name" Names (category) Nominal Data: various cancer types; different insurance
companies; physician specialties offered at a clinic; types of medical units in a hospital (ICU,
pediatrics, med-surg)
Question 17:
Ordinal Data
Answer:
can be measured as a frequency, and the mean of ordinal data is often calculated. Ordinal data in
healthcare might include patient satisfaction surveys using a Likert scale. The word ordinal means to
"put in order" Ordinal Data: data found in a Likert scale; pain scale; stages of cancer; trimesters of
pregnancy. Ordinal Variable is a type of variable where the order or ranking of values matters, but the
differences between values may not be uniform. In ordinal variables, the intervals between values are
not consistent. An example is a pain scale where the order of pain severity matters, but the difference
between a rank of "7" and "5" may not be the same as between "5" and "3".
Question 18:
Parametric Tests Based on probability distribution.
Answer:
Statistical tests that make assumptions about the parameters of the population distribution(s) from
which the data are drawn. These tests are based on specific distributional assumptions and are used in
inferential statistics. Parametric tests are sensitive to the underlying distribution of data and are
applicable when certain conditions are met. Parametric: summarizes a population (the entire group you
are interested in investigating) Using a t-test to compare the means of two groups when the data are
assumed to follow a normal distribution.
Question 1:
ANOVA
Answer:
Analysis of variance (ANOVA) may be used in research studies where there are three or more groups
to compare. Stands for analysis of variance. The ANOVA test is similar to the independent-samples
t-test. It is used as a test to compare means between independent variables with similar variance and
normality of distribution. Whereas the t-test compares just two means, an ANOVA test can be used to
compare multiple groups. Example: Researchers are performing a randomized clinical trial evaluating
the effectiveness of three distinct treatments for painful TMJ clicking. 60 patients were randomly
assigned to 3 treatment options: (A) anterior positioning splint therapy; (B) physical therapy; (C)
physical therapy and splinting. Similar to the t-test in that patients are all from same type of distribution
(they have the same medical diagnosis) but there are more than 2 treatments in the experimental study.
Question 2:
Chi-Square Test
Answer:
Determines if an association exists between two categorical variables. Qualitative in nature. Tests for
relationships between categorical variables. Chi Square is used when you are comparing values you can
observe with those you expect. • Chi square is a group of tests generally used with qualitative or
non-normal data. There are chi-square tests that can be used to compare independent measures as well
as tests to look for differences or for relationships Example: The owner of a laboratory wants to keep
sick leave as low as possible by keeping employees healthy through disease prevention programs.
Many employees get flu in the winter months, leading to productivity problems due to sick leave from
the disease. There are many sources for flu vaccine today, and the owner believes that it is important to
get as many employees vaccinated as possible. Due to a regional shortage, there was only enough
vaccine for half the employees, last flu season. In effect, there are two groups; employees who received
the vaccine and employees who did not receive the vaccine. The company sent a nurse to every
employee who calls in sick with flu, to provide care if needed, and provide a nursing assessment. They
kept track of the number of employees who contracted flu and the severity of symptoms.
Question 3:
Control Group
Answer:
this group of patients does not receive the treatment that is being studied
,Question 4:
Experimental Group
Answer:
This group receives the treatment being studied with follow-up observation to determine the effect of
the treatment.
Question 5:
F-test
Answer:
The F-test is designed to test if two population variances are equal. The ratio of the two variances is
compared. If they are equal, the ratio of the variances will be 1. For equality of variance: It is most
often used when comparing statistical models that have been fitted to a data set, to identify the model
that best fits the population from which the data were sampled. Ensuring two samples are 'not
statistically different'. Two population variables are equal. Rules for tests F, z and t: dependent variable
must be measured on the interval scale; samples must come from normally distributed population; for
tests z and t, the variance of the samples must be equal.
Question 6:
Frequency
Answer:
measure how often a particular value occurs to assess the importance of a value or check the variation
of the values in a study.
Question 7:
Hypothesis
Answer:
A proposed explanation for an observation that leads to a prediction. Through investigation and the use
of statistical data, those doing the study will either confirm or reject the hypothesis. Testing the
hypothesis will show if there is a link (or not) between two or more variables.
Question 8:
Integrity
Answer:
Research always makes some assumptions, depending on the type of method used. Research
assumptions must be identified to determine possible breaches of integrity.
,Question 9:
Interval Data
Answer:
includes units of equal size, such as IQ results. There is no zero point. An example of interval scale is
time: Time is measured in 24 hours in each day; the time between each hour is the same, 60 minutes.
Question 10:
Mean
Answer:
The arithmetic average. Divide the sum of all scores by the total number of scores.
Question 11:
Median
Answer:
The midpoint of the distribution of values, or the point above or below which 50 % of the values fall.
Question 12:
Methods section components
Answer:
When analyzing the quality of a study, a careful evaluation of the research methods can reveal critical
details about population and sample, covariables and hypothesis, data presentation, statistical analysis,
and study limitations.
Question 13:
Misleading statistics components
Answer:
Interpreting and presenting the results of data analysis affords many opportunities for accidental or
deliberate misrepresentation of the data. Common examples include implying causation, extrapolating
beyond the reasonable, relying on a biased or incomplete sample, and using inappropriate graphical
representations.
Question 14:
Mode
Answer:
the value that occurs most frequently in the data. Think most
, Question 15:
Multivariate regression analyses
Answer:
Can be used to analyze and adjust risk. This analysis model contrasts each measured factor to the
patients risk of a particular outcome.
Question 16:
Nominal data
Answer:
Can be measured as a frequency or percentage, and the mean of these data cannot be calculated.
Nominal data in healthcare might include demographic data about patients. The work nominal means
"pertaining to name" Names (category) Nominal Data: various cancer types; different insurance
companies; physician specialties offered at a clinic; types of medical units in a hospital (ICU,
pediatrics, med-surg)
Question 17:
Ordinal Data
Answer:
can be measured as a frequency, and the mean of ordinal data is often calculated. Ordinal data in
healthcare might include patient satisfaction surveys using a Likert scale. The word ordinal means to
"put in order" Ordinal Data: data found in a Likert scale; pain scale; stages of cancer; trimesters of
pregnancy. Ordinal Variable is a type of variable where the order or ranking of values matters, but the
differences between values may not be uniform. In ordinal variables, the intervals between values are
not consistent. An example is a pain scale where the order of pain severity matters, but the difference
between a rank of "7" and "5" may not be the same as between "5" and "3".
Question 18:
Parametric Tests Based on probability distribution.
Answer:
Statistical tests that make assumptions about the parameters of the population distribution(s) from
which the data are drawn. These tests are based on specific distributional assumptions and are used in
inferential statistics. Parametric tests are sensitive to the underlying distribution of data and are
applicable when certain conditions are met. Parametric: summarizes a population (the entire group you
are interested in investigating) Using a t-test to compare the means of two groups when the data are
assumed to follow a normal distribution.
