D514 Exam Study Material+Questions and Answers |
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ANOVA
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.
Chi-Square Test
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.
Control Group
this group of patients does not receive the treatment that is being studied
Experimental Group
This group receives the treatment being studied with follow-up observation to determine the
effect of the treatment.
F-test
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.
Frequency
measure how often a particular value occurs to assess the importance of a value or check the
variation of the values in a study.
Hypothesis
,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.
Integrity
Research always makes some assumptions, depending on the type of method used. Research
assumptions must be identified to determine possible breaches of integrity.
Interval Data
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.
Mean
The arithmetic average. Divide the sum of all scores by the total number of scores.
Median
The midpoint of the distribution of values, or the point above or below which 50 % of the values
fall.
Methods section components
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.
Misleading statistics components
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.
Mode
, the value that occurs most frequently in the data. Think most
Multivariate regression analyses
Can be used to analyze and adjust risk. This analysis model contrasts each measured factor to
the patients risk of a particular outcome.
Nominal data
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)
Ordinal Data
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".
Parametric Tests
Based on probability distribution.
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.
Verified
ANOVA
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.
Chi-Square Test
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.
Control Group
this group of patients does not receive the treatment that is being studied
Experimental Group
This group receives the treatment being studied with follow-up observation to determine the
effect of the treatment.
F-test
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.
Frequency
measure how often a particular value occurs to assess the importance of a value or check the
variation of the values in a study.
Hypothesis
,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.
Integrity
Research always makes some assumptions, depending on the type of method used. Research
assumptions must be identified to determine possible breaches of integrity.
Interval Data
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.
Mean
The arithmetic average. Divide the sum of all scores by the total number of scores.
Median
The midpoint of the distribution of values, or the point above or below which 50 % of the values
fall.
Methods section components
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.
Misleading statistics components
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.
Mode
, the value that occurs most frequently in the data. Think most
Multivariate regression analyses
Can be used to analyze and adjust risk. This analysis model contrasts each measured factor to
the patients risk of a particular outcome.
Nominal data
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)
Ordinal Data
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".
Parametric Tests
Based on probability distribution.
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.
