Chapter 12 - Healthcare Information / Chapter 13 - Research and Data Analysis

Question 1

dashboard vs scorecard

Answer 1

A dashboard is different from a scorecard.

The dashboard is a data analytics tool that is a computerized visual display of specific data points. Typically, a dashboard focuses on a process and the rate of achievement.

A scorecard, which can also be a computerized visual display, focuses on outcome or goal achieved, such as money raised for an event or cause.

Both a dashboard and a scorecard can involve key indicators. A key indicator is a measurement or value which gives you an idea of what something is like

Question 2

extrapolate

Answer 2

1. to predict by projecting past experience or known data
2. to project, extend, or expand (known data or experience) into an area not known or experienced so as to arrive at a usually conjectural knowledge of the unknown area
3. to infer (values of a variable in an unobserved interval) from values within an already observed interval

Question 3

executive information system (EIS)

Answer 3

a decision support system (DSS) used to assist senior executives in the decision-making process

Question 4

senior executive

Answer 4

also called upper management or executive management

an individual at the highest level of management of an organization

Question 5

point-of-care (POC) charting

Answer 5

A system whereby information is entered into the health record at the time and location of service.

Question 6

social media

Answer 6

the means of interactions among people in which they create, share, and/or exchange information and ideas in virtual communities and networks

Question 7

ethnography

Answer 7

the scientific description of the customs of individual peoples and cultures

Question 8

analysis of variance (ANOVA)

Answer 8

A statistical technique that is used to check if the means of two or more groups are significantly different from each other. ANOVA checks the impact of one or more factors by comparing the means of different samples.

Question 9

chi-square test

Answer 9

A hypothesis test designed to test for a statistically significant relationship between nominal and ordinal variables organized in a bivariate (involving two variables) table. In other words, it tells us whether two variables are independent of one another.

Question 10

confounder

Answer 10

also confounding variable, confounding factor, extraneous determinant or lurking variable

it is a variable that influences both the dependent variable and independent variable, causing a spurious association

Question 11

spurious

Answer 11

(1) not being what it purports to be; false or fake
(2) (of a line of reasoning) apparently but not actually valid
(3) born to parents not married to each other

Question 12

purport

Answer 12

1. to have the often specious appearance of being, intending, or claiming (something implied or inferred)
2. claim
3. intend, purpose
4. meaning conveyed, professed, or implied

Question 13

specious

Answer 13

1. having a false look of truth or genuineness

2. having deceptive attraction or allure

Question 14

sophistic

Answer 14

1. plausible but fallacious
2. subtly deceptive reasoning or argumentation
3. related to a group of Greek philosophers called Sophists, famous for their fallacious reasoning

Question 15

fallacious

Answer 15

1. embodying a fallacy

2. tending to deceive or mislead: DELUSIVE

Question 16

fallacy

Answer 16

1. a false or mistaken idea
2. deceptive appearance
3. an often plausible argument using false or invalid inference

Question 17

plausible

Answer 17

1. superficially fair, reasonable, or valuable but often specious
2. superficially pleasing or persuasive
3. appearing worthy of belief

Question 18

discrete variable vs continuous variable

Answer 18

Discrete variables are countable in a finite amount of time. For example, you can count the money in your bank account or even in everyone’s bank account.

Continuous variables would (literally) take forever to count. In fact, you would get to “forever” and never finish counting them. For example, take age. You can’t count “age”. Why not? Because it would literally take forever. For example, you could be:
25 years, 10 months, 2 days, 5 hours, 4 seconds, 4 milliseconds, 8 nanoseconds, 99 picoseconds…and so on forever into smaller units.

Question 19

levels of measurement

Answer 19

also called scales of measurement
A classification approach that describes the nature of information within the values assigned to variables. The four levels of measurement are:

Nominal: the data can only be categorized (e.g. black, white, Asian, Hispanic)

Ordinal: the data can be categorized and ranked (e.g. agree, neutral, disagree; low income, middle income, high income; true or false when measuring truth value; innocent or guilty when making judgments in court; 1st, 2nd, 3rd)

Interval: the data can be categorized, ranked, and evenly spaced, but there is no true zero point (e.g. temperature, SAT score (200-800), credit score (300-850))

Ratio: the data can be categorized, ranked, evenly spaced, and has a natural zero.
(e.g. age: from 0 to death, weight: from 0 to whatever the weight is, etc.)

Variables that are based on the four levels of measurement share the name with the four levels of measurement (e.g. you have nominal variables, ordinal variables, interval variables, and ratio variables)

Question 20

categorical variable
(also called qualitative variable)

Answer 20

a variable that can take on one of a limited, and usually fixed, number of possible values

Examples of values that might be represented in a categorical variable:
The roll of a six-sided die: possible outcomes are 1, 2, 3, 4, 5, or 6.
The blood type of a person: A, B, AB or O.

Question 21

ordinary variable vs array variable

Answer 21

An ordinary variable can hold only one value whereas an array variable can refer to a group of values of the same data type.

Question 22

t-test

Answer 22

The t test tells you how significant the differences between groups are; in other words it lets you know if those differences (measured in means) could have happened by chance.

Question 23

correlational studies

Answer 23

also called correlational research

it is the study of the relationship between two variables with the help of statistical analysis

Question 24

descriptive studies

Answer 24

They are observational studies which describe the patterns of disease occurrence in relation to variables such as person, place and time. It is often the first step or initial inquiry into a new topic, event, disease or condition.

Question 25

grounded theory

Answer 25

Grounded theory involves the collection and analysis of data. The theory is “grounded” in actual data, which means the analysis and development of theories happens after you have collected the data.

Question 26

incidence rate

Answer 26

The term incidence rate refers to the rate at which a new event occurs over a specified period of time. Put simply, the incidence rate is the number of new cases within a time period (the numerator) as a proportion of the number of people at risk for the disease (the denominator).

Question 27

numerator vs denominator

Answer 27

First, a fraction is made up of two integers—one on the top, and one on the bottom.

The top one is called the numerator, the bottom one is called the denominator, and these two numbers are separated by a line.

Question 28

four measures of variability

Answer 28

They are numbers that describe the diversity or dispersion in the distribution of a given variable.

The four measures of variability are the range (the difference between the largest and smallest observations), the interquartile range (the difference between the 75th and 25th percentiles), the variance, and the standard deviation.

Question 29

variance

Answer 29

Variance is a measure of dispersion, meaning it is a measure of how far a set of numbers is spread out from their average value.

Question 30

standard deviation

Answer 30

In statistics, the standard deviation is a measure of the amount of variation or dispersion of a set of values. A low standard deviation indicates that the values tend to be close to the mean of the set, while a high standard deviation indicates that the values are spread out over a wider range.

Question 31

percentile

Answer 31

A value on a scale of 100 that indicates the percent of a distribution that is equal to or below it.

For example:
The 75th percentile is the value at which 25% of things are above that value and 75% of things are below that value.
The 25th percentile is the value at which 75% of things are above that value and 25% of things are below that value.

Question 32

prospective study

Answer 32

also called prospective cohort study
A longitudinal cohort study that follows over time a group of similar individuals (cohorts) who differ with respect to certain factors under study, to determine how these factors affect rates of a certain outcome.
For example, one might follow a cohort of middle-aged truck drivers who vary in terms of smoking habits, to test the hypothesis that the 20-year incidence rate of lung cancer will be highest among heavy smokers, followed by moderate smokers, and then nonsmokers.

Question 33

retrospective study

Answer 33

Also called retrospective cohort study or historic cohort study
A study that compares two groups of people: those with the disease or condition under study (cases) and a very similar group of people who do not have the disease or condition (controls).

Question 34

frequency

Answer 34

the number of occurrences of a repeating event per unit of time

Question 35

rhythm

Answer 35

a strong, regular, repeated pattern of movement or sound

Question 36

oscillation

Answer 36

1. movement back and forth at a regular speed

2. regular variation in magnitude or position around a central point

Question 37

magnitude

Answer 37

the size or extent of something

Question 38

regression equation

Answer 38

the mathematical expression of the relationship between a dependent (outcome or response) variable and one or more independent (predictor) variables that results from conducting a regression analysis

Question 39

graph

Answer 39

A pictorial (visual pictures) representation that represents data or values in an organized manner. Also called a diagram.

Question 40

Answer 40

A normal distribution, sometimes called the bell curve, is a distribution that occurs naturally in many situations. For example, the bell curve is seen in tests like the SAT and GRE. The bulk of students will score the average (C), while smaller numbers of students will score a B or D. An even smaller percentage of students score an F or an A. This creates a distribution that resembles a bell (hence the nickname). The bell curve is symmetrical. Half of the data will fall to the left of the mean; half will fall to the right.

Many groups follow this type of pattern. That’s why it’s widely used in business, statistics and in government bodies like the FDA:
Heights of people.
Measurement errors.
Blood pressure.
Points on a test.
IQ scores.
Salaries.

Question 41

chart

Answer 41

1. a map
2. a sheet giving information in tabular form (e.g. medical records)

(tabular refers to table [aka rows and columns])

Question 42

Answer 42

(called a bar chart or bar graph)
A chart or graph that presents categorical data (i.e. categorical variable) with rectangular bars with heights or lengths proportional to the values that they represent. The bars can be plotted vertically or horizontally. A vertical bar chart is sometimes called a column chart.
(the left image is vertical, the right is horizontal)

Question 43

Answer 43

A histogram is an approximate representation of the distribution of numerical data.

To construct a histogram, the first step is to “bin” (or “bucket”) the range of values—that is, divide the entire range of values into a series of intervals—and then count how many values fall into each interval. The bins are usually specified as consecutive, non-overlapping intervals of a variable. The bins (intervals) must be adjacent and are often (but not required to be) of equal size.

Question 44

bar chart vs histogram

Answer 44

A bar chart is the graphical representation of categorical data using rectangular bars where the length of each bar is proportional to the value they represent.
A histogram is the graphical representation of data where data is grouped into continuous number ranges and each range corresponds to a vertical bar.

Histograms are used to show distributions of variables while bar charts are used to compare variables.

Bar charts: Equal space between every two consecutive bars. The x-axis can represent anything.
Histograms: No space between two consecutive bars. They should be attached to each other. The x-axis should represent only continuous data (continuous variable) that is in terms of numbers.

Question 45

Answer 45

A line graph is a type of chart used to show information that changes over time. We plot line using several points connected by straight lines. We also call it a line chart. The line graph comprises of two axes known as ‘x’ axis and ‘y’ axis.

Question 46

Answer 46

A scatter plot (aka scatter chart, scatter graph) uses dots to represent values for two different numeric variables. The position of each dot on the horizontal and vertical axis indicates values for an individual data point. Scatter plots are used to observe relationships between variables.

Question 47

Answer 47

A bubble chart (aka bubble plot) is an extension of the scatter plot used to look at relationships between three numeric variables. Each dot in a bubble chart corresponds with a single data point, and the variables’ values for each point are indicated by horizontal position, vertical position, and dot size.

The example bubble chart above depicts the points scored per game by teams in the regular season of the National Football League in 2018. Each bubble represents a single team’s performance. A bubble’s horizontal position notes the average points scored against that team each game, and the vertical position notes the average points scored by that team each game. Each bubble’s size indicates the number of wins earned by each team, with larger bubbles corresponding to higher win rates. (Ties are worth half a win.)

Question 48

Answer 48

A Pareto chart is a type of chart that contains both bars and a line graph, where individual values are represented in descending order by bars, and the cumulative total is represented by the line. The chart is named for the Pareto principle, which, in turn, derives its name from Vilfredo Pareto, a noted Italian economist.

The left vertical axis is the frequency of occurrence, but it can alternatively represent cost or another important unit of measure. The right vertical axis is the cumulative percentage of the total number of occurrences, total cost, or total of the particular unit of measure. To take the example below, in order to lower the amount of late arrivals by 78%, it is sufficient to solve the first three issues.

The purpose of the Pareto chart is to highlight the most important among a (typically large) set of factors. In quality control, Pareto charts are useful to find the defects to prioritize in order to observe the greatest overall improvement. it often represents the most common sources of defects, the highest occurring type of defect, or the most frequent reasons for customer complaints, and so on.

Question 49

Answer 49

A pie chart (or a circle chart) is a circular statistical graphic, which is divided into slices to illustrate numerical proportion.

In a pie chart, the arc length of each slice (and consequently its central angle and area), is proportional to the quantity it represents. While it is named for its resemblance to a pie which has been sliced, there are variations on the way it can be presented.

Question 50

Answer 50

In descriptive statistics, a box plot (also called box and whisker plot) is a method for graphically depicting groups of numerical data through their quartiles. Box plots may also have lines extending from the boxes (whiskers) indicating variability outside the upper and lower quartiles, hence the terms box-and-whisker plot and box-and-whisker diagram. Outliers may be plotted as individual points.

A box and whisker plot displays the five-number summary of a set of data. The five-number summary is the minimum, first quartile, median, third quartile, and maximum.

Minimum (Q0 or 0th percentile): the lowest data point excluding any outliers.
Also called lower extreme.
Maximum (Q4 or 100th percentile): the highest data point excluding any outliers. Also called upper extreme.
Median (Q2 or 50th percentile): the middle value of the dataset.
First quartile (Q1 or 25th percentile): also known as the lower quartile qn(0.25), is the median of the lower half of the dataset.
Third quartile (Q3 or 75th percentile): also known as the upper quartile qn(0.75), is the median of the upper half of the dataset.

Question 51

Answer 51

A stem-and-leaf display or stem-and-leaf plot is a device for presenting quantitative data in a graphical format, similar to a histogram, to assist in visualizing the shape of a distribution. Stemplots became more commonly used in the 1980s after the publication of John Tukey’s book on exploratory data analysis in 1977. The popularity during those years is attributable to their use of monospaced (typewriter) typestyles that allowed computer technology of the time to easily produce the graphics. Modern computers’ superior graphic capabilities have meant these techniques are less often used.

These are the values represented by the picture: 20, 30, 32, 35, 41, 41, 43, 47, 48, 51, 53, 53, 54, 56, 57, 58, 58, 59, 60, 62, 64, 65, 65, 69, 71, 74, 77, 88 and 102

Question 52

Answer 52

A frequency polygon is another graphical means to display a frequency distribution using continuous data in a line form. A single data point placed at the midpoint of the interval is used to mark the specific number of observations within that interval. Each point is then connected by a line.Frequency polygons serve the same purpose as histograms.

The image belowshows a frequency polygon over an outline of a histogram for the same data. In this example from the Centers for Disease Control and Prevention (CDC), it is easier to see the peak of the epidemic in the frequency polygon. Frequency polygons differ from line graphs in that frequency polygons (and histograms) display the entire frequency distribution (counts) of the continuous variable; a line graph plots only the specific data points over time

Question 53

measures of central tendency

Answer 53

A measure of central tendency is a single value that attempts to describe a set of data by identifying the central position within that set of data. As such, measures of central tendency are sometimes called measures of central location. They are also classed as summary statistics. The mean (often called the average) is most likely the measure of central tendency that you are most familiar with, but there are others, such as the median and the mode.

The mean, median and mode are all valid measures of central tendency, but under different conditions, some measures of central tendency become more appropriate to use than others.

Question 54

mode (statistics)

Answer 54

the value that appears most often in a set of data values

Question 55

median (statistics)

Answer 55

The value separating the higher half from the lower half of a data sample, a population, or a probability distribution. For a data set, it may be thought of as “the middle” value.