Data Analysis Flashcards

1
Q

must be based on a solid understanding of statistical analysis and epidemiological concepts.

A

Definitions used in data analysis

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2
Q

The data include all positive cases, taking into account variables and decreasing the number of false-negatives.

A

Sensitivity

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3
Q

The data include only those cases specific to the needs of the measurement, excluding those from a different population thereby decreasing the number of false-positives.

A

Specificity

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4
Q

Data are classified according to subsets, taking variables into consideration.

A

Stratification

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5
Q

The tool/indicator collects and measures the necessary data

A

Recordability

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6
Q

Results should be reproducible.

A

Reliability

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7
Q

The tool or indicator should be easy to use and understand.

A

Usability

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8
Q

Collection measures the target adequately, so that the results have predictive value.

A

Validity

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9
Q

a method by which to identify patterns and relationships in large amounts of data, such as the identification of risk factors or the effectiveness of interventions.

A

Knowledge discovery in database (KDD)

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10
Q

he steps to KDD include

A

selecting data, preprocessing (e.g., assembling target data set, cleaning data of noise), transforming data, data mining, and interpreting results.

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11
Q

the analysis (often automatic) of large amounts of data to identify underlying or hidden patterns.

A

Data Mining

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12
Q

may be applied to multiple patients’ electronic health records to generate information about the need for further examination or interventions.

A

Data Mining

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13
Q

The steps to data mining include

A

detecting anomalies, identifying relationships, clustering, classifying, regressing, and summarizing.

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14
Q

involves electronically searching through large amounts of information to find relevant items.

A

Data Mining

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15
Q

Data mining uses several tools to look for patterns:

A

Association rule mining
Classification
Clustering

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16
Q

This tool looks for patterns in which a certain data object shows up repeatedly (more than randomly) and is associated with an unrelated data object.

A

Association Rule mining

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17
Q

This tool looks for data group membership. An example would be the number of sunny days in a year.

A

Classification

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18
Q

This tool organizes data objects according to their similar characteristics. This results in a natural pattern or clustering of similar data.

A

Clustering

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19
Q

Data mining can also be called

A

Knowledge discovery

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20
Q

refers to the collection and summation of data for further use, such as for statistical analysis.

A

Data aggregation

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21
Q

may be used to collect information about an individual from multiple sources, often for targeted marketing purposes.

A

Data aggregation

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22
Q

show the spread or dispersion of data.

A

Measures of distribution

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23
Q

is the distance from the highest to the lowest number

A

Range

24
Q

measures the distribution spread around an average value.

A

Variance

25
Q

is the square root of the variance and shows the dispersion of data above and below the mean in equally measured distances

A

standard deviation

26
Q

a method of comparing rates or ratios.

A

Chi-square (X2)

27
Q

a means by which to establish if a variance in categorical data (as opposed to numerical data) is of statistical significance.

A

Chi-square test

28
Q

generally used to show whether there is a significant difference between groups or conditions being analyzed.

A

Chi- square testing

29
Q

used to analyze data to determine if there is a statistically significant difference in the means of both groups. examines two sets of data that are similar,

A

The “t” test

30
Q

used to evaluate the data sets found in scattergrams; it compares the relationship between the dependent variable and the independent variable to determine if the relationship correlates.

A

Regression analysis

31
Q

attempting performance improvement and developing practice guidelines without data can be problematic.

A

Integrating the results of data analysis

32
Q

should assist with case management, decision-making about individual care, improvement of critical pathways related to clinical performance, staff performance evaluations, credentialing, and privileging.

A

Integration of information

33
Q

the process of changing information from a given source (such as a data entry terminal) into information that can be understood by a destination point (such as a large database)

A

Data transformation

34
Q

Data transformation is performed in two steps:

A

Data mapping
code generation

35
Q

This process develops a map of how information flows from one place to another and figures out which parts of the information needs to be transformed.

A

Data Mapping

36
Q

This is when the actual transformation occurs and the data is converted into a form compatible with its destination.

A

Code generation

37
Q

can be verbal (e.g., spoken/written representations), analog (e.g., television, radio, telephone, recorded), or digital (e.g., coded).

A

Data Representation

38
Q

uses continuous waveform signals varying in intensity.

A

Analog representation

39
Q

uses codes (usually numeric), such as the binary code (base 2) to represent values.

A

Computerized representation of data

40
Q

comprised of strings of 1s and 0s with 1s stored in magnetized areas of disks and 0s stored in non-magnetized areas; thus, 1 represents “on,” and 0 represents “off.”

A

binary code

41
Q

Each representation (0 or 1) is referred to as a

A

Bit - binary digit

42
Q

8 bits =

A

1 byte

43
Q

1 byte can represent

A

256 characters

44
Q

1,000 bytes =

A

1 kilobyte

45
Q

1 million bytes =

A

1 megabyte

46
Q

1 billion bytes =

A

1 gigabyte

47
Q

1 trillion bytes =

A

1 terabyte

48
Q

the pattern of 0s and 1s used to represent characters.

A

The coding scheme

49
Q

the most common binary coding scheme is

A

American Standard Code for Information Interchange

50
Q

characters represent 4 binary bits; thus, 1 byte can be represented by 2 hexadecimal characters.

A

Hexadecimal

51
Q

Uses a base of 16 and 16 symbols (usually the numeral 1–9, representing values 0 to 9 and Arabic letters A through F, representing values 10–15).

A

Hexadecimal coding

52
Q

One digit (4 bits) is referred to as a

A

nibble

53
Q

8 bits/ 1 byte are referred to as

A

octet

54
Q

used with the Universal Character Set, is a standardized coding system that has a large capacity and can be used to represent text for most languages, including Asian languages.

A

The unicode standard coding scheme

55
Q

provides a specific numeric value for each character and can be used across multiple platforms.

A

Unicode

56
Q

representing all alphabets of the world languages, ideographic sets, symbols, and 100 scripts, and is particularly valuable for making coding accessible internationally.

A

Unicode