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NURS 341 - Nursing Research > Module 5 > Flashcards

Module 5 Flashcards

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1
Q

Four levels of statistical measurement

A

Nominal
Ordinal
Interval
Ratio

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2
Q

Nominal Measurement

A

The lowest in the hierarchy of measurement scales.
Involves labelling or categorizing - the number does not suggest rank or ability, it is simply the means of identifying data.

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3
Q

Ordinal Measurement

A

Ranks events or objects on some attribute, assigning numbers to each category.
i.e., shortest to tallest, best student to worst student, etc.

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4
Q

Interval Measurement

A

Involves ranking events or variables on a scale in which the intervals between the numbers are equal and the zero value is arbitrarily set and does not have an absolute value.
i.e., Stanford-Binet Intelligence Scale

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5
Q

Ratio Measurement

A

The highest form of measurement.
Ratio measurement has a true zero on the scale.
i.e., time, weight, height
Statistical procedures such as calculating means and standard deviations are suitable for ratio-level data.

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6
Q

Descriptive Statistics

A

Used to describe and synthesize data.

Includes: frequency distributions, measures of central tendency, measures of variability.

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7
Q

Frequency Distributions

A

A systemic listing of all the values of a variable from the lowest to the highest with the number of times (frequency) each value was observed.
Often presented in the form of a table, graph, or frequency polygon.

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8
Q

Measures of central tendency

A

Measures to calculate an average.

Three types: mean, median, mode

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9
Q

Mean

A

The sum of a set of scores divided by the number of scores.

The most widely used measure of central tendency.

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10
Q

Median

A

The middle score.

The score of the point in a distribution above which one-half of the scores lie.

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11
Q

Mode

A

The score that occurs most frequently.

Best used with nominal data such as gender.

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12
Q

Measures of Variability

A

Used to describe the dispersion or the spread of data.
Appropriate for specific kinds of measurement and types of distributions.
Several measures: range, percentile, standard deviation

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13
Q

Range

A

The difference between the highest and the lowest scores in a distribution.

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14
Q

Percentile

A

Assigns the score to a specific place within the distribution.
Describes the number of cases a given score exceeds.

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15
Q

Standard Deviation

A

The most commonly used measure of variability.

The average amount that each of the individual scores varies from the mean of the set of scores.

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16
Q

Bivariate Descriptive Statistics

A

Allow a researcher to consider two variables together and describe the relationship between the variables.
Shows a statistical relationship between variables.
i.e., correlations and crosstabulations

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17
Q

Correlations

A

Tell the researcher to what extent the variables are related.
i.e., is there a relationship between smoking and lung capacity?

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18
Q

Correlation coefficient (r)

A

An index that describes the relationship between two variables.
Possible values range from -1.00 through .00 to +1.00

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19
Q

Positive Correlation

A

Indicates that high scores on one variable are paired with high scores on the other variable and low scores on one variable are paired with low scores on the other variable.

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20
Q

Negative Correlation

A

Indicates that low scores on one variable are paired with high scored on the other variable and high scores on one variable are paired with low scores on the other variable.

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21
Q

Inferential Statistics

A

Based on the law of probability.
Used to draw conclusions about the population on the basis of data obtained from the sample.
Purposes are to estimate the probability that the sample accurately reflects the population and to test hypotheses about the population.
Should be used when the sample is randomly selected and the measurement scale is at the interval or ratio area.

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22
Q

Sample

A

Used as a basis for making estimates of population characteristics.

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23
Q

Probability Samples

A

Selection of sample units by random selection.

Most effective means of securing representative samples.

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24
Q

Sampling Error

A

The variation in the statistical values that different samples of the population may present.
Will effect the statistical probability that the sample will accurately reflect the population.

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25
Q

Parameter estimation

A

A useful way of estimating a population parameter, such as a mean, a proportion, or a difference in the mean of two groups.

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26
Q

Point estimation

A

Involves calculating a single statistic to estimate the parameter.

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27
Q

Confidence interval

A

Constructed around the point estimate.
Establishes a range of values for the population value and the probability that the population value falls within that range.

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28
Q

Null hypothesis

A

States that there is no relationship between the independent and dependent variables.

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29
Q

Research hypothesis

A

The prediction that the researcher makes about what will happen in the study.

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30
Q

Type I Error

A

Occurs when the researcher states that a relationship exists when none exists.
Falsely rejecting a null hypothesis.

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31
Q

Type II Error

A

Occurs when the researcher states that a relationship does not exist when it does.
Falsely accepting a null hypothesis.

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32
Q

Level of significance

A

Set before the study begins.
The probability of making a Type I error.
Most commonly .05 and .01

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33
Q

If a researcher states that the results are significant at the .05 level, it means:

A

Results like these are due to chance factors only 5 in 100 times.
There is a 95% chance that the sample results are not due to chance factors alone, but reflect the population accurately.
The odds of such results based on chance alone are .05 or 5%.
One can be 95% confident that the results are due to a real relationship in the population.

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34
Q

Parametric tests

A

Use the sample statistic to estimate the population parameter.
Allow the researcher to study the effects of variables on one another and their interaction.
Three characteristics:
1) they focus on population parameters
2) they require measurements on at least an interval scale
3) they involve other assumptions

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35
Q

Nonparametric tests

A

Require fewer assumptions than parametric tests because they are not based on population parameters and involve less restrictive assumptions about the shape of the distribution.
Usually used when the data has been measured on a nominal or ordinal scale.
Most useful when the data cannot be interpreted as interval-level measures.

36
Q

Bivariate Statistical Tests

A

Used to analyze the relationship between two variables.

Includes: t-tests, analysis of variance, chi-squared tests, and product-moment correlation coefficients.

37
Q

Multivariate statistical analysis

A

Deals with three or more variables simultaneously.
Increasing numbers of nurse researchers are using sophisticated multivariate statistical procedures to analyze their data.

38
Q

Three major challenges associated with qualitative analysis:

A

1) the absence of systematic rules for analyzing and presenting qualitative data.
2) the enormous amount of work required to organize the data
3) difficulties in reducing the data for reporting purposes.

39
Q

Template analysis style

A

Involves developing a template to organize the data.

40
Q

Editing analysis style

A

The researcher acts as an interpreter who searches the data for significant segments.

41
Q

Immersion/crystallization analysis style

A

The researcher becomes totally involved in and reflects on the data.

42
Q

Intellectual processes involved in analysis of qualitative data

A

Comprehending
Synthesizing
Theorizing
Recontextualizing

43
Q

Comprehending

A

The qualitative researcher attempts to make sense of the data.
Completed when data saturation is achieved.

44
Q

Synthesizing

A

The researcher sifts through the data and tries to put the pieces together.

45
Q

Recontextualizing

A

The theory is further developed and its applicability to other settings or groups is explored.

46
Q

Theorizing

A

Involves systematic sorting of the data.

Develops alternative explanations of the phenomenon under study to determine their fit with the data.

47
Q

Coding scheme

A

Method for organizing qualitative data.
Useful strategy for classifying and indexing qualitative data.
The researcher carefully reads through the data to identify underlying concepts and to identify codes.

48
Q

Coding qualitative data

A

Involves two simultaneous activities:

1) mechanical data reduction
2) analytic categorization of data into themes

49
Q

Steps involved in analysis of qualitative materials

A

1) A search for themes or recurring regularities is done.
2) The themes are validated to determine whether they accurately represent the phenomenon.
3) Researchers strive to put the thematic pieces together into an integrated whole.

50
Q

Ethnographic analysis

A

Generally begins at the time the researcher enters the field.
Continually looking for patterns in the behaviour and thoughts of the participants, comparing one pattern against another, and analyzing many patterns simultaneously.
Tools to analyze data include maps, flow charts, organizational charts, and other documents.

51
Q

Descriptive phenomenology

A

Researchers seek common patterns by identifying essential themes

52
Q

Van Manen’s Method of Phenomenological Analysis

A

Searches for themes using the following approaches:

1) Holistic approach
2) Selective approach
3) Detailed approach

53
Q

Holistic approach

A

Viewing the text as a whole to grasp its meanings

54
Q

Selective approach

A

Pulling out key statements and phrases that seem essential to the experience under study.

55
Q

Detailed approach

A

Analysing every sentence

56
Q

Grounded theory

A

Used to summarize results and key findings from the data in the form of conceptual maps or models.

57
Q

Constant comparative method

A

Data analysis of grounded theory.

Where the researcher simultaneously collects, codes, and analyzes data.

58
Q

Two types of coding in grounded theory

A

Substantive coding

Theoretical coding

59
Q

Types of substantive coding

A

Open coding

Selective coding

60
Q

Open coding

A

the researcher is trying to capture what is going on in the data

61
Q

Selective coding

A

The researcher codes only those variables that are related to the core variable

62
Q

Theoretical coding

A

Involves putting the broken pieces of data back together again

63
Q

T/F: The tendency for statistical values to differ from one sample to another is known as the standard error of the mean.

A

False

64
Q

T/F: A researcher never knows whether an error has been committed in statistical decision making

A

True

65
Q

T/F; Parametric tests make no assumptions about the shape of the distribution in the population

A

False

66
Q

T/F: Nonparametric tests have fewer assumptions than parametric tests

A

True

67
Q

T/F: A +0.50 correlation coefficient indicates a stronger relationship between two variables than a correlation of -0.75

A

False

68
Q

What are the three characteristics that can completely summarize a set of data?

A

Shape of the distribution
Central tendency
Variability

69
Q

T/F: One of the features of qualitative analysis is that a number of universal formal rules facilitate the process

A

False

70
Q

T/F: The process of recontextualization involves sifting the data and putting pieces together

A

False

71
Q

T/F: The grounded theory approach is applied to qualitative data after they have been gathered in the field

A

False

72
Q

T/F: The grounded theory analyst documents assumptions, insights, and the conceptual scheme on memos once all the data has been analyzed

A

False

73
Q

The level of measurement that classifies and ranks objects in terms of the degree to which they possess the attribute of interest.

A

Ordinal

74
Q

A record of fluid intake, in ounces, of a postsurgical patient is an example of which level of measurement?

A

Ratio

75
Q

Degrees such as an associate, bachelor’s, master’s, and doctorate correspond to measures on which of the following scales?

a) nominal
b) ordinal
c) interval
d) ratio

A

Ordinal

76
Q

A group of 100 students completed a test. The mean was 85, the standard deviation was 5, and the scores were normally distributed? About how many scores fell between 80 and 90?

A

68 - nooooo idea how to get this answer. Statistics suck.

77
Q

A parameter is a characteristic of:

a) a population
b) a frequency distribution
c) a sample
d) a normal curve

A

A population

78
Q

The measure of variability that takes into account all score values

A

Standard deviation

79
Q

Which measure of central tendency is the most stable?

A

The mean

80
Q

Which of the following is an example of a bivariate descriptive statistic?

a) frequency distribution
b) mean
c) range
d) correlation coefficient

A

Correlation coefficient

81
Q

This allows the researcher to draw conclusions about a population, based on information gathered from a sample

A

Inferential statistics

82
Q

A statistical procedure that is used to determine whether a significant difference exists between any number of group means on a dependent variable measured on an interval scale.

A

ANOVA

83
Q

The analysis style that is sometimes referred to as manifest content analysis

A

Quasi-statistical style

84
Q

In which of the following analysis styles does the researcher act as an interpreter who reads through data and develops a categorization scheme on the basis of meaningful segments?

a) quasi-statistical style
b) template analysis style
c) editing analysis style
d) immersion-crystallization style

A

Editing analysis style

85
Q

Quasi-statistics is essentially a method of:

a) statistical analysis
b) validation
c) thematic generation
d) analytic induction

A

Validation

86
Q

When does selective coding begin?

A

When a core variable has been identified.

NURS 341 - Nursing Research (4 decks)

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