1. Week Four

3. Basic decision is use of:  New data, collected specifically for research purposes, or  Existing data ◦ Records (e.g., patient charts) ◦ Historical data ◦ Existing data set (secondary analysis)

4.  Hospital records (e.g., nurses’ shift reports)  School records (e.g., student absenteeism)  Corporate records (e.g., health insurance choices)  Letters, diaries, minutes of meetings, etc.  Photographs

5.  Self-reports  Observation  Biophysiologic measures

6.  Structure  Quantifiability  Researcher obtrusiveness  Objectivity

7.  Data are collected with a formal instrument. ◦ Interview schedule  Questions are prespecified but asked orally.  Either face-to-face or by telephone ◦ Questionnaire  Questions prespecified in written form, to be self-administered by respondents

8.  Closed-ended (fixed alternative) questions ◦ e.g., “Within the past 6 months, were you ever a member of a fitness center or gym?” (yes/no)  Open-ended questions ◦ e.g., “Why did you decide to join a fitness center or gym?”

9.  Dichotomous questions  Multiple-choice questions  Cafeteria questions  Rank-order questions  Forced-choice questions  Rating questions

10.  Lower costs  Possibility of anonymity, greater privacy  Lack of interviewer bias

11.  Higher response rates  Appropriate for more diverse audiences  Opportunities to clarify questions or to determine comprehension  Opportunity to collect supplementary data through observation

12.  Scales—used to make fine quantitative discriminations among people with different attitudes, perceptions, traits  Likert scales—summated rating scales  Semantic differential scales

13.  Consist of several declarative statements (items) expressing viewpoints  Responses are on an agree/disagree continuum (usually 5 or 7 response options).  Responses to items are summed to compute a total scale score.

14.  Require ratings of various concepts  Rating scales involve bipolar adjective pairs, with 7-point ratings.  Ratings for each dimension are summed to compute a total score for each concept.

16.  Used to measure subjective experiences (e.g., pain, nausea)  Measurements are on a straight line measuring 100 mm  End points labeled as extreme limits of sensation

18.  Biases reflecting the tendency of some people to respond to items in characteristic ways, independently of item content  Examples: ◦ Social desirability response set bias ◦ Extreme response set ◦ Acquiescence response set (yea- sayers) ◦ Nay-sayers response set

19.  Participants sort a deck of cards into piles according to specific criteria.  Cards contain statements to be sorted on a bipolar continuum (e.g., most like me/least like me).  Usually 50 to 100 cards; usually 9 or 11 piles

20.  Brief descriptions of situations to which respondents are asked to react  Descriptions are usually written “stories.”  Respondents can be asked open-ended or closed-ended questions about their reactions.  Aspects of the vignettes can be experimentally manipulated.

21.  Strong on directness  Allows access to information otherwise not available to researchers  But can we be sure participants actually feel or act the way they say they do?

22.  Activities and behavior  Characteristics and conditions of individuals  Skill attainment and performance  Verbal and nonverbal communication  Environmental characteristics

23.  Time-sampling—sampling of time intervals for observation Examples:  Random sampling of intervals of a given length  Systematic sampling of intervals of a given length  Event sampling—observation of integral events

24.  Excellent method for capturing many clinical phenomena and behaviors  Potential problem of reactivity when people are aware that they are being observed  Risk of observational biases—factors that can interfere with objective observation

25.  In vivo measurements ◦ Performed directly within or on living organisms (e.g., blood pressure measures)  In vitro measurements ◦ Performed outside the organism’s body (e.g., urinalysis)

26.  Strong on accuracy, objectivity, validity, and precision  May be cost-effective for nurse researchers  But caution may be required for their use, and advanced skills may be needed for interpretation.

28.  The assignment of numbers to represent the amount of an attribute present in an object or person, using specific rules  Advantages: ◦ Removes guesswork ◦ Provides precise information ◦ Less vague than words

29.  There are four levels (classes) of measurement: ◦ Nominal (assigning numbers to classify characteristics into categories) ◦ Ordinal (ranking objects based on their relative standing on an attribute) ◦ Interval (objects ordered on a scale that has equal distances between points on the scale) ◦ Ratio (equal distances between score units; there is a rational, meaningful zero)  A variable’s level of measurement determines what mathematic operations can be performed in a statistical analysis.

30.  Obtained Score = True score ± Error ◦ Obtained score: An actual data value for a participant (e.g., anxiety scale score) ◦ True score: The score that would be obtained with an infallible measure ◦ Error: The error of measurement, caused by factors that distort measurement

31.  Situational contaminants  Transitory personal factors (e.g., fatigue)  Response-set biases  Administration variations  Item sampling

32.  A psychometric assessment is an evaluation of the quality of a measuring instrument.  Key criteria in a psychometric assessment: ◦ Reliability ◦ Validity

33.  The consistency and accuracy with which an instrument measures the target attribute  Reliability assessments involve computing a reliability coefficient. ◦ Reliability coefficients can range from.00 to 1.00. ◦ Coefficients below.70 are considered unsatisfactory. ◦ Coefficients of.80 or higher are desirable.

34.  Stability  Internal consistency  Equivalence

35.  The extent to which scores are similar on two separate administrations of an instrument  Evaluated by test–retest reliability ◦ Requires participants to complete the same instrument on two occasions ◦ Appropriate for relatively enduring attributes (e.g., creativity)

36.  The extent to which all the items on an instrument are measuring the same unitary attribute  Evaluated by administering instrument on one occasion  Appropriate for most multi-item instruments  The most widely used approach to assessing reliability  Assessed by computing coefficient alpha (Cronbach’s alpha)  Alphas ≥.80 are highly desirable.

37.  Low reliability can undermine adequate testing of hypotheses.  Reliability estimates vary depending on procedure used to obtain them.  Reliability is lower in homogeneous than heterogeneous samples.  Reliability is lower in shorter than longer multi-item scales.

38.  The degree to which an instrument measures what it is supposed to measure  Four aspects of validity: ◦ Face validity ◦ Content validity ◦ Criterion-related validity ◦ Construct validity

39.  Refers to whether the instrument looks as though it is an appropriate measure of the construct  Based on judgment; no objective criteria for assessment

40.  The degree to which an instrument has an adequate sample of items for the construct being measured  Evaluated by expert evaluation, often via a quantitative measure—the content validity index (CVI)

41.  The degree to which the instrument is related to an external criterion  Validity coefficient is calculated by analyzing the relationship between scores on the instrument and the criterion.  Two types:  Predictive validity: the instrument’s ability to distinguish people whose performance differs on a future criterion  Concurrent validity: the instrument’s ability to distinguish individuals who differ on a present criterion

42.  Concerned with these questions: ◦ What is this instrument really measuring? ◦ Does it adequately measure the construct of interest?

43.  Known-groups technique  Testing relationships based on theoretical predictions  Factor analysis

45.  Descriptive statistics ◦ Used to describe and synthesize data  Inferential statistics ◦ Used to make inferences about the population based on sample data

46.  Parameter ◦ A descriptor for a population (e.g., the average age of menses for Canadian females)  Statistic ◦ A descriptor for a population (e.g., the average age of menses for female students at McGill University)

47.  A systematic arrangement of numeric values on a variable from lowest to highest, and a count of the number of times (and/or percentage) each value was obtained  Frequency distributions can be described in terms of: ◦ Shape ◦ Central tendency ◦ Variability  Can be presented in a table (Ns and percentages) or graphically (e.g., frequency polygons)

48.  Symmetry ◦ Symmetric ◦ Skewed (asymmetric)  Positive skew (long tail points to the right)  Negative skew (long tail points to the left)

51.  Peakedness (how sharp the peak is)  Modality (number of peaks) ◦ Unimodal (1 peak) ◦ Bimodal (2 peaks) ◦ Multimodal (2+ peaks)

52.  Characteristics: ◦ Symmetric ◦ Unimodal ◦ Not too peaked, not too flat  More popularly referred to as a bell-shaped curve  Important distribution in inferential statistics

53.  Index of “typicalness” of a set of scores that comes from center of the distribution  Mode—the most frequently occurring score in a distribution ◦ Ex: 2, 3, 3, 3, 4, 5, 6, 7, 8, 9 Mode = 3  Median—the point in a distribution above which and below which 50% of cases fall ◦ Ex: 2, 3, 3, 3, 4 | 5, 6, 7, 8, 9 Median = 4.5  Mean—equals the sum of all scores divided by the total number of scores ◦ Ex: 2, 3, 3, 3, 4, 5, 6, 7, 8, 9 Mean = 5.0

54.  Mode, useful mainly as gross descriptor, especially of nominal measures  Median, useful mainly as descriptor of typical value when distribution is skewed (e.g., household income)  Mean, most stable and widely used indicator of central tendency

55. The degree to which scores in a distribution are spread out or dispersed  Homogeneity—little variability  Heterogeneity—great variability

57.  Range: highest value minus lowest value  Standard deviation (SD): average deviation of scores in a distribution

59.  Used for describing the relationship between two variables  Two common approaches: ◦ Contingency tables (Crosstabs) ◦ Correlation coefficients

60.  A two-dimensional frequency distribution; frequencies of two variables are cross- tabulated  “Cells” at intersection of rows and columns display counts and percentages  Variables usually nominal or ordinal

61.  Indicate direction and magnitude of relationship between two variables  The most widely used correlation coefficient is Pearson’s r.  Pearson’s r is used when both variables are interval- or ratio-level measures.

62.  Correlation coefficients can range from -1.00 to +1.00 ◦ Negative relationship (0.00 to -1.00) —one variable increases in value as the other decreases, e.g., amount of exercise and weight ◦ Positive relationship (0.00 to +1.00) —both variables increase, e.g., calorie consumption and weight

63.  The greater the absolute value of the coefficient, the stronger the relationship: Ex: r = -.45 is stronger than r = +.40  With multiple variables, a correlation matrix can be displayed to show all pairs of correlations.

64.  Used to make objective decisions about population parameters using sample data  Based on laws of probability  Uses the concept of theoretical distributions ◦ e.g., the sampling distribution of the mean

65.  A theoretical distribution of means for an infinite number of samples drawn from the same population  Is always normally distributed  Its mean equals the population mean.  Its standard deviation is called the standard error of the mean (SEM).  SEM is estimated from a sample SD and the sample size.

66.  Parameter estimation  Hypothesis testing (more common among nurse researchers than among medical researchers)

67.  CIs indicate the upper and lower confidence limits and the probability that the population value is between those limits. ◦ For example, a 95% CI of 40–50 for a sample mean of 45 indicates there is a 95% probability that the population mean is between 40 and 50.

68.  Based on rules of negative inference: research hypotheses are supported if null hypotheses can be rejected.  Involves statistical decision-making to either: ◦ accept the null hypothesis or ◦ reject the null hypothesis  Researchers compute a test statistic with their data and then determine whether the statistic falls beyond the critical region in the relevant theoretical distribution. ◦ Values beyond the critical region indicate that the null hypothesis is improbable, at a specified probability level.

69.  If the value of the test statistic indicates that the null hypothesis is improbable, then the result is statistically significant.  A nonsignificant result means that any observed difference or relationship could have happened by chance.  Statistical decisions are either correct or incorrect.

70.  Type I error: rejection of a null hypothesis when it should not be rejected; a false-positive result ◦ Risk of error is controlled by the level of significance (alpha), e.g.,  =.05 or.01.  Type II error: failure to reject a null hypothesis when it should be rejected; a false-negative result ◦ The risk of this error is beta (β). ◦ Power is the ability of a test to detect true relationships; power = 1 – β. ◦ By convention, power should be at least.80. ◦ Larger samples = greater power

72.  Parametric Statistics: ◦ Use involves estimation of a parameter; assumes variables are normally distributed in the population; measurements are on interval/ratio scale  Nonparametric Statistics: ◦ Use does not involve estimation of a parameter; measurements typically on nominal or ordinal scale; doesn’t assume normal distribution in the population

73.  Select an appropriate test statistic.  Establish significance criterion (e.g.,  =.05).  Compute test statistic with actual data.  Calculate degrees of freedom (df) for the test statistic.  Obtain a critical value for the statistical test (e.g., from a table).  Compare the computed test statistic to the tabled value.  Make decision to accept or reject null hypothesis.

74.  t-Test  Analysis of variance (ANOVA)  Pearson’s r  Chi-squared test

75.  Tests the difference between two means  t-test for independent groups: between- subjects test ◦ e.g., means for men vs. women  t-test for dependent (paired) groups: within- subjects test ◦ e.g., means for patients before and after surgery

76. Tests the difference between more than 2 means ◦ One-way ANOVA (e.g., 3 groups) ◦ Multifactor (e.g., two-way) ANOVA ◦ Repeated measures ANOVA (RM-ANOVA): within subjects

77.  Tests the difference in proportions in categories within a contingency table  Compares observed frequencies in each cell with expected frequencies—the frequencies expected if there was no relationship

78.  Pearson’s r is both a descriptive and an inferential statistic.  Tests that the relationship between two variables is not zero.

79.  Effect size is an important concept in power analysis.  Effect size indexes summarize the magnitude of the effect of the independent variable on the dependent variable.  In a comparison of two group means (i.e., in a t-test situation), the effect size index is d.  By convention: d ≤.20, small effect d =.50, moderate effect d ≥.80, large effect

80.  Statistical procedures for analyzing relationships among 3 or more variables  Two commonly used procedures in nursing research: ◦ Multiple regression ◦ Analysis of covariance (ANCOVA)

81.  The correlation index for a dependent variable and 2+ independent (predictor) variables: R  Does not have negative values: shows strength of relationships, not direction  R 2 is an estimate of the proportion of variability in the dependent variable accounted for by all predictors.

82.  Extends ANOVA by removing the effect of confounding variables (covariates) before testing whether mean group differences are statistically significant  Levels of measurement of variables: ◦ Dependent variable is continuous—ratio or interval level ◦ Independent variable is nominal (group status) ◦ Covariates are continuous or dichotomous

83.  Used to reduce a large set of variables into a smaller set of underlying dimensions (factors)  Used primarily in developing scales and complex instruments

84.  The extension of ANOVA to more than one dependent variable  Abbreviated as MANOVA  Can be used with covariates: Multivariate analysis of covariance (MANCOVA)