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Research > Week 8 - Inferential Stat Methods > Flashcards

Week 8 - Inferential Stat Methods Flashcards

1
Q

what is inferential stats?

A
  • based on the laws of probability
  • requires a random sample for best representativeness (gold standard is probability sampling, but is often widely violated, i.e. need something like NHANES or large funds)
  • cause/effect
  • compares groups
  • determines relationships among variables, etc.
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2
Q

t/f - Even if it’s a random sample, it’s rarely identical to the real population

A

true

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3
Q

what is sampling error?

A

tendency for statistics to fluctuate from one sample to another; the challenge is how to decide whether estimates are good population parameters?

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4
Q

what is sampling distribution of the mean?

A

a theoretical distribution of a test statistic (i.e., mean) from an infinite number of samples as data points.

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5
Q

what is a standard error of the mean (SEM)?

A

standard deviation (SD) of a sampling distribution of the mean (i.e. estimated from the sample’s SD and the sample size)

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6
Q

what happens in a normally distributed population…

A
  • 68 out of 100 of any randomly drawn sample means lies between +1 SD and -1 SD of the population mean.
  • Larger sample size ► we can increase the accuracy of our mean estimate
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7
Q

what are the two forms of statistical inference?

A
  • estimation of parameters (not as common in nursing research)
  • statistical hypothesis testing
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8
Q

what is an example of estimation of parameters (trying to estimate for the entire population)?

A
  • NCLEX pass rate for all individuals taking exam within a set time window
  • States w/laws requiring employers to offer paid sick leave for FT and PT staff nurses: Gaps in the Emergency Paid Sick Leave Law for Health Care Workers | KFF
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9
Q

what is an example of hypothesis testing (for a particular sample or group)?

A
  • Impact of investigational drug compared to placebo on BP

- Impact of nursing intervention to prevent burnout and turnover

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10
Q

what are the two forms of parameter estimation?

A
  • point estimation

- interval estimation

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11
Q

what is point estimate (as a form of parameter estimation)?

A

calculating a single statistic (i.e. sample mean) to estimate the population parameter (i.e. population mean)

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12
Q

what is interval estimation (as a form of parameter estimation)?

A

calculating a range of values the parameter (i.e. population mean) has a specified probability of being located

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13
Q

what is the confidence interval?

A

95-99%

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14
Q

what is confidence limits (CL)?

A

the range of values for the population and the probability of being right with a certain degree of confidence (i.e. 95% or 99%).

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15
Q

what are things to keep in mind about binomial distribution?

A
  • CIs are rarely symmetric like this picture
  • Width of CIs depends on sample size and proportion values
  • CIs for proportion values never extend <0 to >1, but can be constructed around proportions of 0 or 1
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16
Q

what are the typical CI proportions constructed on a binomial distribution?

A 
ARR = absolute risk reduction
RRR= relative risk reduction
OR = odds ratio
NNT = number needed to treat
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17
Q

what is hypothesis testing?

A

-Objective criteria for deciding if hypotheses are supported by data
-Based on rules of negative inference: Research hypotheses are supported if null hypotheses (H0) can be rejected
-Researchers want to accept or reject H0
H0 = there are no mean differences between groups
HA= there are mean differences between groups
-Uses statistical decision-making to either reject or accept H0

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18
Q

what is the simplest way to decrease type ll error?

A

increase the sample size

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19
Q

t/f - making decisions to decrease type l error decreases the risk of type ll error

A

false - making decisions to decrease type l error increases the risk of type ll error

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20
Q

what is type l error?

A
  • the H0 is rejected when it should not be (false positive)

- Risk of Type I Error is controlled by significance (α or p-value) of 0.05 or 0.01

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21
Q

what is type ll error?

A
  • accepting the H0 when it should be rejected (false negative)
  • Risk of Type II Error is controlled by setting power (1-β) at 80%
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22
Q

t/f - There is no “almost” significant at p=0.06

A

true

23
Q

researchers compute a test statistic with their data for what reason

A

determine whether the statistic falls beyond the critical region in the relevant theoretical distribution

24
Q

what is bivariate tests?

A

analysis of 2 variables to assess the empirical relationship between them

25
Q

what is a non-significant result?

A

means that the relationship is from chance fluctuations (accept H0)

26
Q

what is a significant result?

A

means that the H0 is very improbable and thus statistically significant (reject H0 and accept Ha)

27
Q

what is normal theoretical probability distributions?

A

Each test statistic has its own mathematic proportions for calculating critical value-limit scores falling within normal theoretical probability distributions.

28
Q

what are examples of normal theoretical probability distributions?

A
  • t Distribution for t-tests
  • F Distribution for ANOVA tests
  • χ2 Distribution
  • r Distribution
29
Q

what is a two-tailed test?

A

Hypothesis testing in which both ends of the sampling distribution are used to define the region of “improbable” values.

30
Q

what are the pros of two-tailed tests?

A

you do not need to know in advance the alternative hypothesis

31
Q

what are the cons of two-tailed tests?

A

you may not be able to detect small differences

32
Q

what is a one-tailed test?

A

Critical region of improbable values is entirely in 1 tail of the distribution—corresponding to the direction of the hypothesis set by the researcher

33
Q

what is a pro of one-tailed test?

A

offers you more power to detect small differences

34
Q

what are the cons of one-tailed test?

A

must guess the right direction in advance; even if a large effect, if it is in the wrong direction, it must be written off due to chance (thus a less conservative approach)

35
Q

what does the sampling distribution look like for two-tailed tests?

A

5% of the sampling distribution is equally split b/t 2 tails: 2.5% on each side

36
Q

what is the sampling distribution of a one-tailed test?

A

5% of the sampling distribution is on 1 side

37
Q

what are parametric stats?

A
  • Involves the estimation of a parameter
  • Requires measurements on an interval/ratio scale
  • Involves several assumptions (i.e. variable w/normal distribution)
  • pearson’s r
  • preferable
38
Q

what are non-parametric stats?

A
  • Does not estimate parameters, uses a rank ordering procedure
  • Uses variables/data on a nominal or ordinal scale
  • Do not involve a distribution (distribution-free statistics)
  • Has less restrictive assumptions about the shape of the variables’ distribution than parametric tests (i.e. multiple peaks, outliers, abnormal distribution)
  • spearman’s rho
  • small sample size
39
Q

what is a bonferroni correction?

A
  • designate a more conservative α threshold = p-value/# of total tests; 0.05/6 = 0.0083
  • reduces type l error
40
Q

what falls under the category of parametric (interval or ratio)?

A
  • Student’s Independent t-test
  • Paired (Dependent) t-test
  • ANOVA (1,2 way ANOVA), (DV for 3 groups on 1 or 2 IV)
  • Repeated Measure ANOVA (3 Groups over different time pts)
  • Pearson-Product Moment (pearson’s r)
41
Q

what falls under non-parametric (highly skewed or non-interval, non-ratio) tests?

A
  • Mann-Whitney U test
  • Wilcoxon signed ranks test
  • Kruskal Wallis
  • Friedman Test
  • Spearman Rank Correlation (phi coefficient)
42
Q

what is a chi-square test?

A
  • A statistical test used to determine if group differences in a cross-tabs (or proportions of categories in 2 group variables) differ from one another
  • Computed by summarizing differences b/t observed vs expected frequencies for each cell
  • Assumes a random sample, independence, and mutually exclusive groups
43
Q

what is pearson’s r (correlation)?

A
  • a correlation coefficient; designates the magnitude of relationship (strength and direction) between two variables measured on at least an interval scale; can be used between group and within group situations
  • Interpreting r Values: -1 (Negative correlation) to 1 (Positive Correlation); 0 = No correlation
  • Both descriptive and inferential
44
Q

what is descriptive correlation?

A

summarizes the magnitude of relationship b/t 2 variables

45
Q

what is inferential correlation?

A

-r is used to test population correlations, rho (ρ)
H0 : ρ = 0
HA : ρ ≠ 0

46
Q

what is a t-test?

A

-Testing for differences between 2-group means
-Can be used when there are two independent groups (i.e. experimental versus control; or pre- and post-scores for a group of the same people) OR paired (2 measurements from the same person over different time pts or paired participants together)
H0 : µA = µB H0 : µX1 = µX2
HA : µA ≠ µB HA : µX1 ≠ µX2

47
Q

what test do you use when testing the mean differences with 3 or more groups?

A

analysis of variance (ANOVA)

48
Q

what is a one-way ANOVA?

A

sum of squares between groups and within groups for 3+ groups on 1 IV (Example: Burnout survey scores across different patient care units)

49
Q

what is a two-way ANOVA?

A

3+ groups on 2 IV; main effects versus interaction effects (see Prof KTs thesis example = SNP genotype by race)

50
Q

what is a repeated measures ANOVA?

A

3+ groups over repeated times
F-ratio statistic: are omnibus tests—they only tell you a difference exists, not where the difference is located:
H0 : µA = µB = µC
HA : µA ≠ µB ≠ µC

51
Q

t/f - If significant p-values are obtained, post-hoc tests (multiple comparison procedures) are used to examine where group differences are occurring

A

true

52
Q

A researcher hypothesized that there would be no difference in respiratory rates for patients who self-administered respiratory treatments compared to respiratory treatments administered by a health care provider. Which would be the most appropriate statistical test?

Paired (dependent) t test
Pearson’s r 
Mann Whitney U
Independent (Student’s) t test
None of the above
A

Independent (Student’s) t test
RATIONALE: Dependent variable is respiratory rates, which is interval level data and 2 groups are being compared one time only.

53
Q

Capillary blood glucose levels were studied in four groups of patients with diabetes who had varying social support systems. Fasting glucose levels were obtained for each group member using a calibrated glucose meter. Subsequently, the mean glucose levels for the groups were compared.

	Student’s (Independent) t test
	Paired (Dependent) t test
	Kruskal Wallis
	Pearson’s r
	ANOVA
A

ANOVA
RATIONALE: Dependent variable is glucose level, which is interval level data and 4 groups are being compared or measured one time only.

54
Q

An investigation was undertaken to determine if there was a relationship between the quantity of coronary artery blockage and cholesterol levels in obese Caucasian males undergoing cardiac catheterization during January of 2013. The results indicate a strong positive correlation, r = .88, p = .001. What statistical test would you suspect was used to analyze these data?

a. Spearman Rho correlation test
b. Independent t test, also called the Student’s t test
c. Pearson’s Correlation
d. Mann-Whitney U test
e. Repeated measure ANOVA

A

c. Pearson’s Correlation

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