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Flashcards in Comm Health Final Deck (77):
1

The Null Hypothesis (Ho or sometimes NH)

null hypothesis refers to a general or default position: that there is no relationship between two measured phenomena, or that a potential medical treatment has no effect.

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Correlation coefficient

a measure of the linear correlation between two variables X and Y

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Value of correlation coefficient

between -1 and 1; closer to -1 or 1 is a stronger; 0 is no correlation

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Positive correlation

graph with positive slope; as one variable increases so does the other or as one decreases so does the other - they both go the same way

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Negative correlation

graph with negative slope; as one variable increases the other decreases or as one decreases the other increases - they go opposite directions

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Strength of association

Value of r, can be -r or r; 0-0.25 is little if any association; 0.26-0.49 is weak; 0.7-0.89 is high; 0.9-1 is very high

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p value

usually set at 0.05; probability that findings are due to chance; this is to test the hypothesis

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Hypothesis testing

a statistical decision to reject or accept the null hypothesis based on probability (p) that is at a set significance level

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Statistically significant

a value of p less than or equal to the set significant level means the results are statistically significant and you reject the null hypothesis

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Not statistically significant

a value of p greater than the set significant level means the results are not statistically significant and you do not reject the null hypothesis

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Type 1 statistic error

researcher rejects null hypothesis and concludes that a statistically significant difference exists when in fact no true difference is present

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Type 2 statistic error

researcher concludes that no statistically significant difference exists and accepts the null hypothesis when in fact a significant difference does exist

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Causes: type 1 error

large sample size; p value set too high (0.05 or greater); corrected by: randomly selected sample and good study design and set p value lower than 0.05

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Causes: type 2 error

too small a sample size; unrealistic measuring devices; imprecise research methods; corrected by: correction of causes

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t-test used in inferential statistics

statistical measure used when comparing hypothetical difference between TWO mean scores; NH for these tests - two unrelated groups are equal; looking to see if we can show that we can reject NH and accept alternative hypothesis - two NOT equal

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ANOVA test used in inferential statistics

statistical measure used when comparing hypothetical difference between THREE OR MORE mean scores

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Chi-Square test used in inferential statistics

*attitudes - non-numbers; used to analyze discrete, nominally scaled data, and to test differences between frequency distributions; test independence of two categorical (descriptive) variables; helps one arrive at a p value

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Standard of acceptability

p < 0.05; 1 out of 20 occurred by chance or it has nothing to do with the testing situation

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Validity

degree that a study or procedure measures what it claims to be measuring; is it measuring how it claims it should?

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Sensitivity

the ability of a test to correctly identify the presence of a disease

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Specificity

the ability of a test to identify the absence of a disease

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Reliability

the extent to which the method of measurement consistently performs

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Null hypothesis

a question or statement to be answered that can be stated in a negative outcome; no benefit or significance

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research question or hypothesis

a question or statement to be answered; stated negatively or positively

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Positive hypothesis

Brand X significantly whitens teeth

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Negative (Null) hypothesis

No statistically significant difference exists between brand X and placebo; "NO SIGNIFICANT DIFFERENCE"

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Dependent variable

the outcome of interest; change in it should be observed in response to some intervention

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Independent variable

the intervention; what is being manipulated to change the outcome of interest

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Extraneous variables

not related to the purpose of the study BUT may influence the outcome; interfere with accurate interpretation and produce invalid research results

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Placebo

should have no positive or negative effects on subjects; should seem identical in every way to the real thing

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Placebo effect

just knowing the desired effect produces it

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Calibrated examiners

should be educated the same on how to perform; unification of what they do - do things the same

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Intra-rater reliability

in one person; the one person is consistent in performance/ does the same all the time

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Inter-rater reliability

between more than one person; they all have consistent performance/ do the same all the time

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Hawthorne effect

they KNOW they are being watched and their performance is affected because of it - better

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Examiner bias

they have a reason for which way the experiment goes; prefer a certain side and make sure their side comes out positively; examining their own or being paid by a biased company

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Lack of control group

won't have comparisons; less valid

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Single blind study

subject does not know but examiner does know what is being tested; sometimes knowing examiners accidentally persuade results

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Double blind study

both subject and examiner do not know what is being tested; *BEST outcome

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Sample

portion of the population that, if properly selected, can provide meaningful information about the entire population

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Simple random sample

gives every member of the population an equal chance of being selected; some mechanism of chance to choose them; no one is favored over another

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Systematic sample

every nth individual participates; ex: if you have 1000 and want 100 of that, every 10th person

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Stratified random sample

random sample carried further into sub groups; some from each group; if you want to be sure to cover such things as age, gender, income level, education levels, etc.

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Biased sample

can lead to misleading results; sample chosen ensures results consistent with desired outcome

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Judgement sample (Purposive)

choosing with a purpose for choosing certain groups; someone who knows the population chooses a sample to represent the population; risk of bias

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Convenience sample

at convenience of researcher; simplest method; little concern for representativeness; may not be applicable to general population

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Sampling error

occurs when a sample measurement is different from the population measurement; selecting sample not perfectly matched to represent entire population; can lead to inaccurate conclusions about the population

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General sampling rules

30 subjects; study done for at least 6 months; should be of a meaningful population

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Statistics

the science that deals with the collection, tabulation, and systematic classification of data

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Types of data

Qualitative (categorical); Quantitative (continuous)

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Qualitative

uses descriptive terms to measure or classify something; nominal, dichotomous, ordinal

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Quantitative

uses *numerical values to describe something; interval, ratio

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Nominal

variables with a name, that have no particular order; ie: puppy types, blood types, eye colors

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Dichotomous

variables that only have TWO categories or levels; ie: gender: female or male, state of living: dead or alive

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Ordinal

variables whose categories can be ordered or ranked, but the spacing between values may not be the same across the levels of variables; ordered but not proportionally ratioed; ie: education level (elementary, freshman, sophomore, junior, etc), pain level, cancer stages, *decay classifications (1-6)

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Interval

points are equally spaced along the scale and the difference between the two points is meaningful (as opposed to ordinal scales); ie: temperature, IQ, pain scale 1-10, body length of infant; NO meaningful zero point

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Ratio

ratios between points has meaning; Zero does count, can use "twice as much" rule; ie: age, weight, height, time, BP, distance, pulse, etc

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Graphs

to express data; bar, histogram

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Bar Graph

used to present categorical variables, bar for each category with spaces between to represent discrete nature of data

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Histogram

similar to bar graph, but bars appear side by side (touching)

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Measures of central tendency

single value to describe a set of data by identifying the central position within that set of data; mean, median, mode

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Mean

arithmetic average of scores; most common measure of central tendency; particularly susceptible to extreme values; most useful and most familiar; always center of balance of distribution in a symmetrical distribution

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Median

point of distribution with 50% of scores falling above it and 50% falling below it; NOT affected by extreme values; Midpoint: when total number is odd, median is midpoint; when total number is eve, take two middle scores and average

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Mode

Most frequent score in a distribution; affects skew of graph, you CAN have 2 modes (bimodal), if everything is equal you have NO mode; least used of the measures of central tendency

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Type of variable: Nominal

Best measure of central tendency: Mode

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Type of variable: Ordinal

Best measure of central tendency: Median

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Type of variable: Interval/Ratio (not skewed)

Best measure of central tendency: Mean

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Type of variable: Interval/Ratio (skewed)

Best measure of central tendency: Median

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Measures of dispersion (spread)

Range; Variance; Standard of Deviation; used to describe how much variation is present in a sample

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Range

difference between the highest and lowest scores in a data set; simplest measure of a spread; Range = Maximum value - Minimum value; ie: 95K-12K=83K; smaller (narrow) range is better because a large range means the mean is not as representative of the data

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Variance

represents the average distance of each score from the mean

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Standard deviation

the square root of the variance; a measure of the spread of scores within a set of data; appropriate when data isn't skewed or has outliers;

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Bell curve

most of the time standard deviation works within a normal bell curve distribution

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Positive (RIGHT) skew

outliers create positive skew when most scores are lower but one or two are higher -------->

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Negative (LEFT) skew

outliers create negative skew when most scores are higher but one or two are lower <-------

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Skewed distribution

distribution of scores is NOT symmetrical

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Normal distribution

bell curve; 68-95-99.7 rule; the majority of the scores always falls within +1 or -1. That is a given. It will always be true regardless of the value of the standard deviation