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34 Cards in this Set
- Front
- Back
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Central Limit Theorem
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Data influenced by random effects are approximately normally distributed.
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Variance
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Indicates the amount of variability in a set of data. Averages of the squared deviations from the mean.
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Standard Deviation
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Square root of variance. Average amount of dispersion of the scores in a distribution. Small SD = scores not very spread out.
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Descriptive Statistics
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Provide measure of central tendency. (E.g. mean, median, range, variance, standard deviation.) Analysis of relationships among variables.
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Correlation Coefficient
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The larger the number the stronger the relationship. + and - indicate the direction of the relationship. Correlation does not equal causation!
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p-value
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A probability statement which reflects that probability that the effect you tested for occurred as a result of chance.
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Three elements of the product-moment correlation write-up
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1) A statement regarding the significance and direction (ie. positive/negative) of the relationship.
2) A report of the actual correlation coefficient (r) together with the degrees of freedom (n-1) and the significance level. 3) A description of the nature of the relationship for any significant correlations. |
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Example correlations write up.
n=15 Mean pure-tone average and word recognition scores: r=-0.94, p<.05 Age and pure-tone average: r=0.09, p>.05 Age and WRS: r=0.10, p>.05 |
There is a significant negative correlation between mean pure-tone average and word recognition scores (presented at 50 dB HL) (r(14) = -0.94, p<.05). Higher pure-tone averages were associated with lower thresholds. Correlations between age and pure-tone average (r(14) = 0.09, p>.05) and between age and word recognition scores (r(14) = 0.10, p>.05) were not significant.
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If you read a question with the word relationship or association you know you need to do which test?
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Correlation
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Which test would you use: Is there a difference between scores for Groups 1 and 2?
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Independent t-test
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Which test would you use: is there a difference between pre- and post-test scores for a single group of subjects?
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Dependent or "paired" t-test
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Independent t-test
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To determine whether there is a difference between means of two groups on a single condition or attribute.
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Dependent (paired) t-test
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To determine whether there is a difference between means of a single group on two levels of a dependent variable.
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Which test would you use: are the nine lists of the Modified Simultaneous Sentence Test equivalent in terms of difficulty?
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ANOVA
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Which test would you use: do people with cochlear dead regions benefit more from digital noise reduction than people without cochlear dead regions (each group tested with DNR and without DNR)?
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ANOVA
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ANOVA
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Can be used to determine if there are differences between one or more groups on one or more independent variables with 2 or more levels.
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Which test would you use: differences in thresholds at different frequencies for men and women.
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ANOVA
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Which test would you use: difference between recognition scores under three different listening conditions for a single group of subjects.
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ANOVA
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Which test would you use: can PTA predict speech recognition?
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Correlation
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Which test would you use: is mean hearing loss (PTA) associated with either of the speech recognition measures?
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Correlation
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A significance level of p<.01 for a correlation coefficient means that
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There is less than a 1/100 chance that the relationship observed occurred as a result of chance.
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What test would you use: do teenagers with a history of middle ear problems have higher pure-tone thresholds (dB HL) than children without middle ear problems?
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Independent t-test
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What test would you use: in a group of overweight adult diabetics, does blood glucose decrease after a six-week exercise program (compared to pre-test levels)?
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Dependent "paired" t-test
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What test would you use: do teenagers with a history of middle ear problems have higher thresholds at 500, 1000, and 2000 Hz (dB HL) than children without middle ear problems?
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ANOVA
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Type I error
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False positive. The null hypothesis is true but is incorrectly rejected. Conclude that the observations did not occur by chance, but they really did. The level of significance (p-level)!
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Type II error
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False negative. Null hypothesis is false, but is incorrectly accepted. Conclude there is no difference, but there really is. Often happens with under-powered designs.
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The probability of making a Type ___ error is the level of significance (p-level).
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Type I
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You concluded that there is no difference between WRS for people with mild hearing loss and people with profound hearing loss. There really is a difference. This is called a _____ error.
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Type II
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You concluded that the correlation between height and IQ did not occur by chance. IT really did occur by chance. This is called a _____ error.
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Type I
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What are you asking in hypothesis testing?
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"Could these observations really have occurred by chance?"
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Four steps of hypothesis testing
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1) Formulation of hypotheses: null hypothesis and alternate hypothesis.
2) Test statistic: identify a statistic that will assess the evidence against the null hypothesis. 3) Select p-value: basis for rejecting null. 4) Compare the measured p-value to the selected significance level (alpha). |
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Null Hypothesis
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These observations occurred by chance i.e. there is no real difference between groups/conditions or relationship among variables. Generally trying to prove this wrong.
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Alternate Hypothesis
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There is a real effect i.e. the observations are the results of this real effect plus chance variation.
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f value
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Systematic error/variance error
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