Flashcards
Cramming Sam's Tips
Cards in this deck (32)
- 1The Standard ErrorThe standard error is the standard deviation of sample means. As such, it is a measure of how representative a sample is likely to be of be of the population. A large standard error (relative to the sample mean) means that there is a lot of variability between the means of different samples and so the sample we have might not be representative of the population. A small standard error indicates that most sample means are similar to the population mean and so our sample is likely to be an accurate reflection of the population.
- 2Confidence Intervals- A confidence interval for the mean is a range of scores constructed such that the population mean will fall within this range in 95% of samples.
- The confidence interval is not an interval within which we are 95% confident that the population mean will fall. - 3Null Hypothesis Significance Testing1. NHST is a widespread method for assessing scientific theories. The basic idea is that we have two competing hypotheses: one says that an effect exists (the alternative hypothesis) and the other says that an effect does not exist (the null hypothesis). We compute a test statistic that represents the alternative hypothesis and calculate the probability that we would get a value as big as the one we have if the null hypothesis were true. If this probability is less than 0.05 we reject the idea that there is no effect, say that we have statistically significant finding and throw a little party. If the probability is greater than 0.05 we do not reject the idea that there is no effect, we say that we have a non-significant finding and we look sad.
- 4Null Hypothesis Significance TestingWe can make two types of error: we can believe that there is an effect when, in reality, there is (a Type II error).
- 5Null Hypothesis Significance TestingThe power of a statistical test is the probability that it will find an effect when one actually exists.
- 6Null Hypothesis Significance TestingThe significance of a test statistic is directly linked to the sample size: the same effect will have different p-values in different sized samples: small differences can be deemed 'significant' in large samples and large effects might be deemed 'non-significant' in small samples.
- 7Effect SizesAn effect size is a way of measuring the size of an observed effect, usually relative to the background error.
- 8Effect SizesCohen's d is the difference between two means divided by the standard deviation of the mean of the control group, or a pooled estimated based on the standard deviations of both groups.
- 9Effect SizesPearson's correlation coefficient, r, is also a versatile effect size measure.
- 10Graphs- The vertical axis of a graph is known as the y-axis (or ordinate) of the graph.
- The horizontal axis of a graph is known as the x-axis (or abscissa) of the graph. - 11Skewness and Kurtosis- To check that the distribution of scores is approximately normal, we need to look at the values of skewness and kurtosis in the input.
- 12Skewness and Kurtosis- Positive values of skewness indicate too many low scores in the distribution, whereas negative values indicate a build-up of high scores.
- 13Skewness and Kurtosis- Positive values of kurtosis indicate a pointy and heavy-tailed distribution, whereas negative values indicate a flat and light tailed distribution.
- 14Skewness and Kurtosis- The further the value is from zero, the more likely it is that the data are not normally distributed.
- 15Skewness and Kurtosis- You can convert these scores to z-scores by dividing by their standard error. If the resulting score (when you ignore the minus sign) is greater than 1.96 then it is significant (p<0.05).
- 16Skewness and Kurtosis- Significance tests of skew and kurtosis should not be used in large samples (because they are likely to be significant even when skew and kurtosis are not too different from normal).
- 17Normality Tests- The K-S test can be used to see if a distribution of scores significantly differs from a normal distribution.
- 18Normality TestsIf the K-S test is significant (Sig. in the SPSS table is less than 0.05) then the scores are significantly different from a normal distribution.
- 19Normality TestsThe Shapiro-Wilk test does much the same thing, but it has more power to detect differences from normality (so this test might be significant when the K-S test is not).
- 20Normality TestsWarning: In large samples these tests can be significant even when the scores are only slightly different from a normal distribution. Therefore, I don't particularly recommend them and they should always be interpreted in conjunction with histograms, P-P or Q-Q plots, and the values of skew and kurtosis.
- 21Homogeneity of VarianceHomogeneity of variance/homoscedasticity is the assumption that the spread of the outcome scores is roughly equal at different points on the predictor variable.
- 22Homogeneity of VarianceThis can be tested by looking at a plot of the standardized predicted values from your model against the standardized residuals (zpred vs zresid).
- 23Homogeneity of VarianceWhen comparing groups, this assumption can be tested with Levene's test and variance ratio (Hartley's F)
- If Levene's test is significant (Sig in the SPSS table is less than 0.05) then the variances are significantly different in difference groups.
- Otherwise, homogeneity of variance can be assumed.
- The variance ratio is the largest group variance divided by the smallest. This value needs to be smaller than the critical values in additional material. - 24Homogeneity of VarianceWarning: There are good reasons not to use tests like Leven's test. In large samples Leven's test can be significant even when group variances are not very different. Therefore, it should be interpreted in conjuntion witht he variance ratio.
- 25Correlation- A crude measure of the relationship between variables is the covariance.
- If we standardize this value we get Pearson's correlation coefficient. - 26Correlation- The correlation coefficient has to be between -1 and +1.
- A coefficient of +1 indicates a perfect positive relationship, a coefficient of -1 indicates a perfect negative relationship and a coefficient of 0 indicates no linear relationship at all. - 27Correlation- The correlation coefficient is a commonly used measure of the size of an effect: values of +/-.1 represent a small effect +/-.3 is a medium effect and +/-.5 is a large effect. However, if you can try to interpret the size of correlation within the context of the research you've done rather than blindly following these benchmarks.
- 28Correlations- We can measure the relationship between two variables using correlation coefficients.
- These coefficients lie between -1 and +1.
- Pearson's correlation coefficient, r, is a parametric statistic and requires interval data for both variables. To test its significance we assume normality too. - 29Correlations- Spearman's correlation coefficient r, is a non-parametric statistic and requires only ordinal data for both variables.
- Kendall's correlation coefficient t, is like Spearman's r, but probably better for small samples. - 30Correlations- The point-biserial correlation coefficient, r pb, quantifies the relationship between a continuous variable and a variable that is a discrete dichotomy (eg. there is no continuum underlying the two categories, such as dead or alive)
- 31Correlations- The biserial correlation coefficient, r b, quantifies the relationship between a continuous variable and a variable that is a continuous dichotomy (eg. there is a continuum underlying the two categories, such as passing or failing an exam).
- 32Partial and Semi Partial Correlations- A partial correlation quantifies the relationship between two variables while accounting for the effect of a third variable on both variables.
- A semi-partial correlation quantifies the relationship between two variables while accounting for the effects of a third variable on only one of the variables in the original correlation.
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- Bachelor Psychological Science ACAP 1012 Flashcards on Cramming Sam's Tips, created by Melanie Yandell on 11/05/2014.
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