Question | Answer |

Measures of central tendency | mean, median, mode |

Mean | average of all scores |

Median | midpoint of all scores |

Mode | most frequently occurring score |

mean is appropriate for what data types? | interval, ratio |

median is appropriate for what data types? | ordinal data |

mode is appropriate for what data types? | nominal |

Measures of variability | range, standard deviation, normal distribution, percentiles & quartiles |

Range | difference between highest and lowest score |

Standard Deviation | variability of scores from the mean. most frequently used |

How to calculate SD | subtract each score from mean, square each difference, add up all squares, divide by number of scores |

Normal distribution | symmetrical bell shaped curve indiecating distribution of scores. Mean/median/mode all similar. |

Inferential statistics | allow determination of how likely results can be generalized to a population |

Standard error of measurement | an estimate of expected errors in a score, measure of response stability or reliability |

Tests of significance | estimation of true differences not due to chance, rejection of null hypothesis |

Alpha level | probability level - reselected level of statistical significance. Most commonly .05 or .01 (.05= only 5x out of 100 is the difference due to chance) |

Degrees of freedom | based on # of subjects and groups, allows determination of level of significance |

Standard error | result of sampling error, expected chance variation among the means |

Type 1 error | Null hypothesis rejected when it is true. |

Type 2 error | Null hypothesis is not rejected when it is false. means concluded to be due to chance when truly different |

How to decrease type 1/2 errors | increase sample size, random selection, valid measures |

Parametric statistics | Interval or Ratio data |

Assumptions for parametric statistics | normal distribution (usu large representative samples this is met), random sampling performed, variance in groups is equal |

T-test | parametric test of significance used to compare 2 independent groups created by random assignment and ID difference at a selected probability level |

T-test for independent samples | compares 2 independent groups |

T-test for paired samples | compares 2 matched samples (does therapy incr fxn in siblings with autism) |

One-tailed T-test | based on directional hypothesis. Evaluates differences in data on only one end of distribution (neg or pos) |

Two-tailed T-test | based on a nondirectional hypothesis. Evaluates differences in data on both ends of a distribution. Tests of signif are almost always two-tailed |

Inappropriate use of T-test | use to compare more than 2 means within a single sample. |

ANOVA | parametric test used to compare 3 or more independent tx groups at a selected probability level. |

Simple (one-way) ANOVA | compares multiple groups on a single IND variable. Ex: Balance Master score for 3 different age groups |

Factorial ANOVA | compares multiple groups on two or more IND variables. Ex: 3 levels of ankle injury compared for balance and sensory |

ANCOVA | Parametric test used to compare 2 or more treatment groups or conditions while also controlling for the effectss of intervening variables. |

Nonparametric statistics | ORDINAL or NOMINAL data, testing not based on population parameters |

When to use nonparametric | parametric assumptions cannot be met. used with small sample, ordinal or nominal level data. Less powerful than parametric |

Chi square test | nonparametric test of significance. Used to compare data in the form of frequency counts in 2 or more mutually exclusive categories (rate treatment preferences) |

Correlational statistics | used to determine the relative strength of a relationship between 2 variables |

Pearson product-moment coefficient (r) | used to correlate CONTINUOUS data wi |

Linear Regression | used to establish relationship between two variables as a basis of prediction |

Spearman's Rank | NONPARAMETRIC test to correlate ORDINAL data. |

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Created by:
Jenwithonen