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# I/O Psych 542

### Exam 1 Part 3

Question | Answer |
---|---|

4 scales of measurement | nominal, ordinal, interval, ratio |

nominal scale | a variable used for observations that have categories, or names, as their values (ex: blond, brunette, red head). This is a discrete variable |

ordinal scale | a variable used for observations that have rankings as their values (ex: 1st, 2nd, 3rd, etc). This is a discrete variable |

interval scale | variable used for observations that have numbers as their values, and their distance (or interval) between pairs of consecutive numbers is assumed to be equal (ex: measurements of time, or distance, temp that isn’t Kelvin). This is a continuous variable |

ratio scale | a variable that meets the criteria for an interval variable but also has a meaningful zero point (ex: Kelvin temp, the number of times a rat pushes a bar to get food – it has a zero point, the rat may never push it). This is a continuous variable |

descriptive statistics | organize, summarize, and communicate a group of numerical observations. They describe a large amount of data in a single number or just a few numbers. For example: the average weight of a male in the US in 1960 was 175 lbs and in 1980 was 195 lbs |

inferential statistics | use sample data to make general estimates about larger population. Ex: CDC made inferences about weight even though it didn’t weigh everyone in the US. Studied a smaller group of US citizens to make a guess about the entire population |

hypothesis | the prediction we have about something. It differs from a theory or a hunch because we can test it with hypothesis testing, which is the process of drawing conclusions about whether a particular relation between variables is supported by the evidence |

operational definition | specifies the operations or procedures used to measure or manipulate a variable. |

experiment | a study in which participants are randomly assigned to a condition or level of one or more independent variables |

non-experiment | does not introduce a treatment. Example: comparing opinions from different groups |

quasi experiment | you take groups as they are and running experiments there. Example: you can’t test the whole population, but you can use all the grad students at LA Tech |

why are we not able to use true experimental methods | RESOURCE LIMITATIONS OR ETHICAL CONCERNS. FOR EXAMPLE, YOU WOULDN’T EXPERIMENT ON PEOPLE TO SEE IF SUN EXPOSURE CAUSES SKIN CANCER. YOU WOULD NEED TO GATHER THAT DATA USING AN APPROACH THAT IS NOT A TRUE EXPERIMENTAL APPROACH. |

discrete variables | (observations) can take on only specific values (ex. Whole numbers); no other values can exist between those numbers. Example: a person can get up early from 0 to 7 times a week. They can’t get up early 3.5 times a week |

continuous variables | (observations) can take on a full range of values (ex. Numbers out to several decimal places); an infinite number of potential values exist. Example: one person can finish a test in 45 minutes, another person can finish a test in 36 minutes |

how an observation becomes a variable | when we transform them into numbers |

sample (statistics) | observations drawn from the population of interest. |

parameters (whole population) | includes all the possible observations about which we’d like to know something. We use samples to make inferences about the population |

variance | the average of the squared deviations from the mean |

deviations from the mean | subtracting the mean from every score; the amount that a score in a sample differs from the mean of the sample |

standard deviation | the square root of the average of the squared deviations from the mean, and is the typical amount that each score varies, or deviates, from the mean |

sum of squares | the sum of each score’s squared deviation from the mean |

You often want to find out the variance, or how something differs from the mean. The formula for variance is as such: | Subtract the mean (M) from every score (X) to calculate deviations from the mean; then square these deviations, sum them, and divide by sample size (N). By summing the squared deviations and dividing by the sample size, we are taking their mean. |

normal distribution | specific frequency distribution that is a bell-shaped, symmetric, unimodal curve. |

skew to the left | negative |

skew to the right | positive |

skewed distribution | distributions in which one of the tails of the distribution is pulled away from the center. |

positively skewed distribution | has the tail of the distribution extending to the right, in a positive direction. |

negatively skewed distribution | tail of the distribution extending to the left, in a negative direction |

correlation | An association between two or more variables |

purpose of random assignment | every participant in a study has equal chance of being assigned to any of the groups in the study. controls effects of personality traits, life experiences, personal biases,by distributing across each condition of the experiment to an equivalent degree |

reliability | consistency; it will be the same every time. |

validity | accuracy; it measures what it says it measures. |

how reliability and validity are related | has to measure what it says it does (can’t have test that says it measures intelligence but measures personality) has to give same result every time. if test isnt reliable, cant b valid. |

Created by:
cjd021