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# I/O Psych 542

### Exam 1 Part 1

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

Placebo effect | treatment effect due to the expectations that the treatment will work |

Single-blind experiment | where information that could introduce bias or otherwise skew the results is withheld from the participants but the experimenter will be in full possession of the facts |

Double-blind experiment | neither the participants or the researcher knows which participants belong to the control group until after the experiment is finished |

Demand Characteristics | participants responding to subtle cues about what is expected. They are not passive in experiments |

independent variable | a variable that we either manipulate or observe to determine its effects on the dependent variable |

dependent variable | the outcome variable that we hypothesize to be related to, or caused by, changes in the independent variable |

confounding variable | any variable that systematically varies with the independent variable so that we cannot logically determine which variable is at work; also called a confound |

between groups research design | An experiment in which participants experience one, and only one, level of the independent variable. An experiment that compares a control group with an experimental group is an example of a between-groups design |

within groups research design | A study in which the different levels of the independent variable are experienced by all participants of the study. An example would be an experiment that compares the same group of people before & after they experience a level of an independent variable |

Raw Score | Data that has not yet been transformed or analyzed |

Distribution(s) – (aka: Frequency distribution) | this is what we organize our raw scores into. This describes the pattern of a set of numbers by displaying a count or proportion for each possible value of a variable |

frequency table | A visual depiction of data that shows how often each value occurred; that is, how many scores were at each value |

Cumulative Percentage | another way of expressing frequency distribution. It calculates the frequency within each interval, much as relative frequency distribution calculates percentage of frequency. Cumulative percentage = (cumulative frequency / n) x 100 |

Group Frequency Table | Allows us to depict our data visually by reporting the frequencies within a given interval rather than the frequencies for a specific value. Example – a range of values (1 – 5, 6 – 10, etc) |

histogram | Looks like a bar graph but typically depicts scale data with the variable on the x-axis and frequencies on the y-axis (pg 30, figure 2-1) |

frequency polygon | A line graph with the x-axis representing values (or midpoints of intervals) and the y-axis representing frequencies; a dot is placed at the frequency for each value (or midpoint), and the dots are connected (pg 32, figure 2-3) |

central tendency | the descriptive statistic that best represents the center of a data set, the particular value that all the other data seem to be gathering around |

mean | arithmetic average of a group of scores |

median | the middle score of all the scores in a sample when the scores are arranged in ascending order |

mode | the most common score of all the scores in a sample |

unimodal | when the distribution of scores has one mode (not all distributions are normal |

bimodal | when a distribution has two modes |

multimodal | when a distribution has more than two modes |

scatter plot | a graph that depicts the relation between two scale variables (pg 49, table 3.1) |

range | Range is the measure of variability calculated by subtracting the lowest score – the minimum – from the highest score – the maximum |

linear relation | this between variables means that the relation between variables is best described by a straight line |

nonlinear relation | this between variables means that the relation between variables is best described by a line that breaks or curves in some way (pg 50, figure 3-6) |

line graph | shows relation between 2 scale variables; the line can represent predicted y scores for each x value, & the line can represent change in a variable over time |

example of a line graph | (scatterplot is 1– line of best fit – allows us to make predictions for a person’s value on the y variable from his/her value on the x variable |

time series plot | Another type of line graph. A graph that plots a scale variable on the y-axis as it changes over an increment of time (example: second, day, century) labeled on the x-axis (pg 51, figure 3-8) |

bar graph | visual depictions of data when the independent variable is nominal and the dependent variable is scale. Each bar typically represents the average value of the dependent variable for each category |

pareto chart | a type of bar graph in which the categories among the x-axis are ordered from the highest bar on the left to the lowest bar on the right |

pictorial graph | a visual depiction of data typically used for an independent variable with very few levels (categories) and a scale dependent variable. Each categories uses a picture or a symbol to represent the value on the scale dependent variable |

pie chart | a graph in the shame of a circle with a slice for every category. The size of each slice represents the proportion (or percentage) of each category |

chart junk | any unnecessary information or feature in a graph that detracts from a viewer’s ability to understand the data |

moire vibrations | any of the patterns that computers provide as options to fill in bars (this is an example of chartjunk) |

A duck | features of the data that have been dressed up to be something other than merely data (this is also an example of chartjunk) |

generalizability | researcher’s ability to apply findings from one sample or in one context to other samples or contexts. This is also called external validity |