Methods study guide test 2 PDF

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Methods Study Guide Test 2

General Design Within-groups design - An experimental design in which there is only a single group of participants, and each participant is presented with all levels of the independent variable Repeated-measures design- A within-groups design in which participants are measured on a dependent variable more than once, after exposure to each level of the independent variable Concurrent-measures design- A within-groups design in which participants are measured on a dependent variable more than once, after exposure to each level of the independent variable Between-groups design (independent) - An experimental design in which different groups of participants are placed into different levels of the independent variable; aka independentgroups design Pretest/posttest design- An experimental design in which participants are randomly assigned to at least two groups and are tested on the key dependent variable twice-once before exposure to the independent variable, and once after exposure Posttest-only design - An experimental design in which participants are randomly assigned to independent variable groups and are tested on the dependent variable once Concurrent-measures design - A within-groups design in which participants are exposed to all the levels of an independent variable at roughly the same time, and a single attitudinal or behavioral preference is the dependent measure Manipulation- Assigning participants to one level or another of a variable Measuring- Recording a variable that has been affected (or not) by the manipulation of another variable Type I Error – False positive Type II Error – “Miss” Control group- Group of participants who are not affected by a manipulation of the independent variable; the level of an independent variable that is intended to represent a neutral condition. A level of an independent variable that is intended to represent "no treatment" or a neutral condition Treatment group- A group of participants who are affected by a manipulation of the independent variable

Placebo group- A control group of participants who believe they are receiving treatment, but who are instead receiving a placebo Random assignment- Assigning participants to experimental and control conditions by chance, thus minimizing preexisting differences between those assigned to the different groups Masked or Blind Study- participants know which group they are in but observers do not. Double-blind study- Study in which neither the participants nor the researchers who evaluate them know who is in the treatment group and who is in the comparison group factorial designs- designs with more than one independent variable (or factor); all levels of each independent variable are combined with all levels of the other independent variables simplest factorial design- 2 x 2 (two by two) factorial design - has two independent variables, each having two levels Factorial designs yield two kinds of information – (1) main effect (2) interaction 2 x 2 design results in four experimental conditions- 2 X2 design always has four groups 2 x 3 design (increasing the number of levels of an independent variable)- contains two independent variables: independent variable A has two levels, and independent variable B has three levels, thus the 2 x 3 design has six conditions General format for describing factorial designs- Number of levels of first IV x number of levels of third IV Counterbalancing - A technique in which researchers present the levels of the independent variable to participants in different orders in order to avoid the effect of boredom, practice, fatigue, aftertaste, or carryover effects on the results Partial counterbalancing- A type of counterbalancing in which only some of the possible condition orders are represented Latin square- A type of partial counterbalancing that ensures that each condition appears in each position at least once / incomplete counterbalance Alpha- Correlation-based statistic to see if their measurement scales have internal reliability; The value determined in advance, at which researchers decide whether the p-value obtained from a sample statistic is low enough to reject the null hypothesis. Main Effects and Interactions Main Effect- In a factorial design, the overall effect of one independent variable on the dependent variable, averaging over the levels of the other independent variable. 

The overall effect of one IV in a complex design

Simple main effect- the effect of one IV at only one level of a second IV—used as a post hoc if there is a significant interaction Interaction Effect- A result from a factorial design, in which the difference in the levels of one independent variable changes, depending on the levels of the other IV; a difference in differences. Ceiling Effect- An experimental design problem in which independent variable groups score almost the same on a dependent variable, such that all scores fall at the high end of their possible distribution. Floor effect- An experimental design in which independent variable groups score almost the same on a dependent variable, such that all scores fall at the low end of their possible distribution. Error variance (noise) random fluctuation; the biggest component of this is usually the individual differences between the participants. Other sources are more controllable—like inconsistencies in the method (situation noise) and unreliable measurements (measurement error). Error variance makes it harder to find a significant difference if one actually exists (increased chance of Type II error). Remember that error variance is a bigger problem when you DO NOT find a significant difference. The question error variance raises is “Is there really no effect, or was the effect obscured by random variation?” The answer is you can’t tell unless you clean up the noise and look again. Measures to reduce error variance—consistent conditions, targeted populations, increased (and therefore more representative) sample size, running within as opposed to between, better measures. Remember, remove extraneous variables and it reduces error variance.

Confounds “Confounds (or confounding variables) are systematic differences between the levels of an IV, other than the IV itself. A confound may make it harder or easier to find a significant difference, depending on its effect (may increase either Type I or Type II error). You should be able to recognize the confounds and apply some method of control to remove or limit them. Remember that the question confounds raise is “Is the result I have found due to the IV, the confound, or some combination of the two?” And the answer to that question is that you just don’t know.” Confound -A second variable that happens to vary systematically along with the intended independent variable and therefore is an alternative explanation for the results; threatens internal validity

Design confound- A second variable that unintentionally varies systematically with the independent variable. Selection Effects- A threat to internal validity that occurs in an independent group design when the kinds of participants at one level of the IV are systematically different from those at the other level. Carryover effects- A confound in which being exposed to one condition changes how people react to the other condition; aka practice effects or order effects Order Effect – In a repeated measures design, when the effect of the independent variable is confounded with carryover from one level to the other or with practice, fatigue or boredom. Maturation - A change in behavior that emerges more or less spontaneously over time through natural development or spontaneous improvement; e.g. people slowly adapt to strange environments, children get better at walking and talking, plants get taller without any outside help History threat- Threat to internal validity that occurs when a “historical” or external event occurs to everyone in the treatment group at the same time as treatment, so it is unclear whether the change in the experimental group is caused by the treatment received or by the historical event. Regression- Threat caused by a statistical concept called regression toward the mean; when a performance is extreme at time 1, at time 2 it is likely to be closer to a typical, or average, performance Attrition- occurs when people drop out of the study before it ends; aka “mortality”, extreme cases tend to drop out and their scores are not included in the posttest. Instrumentation threat- Threat that occurs when a measuring instrument changes over time from having been used before; aka instrument decay. Testing threat- A specific kind of order effect, refers to a change in the participant as part of taking a test (dependent measure) more than once. Practice Effect – a type of order effect in which participants performance improves over time because they become practiced at the dependent measure (not because of the manipulation or treatment) Also called fatigue effect. Demand characteristics- A problem when participants guess what the study is supposed to be about and change their behavior in the expected direction. Spontaneous remission- Phenomenon that occurs when the symptoms of depression or other disorders get better, for no known cause, with time

Bias blind spot- The tendency for people to think that compared to others, they are less likely to engage in biased reasoning. Biased sample- A sample in which some members of the population of interest are systematically left out, and as a consequence, the results from the sample cannot generalize to the population of interest....