Experiment Basics: Variables

Experiment Basics: Variables

Experiment Basics: Variables Psych 231: Research Methods in Psychology Class Experiment Turn in your data sheets & consent forms I will analyze the data and the results will be discussed in labs Quiz 5 is due Friday Class Experiment Results

Mean: 79.4 Median: 80 Range: 54-97 If you want to go over your exam set up a time to see me Exam 1 Common errors: Four Cannons of scientific method (& pg 6-9 of the textbook)

Exam 1 Common errors: 3 of 5 APA General Ethical Principles (& pg 51-54 of the textbook) Exam 1 Common errors: A researcher examined the relationship between music and mood. He presented two groups of participants the same video clips but the two groups received different musical soundtracks. Following the presentation of the videos, participants completed a questionnaire designed to measure their current mood. (1) What was the IV and DV

(2) Identify the Research Design used in the study (3) Identify a major advantage of using this research design for this study (4) Identify a major disadvantage/limitation of using this research design for this study. Experimental Design Manipulated the music (IV) and measured the effects on mood (DV) Major advantage: ability to make causal claims, impose control, etc. Major disadvantage: lower external validity and/or generalizability Exam 1 Youve got your theory.

What behavior you want to examine Identified what things (variables) you think affect that behavior So you want to do an experiment? Youve got your theory. Next you need to derive predictions from the theory. These should be stated as hypotheses. In terms of conceptual variables or constructs Conceptual variables are abstract theoretical entities

Consider our class experiment Theory & Hypotheses: Activation of social concepts & how connected to your social network you feel. Social vs. Non-social websites Cell phone presence So you want to do an experiment? Youve got your theory. Next you need to derive predictions from the theory. Now you need to design the experiment.

You need to operationalize your variables in terms of how they will be: Manipulated Measured Controlled Be aware of the underlying assumptions connecting your constructs to your operational variables Be prepared to justify all of your choices So you want to do an experiment? Conceptual vs. Operational

Conceptual variables (constructs) are abstract theoretical entities Operational variables are defined in terms within the experiment. They are concrete so that they can be measured or manipulated Conceptual Independent variables Dependent variable Operational Social connectedness Cell phone presence or absence Social concepts/words

Websites social or non-social Activation of social concepts Word scramble test Variables Other Variables in our experiment: Time for unscrambling Kind of cell phone present age, gender, time of testing, Extraneous variables Independent variables (Explanatory)

Dependent variables (Response) Correlational designs Extraneous variables Control variables Random variables Confound variables Many kinds of Variables have similar functions Conceptual

Independent variables Dependent variables Extraneous variables Control variables Random variables Confound variables Speed Operationalized Walk vs. run How wet

Weight of suit Velocity of the rain Speed of the run What you are wearing Amount of rainfall Amount of wind If you look carefully at their design, it looks like they treat this as another IV (listen around 1:52 of the video) Mythbusters exp (~3 mins) Many kinds of Variables Note: Mythbusters revisit the issue and come to a different conclusion Local news story & interview

Independent variables (explanatory) Dependent variables (response) Extraneous variables Control variables Random variables Confound variables Many kinds of Variables

The variables that are manipulated by the experimenter (sometimes called factors) Each IV must have at least two levels Remember the point of an experiment is comparison Combination of all the levels of all of the IVs results in the different conditions in an experiment Independent Variables Factor A 1 factor, 2 levels Condition 1 Condition 2

Factor A 1 factor, 3 levels Cond 1 Cond 2 Cond 3 Factor B 2 factors, 2 x 3 levels Factor A Cond 1 Cond 2 Cond 3 Cond 4 Cond 5 Cond 6 Independent Variables Mythbusters exp (~3 mins) 2 factors, 2 x 2 levels Windy Wind

Speed No Wind Walk Cond 1 Cond 2 Run Cond 3 Cond 4 They only talk about the effect of Speed, but they could have also talked about Wind, and the interaction of speed and wind Independent Variables Methods of manipulation Straightforward Stimulus manipulation - different conditions use different stimuli Social vs. non-social websites Instructional manipulation different groups are given

different instructions Staged Event manipulation manipulate characteristics of the context, setting, etc. Presence or absence of cell phone Subject (Participant) there are (pre-existing mostly) differences between the subjects in the different conditions leads to a quasi-experiment Manipulating your independent variable Choosing the right levels of your independent variable

Review the literature Do a pilot experiment Consider the costs, your resources, your limitations Be realistic Pick levels found in the real world Pay attention to the range of the levels Pick a large enough range to show the effect Aim for the middle of the range Choosing your independent variable These are things that you want to try to avoid by careful selection of the levels of your IV (may be issues for your DV as well).

Demand characteristics Experimenter bias Reactivity Floor and ceiling effects (range effects) Identifying potential problems Characteristics of the study that may give away the purpose of the experiment May influence how the participants behave in the study Examples: Experiment title: The effects of horror movies on mood

Obvious manipulation: Having participants see lists of words and pictures and then later testing to see if pictures or words are remembered better Biased or leading questions: Dont you think its bad to murder unborn children? Demand characteristics Experimenter bias (expectancy effects) The experimenter may influence the results (intentionally and unintentionally) E.g., Clever Hans One solution is to keep the experimenter (as well as the participants) blind as to what conditions are being tested

Experimenter Bias Knowing that you are being measured Just being in an experimental setting, people dont always respond the way that they normally would. Cooperative Defensive Non-cooperative Reactivity Floor: A value below which a response cannot be made

Ceiling: When the dependent variable reaches a level that cannot be exceeded As a result the effects of your IV (if there are indeed any) cant be seen. Imagine a task that is so difficult, that none of your participants can do it. So while there may be an effect of the IV, that effect cant be seen because everybody has maxed out Imagine a task that is so easy, that everybody scores a 100% To avoid floor and ceiling effects you want to pick levels of your IV that result in middle level performance in your DV

Range effects Independent variables (explanatory) Dependent variables (response) Extraneous variables Control variables Random variables Confound variables Variables

The variables that are measured by the experimenter They are dependent on the independent variables (if there is a relationship between the IV and DV as the hypothesis predicts). Dependent Variables How to measure your your construct: Can the participant provide self-report? Introspection specially trained observers of their own thought processes, method fell out of favor in early 1900s Rating scales strongly agree - agree - undecided - disagree - strongly disagree

Is the dependent variable directly observable? Choice/decision Is the dependent variable indirectly observable? Physiological measures (e.g. GSR, heart rate) Behavioral measures (e.g. speed, accuracy) Choosing your dependent variable Scales of measurement Errors in measurement Measuring your dependent variables

Scales of measurement Errors in measurement Measuring your dependent variables Scales of measurement - the correspondence between the numbers representing the properties that were measuring The scale that you use will (partially) determine what kinds of statistical analyses you can perform Measuring your dependent variables Categorical variables (qualitative)

Nominal scale Ordinal scale Quantitative variables Interval scale Ratio scale Scales of measurement Nominal Scale: Consists of a set of categories that have different names.

Label and categorize observations, Do not make any quantitative distinctions between observations. Example: Eye color: blue, green, brown, hazel Scales of measurement Categorical variables (qualitative) Nominal scale Ordinal scale Categories

Quantitative variables Interval scale Ratio scale Scales of measurement Ordinal Scale: Consists of a set of categories that are organized in an ordered sequence. Rank observations in terms of size or magnitude. Example: T-shirt size:

Small, Med, Lrg, XL, Scales of measurement XXL Categorical variables Nominal scale

Ordinal scale Categories Categories with order Quantitative variables Interval scale Ratio scale Scales of measurement Interval Scale: Consists of ordered categories where all of the categories are intervals of exactly the same size. Example: Fahrenheit temperature scale

With an interval scale, equal differences between numbers on the scale reflect equal differences in magnitude. 20 40 20 increase 60 80 20 increase The amount of temperature increase is the same

However, Ratios of magnitudes are not meaningful. 40 Not Twice as hot 20 Scales of measurement Categorical variables Nominal scale Ordinal scale Quantitative variables

Interval scale Ratio scale Categories Categories with order Ordered Categories of same size Scales of measurement Ratio scale: An interval scale with the additional feature of an absolute zero point. Ratios of numbers DO reflect ratios of magnitude.

It is easy to get ratio and interval scales confused Example: Measuring your height with playing cards Scales of measurement Ratio scale 8 cards high Scales of measurement Interval scale 5 cards high Scales of measurement Ratio scale Interval scale 8 cards high 5 cards high

0 cards high means no height Scales of measurement 0 cards high means as tall as the table Categorical variables Nominal scale Ordinal scale

Quantitative variables Interval scale Ratio scale Categories Categories with order Ordered Categories of same size Ordered Categories of same size with zero point Best Scale? Given a choice, usually prefer highest level of measurement possible Scales of measurement

Scales of measurement Errors in measurement Reliability & Validity Sampling error Measuring your dependent variables Example: Measuring intelligence? How do we measure the construct?

How good is our measure? How does it compare to other measures of the construct? Is it a self-consistent measure? Internet IQ tests: Are they valid? (The Guardian Nov. 2013) Measuring the true score In search of the true score Reliability Do you get the same value with multiple measurements? Consistency getting roughly the same results under similar conditions

Validity Does your measure really measure the construct? Is there bias in our measurement? (systematic error) Errors in measurement Bulls eye = the true score for the construct e.g., a persons Intelligence Dart Throw = a measurement e.g., trying to measure that persons Intelligence Dartboard analogy Bulls eye = the true score for the construct Reliability = consistency Measurement error Validity = measuring what is intended Estimate of

true score Estimate of true score = average of all of the measurements unreliable - The dots are spread out - The & are different invalid Dartboard analogy Bulls eye = the true score Reliability = consistency Validity = measuring what is intended biased unreliable reliable

invalid invalid Dartboard analogy reliable valid In search of the true score Reliability Do you get the same value with multiple measurements? Consistency getting roughly the same results under similar conditions

Validity Does your measure really measure the construct? Is there bias in our measurement? (systematic error) Errors in measurement True score + measurement error A reliable measure will have a small amount of error Multiple kinds of reliability Test-retest Internal consistency Inter-rater reliability Reliability

Test-restest reliability Test the same participants more than once Measurement from the same person at two different times Should be consistent across different administrations Reliable Reliability Unreliable Internal consistency reliability

Multiple items testing the same construct Extent to which scores on the items of a measure correlate with each other Cronbachs alpha () Split-half reliability Correlation of score on one half of the measure with the other half (randomly determined) Reliability Inter-rater reliability At least 2 raters observe behavior Extent to which raters agree in their observations Are the raters consistent?

Requires some training in judgment Funny 4:56 Reliability 5:00 Not very funny In search of the true score Reliability Do you get the same value with multiple measurements? Consistency getting roughly the same results under similar conditions

Validity Does your measure really measure the construct? Is there bias in our measurement? (systematic error) Errors in measurement Does your measure really measure what it is supposed to measure (the construct)? There are many kinds of validity Validity VALIDITY CONSTRUCT

INTERNAL CRITERIONORIENTED FACE PREDICTIVE CONVERGENT CONCURRENT DISCRIMINANT Many kinds of Validity EXTERNAL VALIDITY CONSTRUCT INTERNAL

CRITERIONORIENTED FACE PREDICTIVE CONVERGENT CONCURRENT DISCRIMINANT Many kinds of Validity EXTERNAL At the surface level, does it look as if the measure is testing the construct?

This guy seems smart to me, and he got a high score on my IQ measure. Face Validity Usually requires multiple studies, a large body of evidence that supports the claim that the measure really tests the construct Construct Validity The precision of the results Did the change in the DV result from the changes in the IV or

does it come from something else? Internal Validity Experimenter bias & reactivity History an event happens the experiment Maturation participants get older (and other changes) Selection nonrandom selection may lead to biases

Mortality (attrition) participants drop out or cant continue Regression toward the mean extreme performance is often followed by performance closer to the mean The SI cover jinx | Madden Curse Threats to internal validity Are experiments real life behavioral situations, or does the process of control put too much limitation on the way things really work? Example: Measuring driving while distracted

External Validity Variable representativeness Subject representativeness Relevant variables for the behavior studied along which the sample may vary Characteristics of sample and target population along these relevant variables Setting representativeness

Ecological validity - are the properties of the research setting similar to those outside the lab External Validity Scales of measurement Errors in measurement Reliability & Validity Sampling error Measuring your dependent variables Errors in measurement

Sampling error = 71 Population Everybody that the research is targeted to be about Sampling error X = 68 Sample Sampling The subset of the population that actually participates in the research

Population Sampling to make data collection manageable Inferential statistics used to generalize back Sample Sampling Allows us to quantify the Sampling error

Goals of good sampling: Maximize Representativeness: To what extent do the characteristics of those in the sample reflect those in the population Reduce Bias: A systematic difference between those in the sample and those in the population Key tool: Random selection Sampling Probability sampling

Have some element of random selection Non-probability sampling Simple random sampling Cluster sampling Stratified sampling Quota sampling Convenience sampling Random element is removed. Susceptible to biased selection

There are advantages and disadvantages to each of these methods I recommend that you check out table 6.1 in the textbook pp 127-128 Here is a nice video (~5 mins.) reviewing some of the sampling techniques (Statistics Learning Centre) Sampling Methods Every individual has a equal and independent chance of being selected from the population Simple random sampling

Step 1: Identify clusters Step 2: randomly select some clusters Step 3: randomly select from each selected cluster Cluster sampling Step 1: Identify distribution of subgroups (strata) in population 8/40 = 20% 20/40 = 50% 12/40 = 30% Step 2: randomly select from each group so that your sample distribution matches the population distribution Stratified sampling

Step 1: identify the specific subgroups (strata) Step 2: take from each group until desired number of individuals (not using random selection) Quota sampling Use the participants who are easy to get (e.g., volunteer sign-up sheets, using a group that you already have access to, etc.) Convenience sampling Use the participants who are easy to get (e.g., volunteer sign-up sheets, using a group that you already have access to, etc.) College student bias (World of Psychology Blog) Western Culture bias

Who Who are are the the people people studied studied in in behavioral behavioral science science research? research? A A recent recent analysis analysis of of the the top top journals journals in in six six sub-disciplines sub-disciplines of of psychology

psychology from from 2003 2003 to to 2007 2007 revealed revealed that that 68% 68% of of subjects subjects came came from from the the United United States, States, and and aa full full 96% 96% of of subjects subjects were

were from from Western Western industrialized industrialized countries, countries, specifically specifically those those in in North North America America and and Europe, Europe, as as well well as as Australia Australia and and Israel Israel (Arnett (Arnett 2008). 2008). The

The make-up make-up of of these these samples samples appears appears to to largely largely reflect reflect the the country country of of residence residence of of the the authors, authors, as as 73% 73% of of first first authors authors

were were at at American American universities, universities, and and 99% 99% were were at at universities universities in in Western Western countries. countries. This This means means that that 96% 96% of of psychological psychological samples samples come come from

from countries countries with with only only 12% 12% of of the the world's world's population. population. Henrich, Henrich, J.J. Heine, Heine, S.J., S.J., & & Norenzayan, Norenzayan,A. A. (2010). (2010). The The weirdest weirdest people people in in the

the world? world? (free (free access). access). Behavioral Behavioral and and Brain Brain Sciences, Sciences, 33(2-3), 33(2-3), 61-83. 61-83. Convenience sampling Independent variables Dependent variables Measurement

Scales of measurement Errors in measurement Extraneous variables Control variables Random variables Confound variables Variables Control variables

Holding things constant - Controls for excessive random variability Random variables may freely vary, to spread variability equally across all experimental conditions Randomization A procedure that assures that each level of an extraneous variable has an equal chance of occurring in all conditions of observation. Confound variables Variables that havent been accounted for (manipulated, measured, randomized, controlled) that can impact changes in the dependent variable(s)

Co-varys with both the dependent AND an independent variable Extraneous Variables Divide into two groups: men women Instructions: Read aloud the COLOR that the words are presented in. When done raise your hand. Women first. Men please close your eyes.

Okay ready? Colors and words Blue Green Red Purple Yellow Green Purple Blue Red Yellow Blue Red Green List 1

Okay, now it is the mens turn. Remember the instructions: Read aloud the COLOR that the words are presented in. When done raise your hand. Okay ready? Blue Green Red Purple Yellow Green Purple Blue Red Yellow Blue

Red Green List 2 So why the difference between the results for men versus women? Is this support for a theory that proposes: Women are good color identifiers, men are not Why or why not? Lets look at the two lists. Our results Matched

List 1 List 2 Women Men Blue Green Red Purple Yellow Green Purple Blue Red Yellow Blue Red Green

Blue Green Red Purple Yellow Green Purple Blue Red Yellow Blue Red Green Mis-Matched Blue Green Red

Purple Yellow Green Purple Blue Red Yellow Blue Red Green What resulted in the performance difference? Our manipulated independent variable (men vs. women) Our question of interest

The other variable match/mis-match? Because the two variables are perfectly correlated we cant tell This is the problem with confounds IV Co-vary together ? DV Confound Confound that we cant rule out Blue Green Red Purple

Yellow Green Purple Blue Red Yellow Blue Red Green Blue Green Red Purple Yellow Green Purple Blue Red Yellow Blue Red

Green What DIDNT result in the performance difference? Extraneous variables Control # of words on the list The actual words that were printed Random Age of the men and women in the groups Majors, class level, seating in classroom,

These are not confounds, because they dont co-vary with the IV Blue Green Red Purple Yellow Green Purple Blue Red Yellow Blue Red Green Our goal:

To test the possibility of a systematic relationship between the variability in our IV and how that affects the variability of our DV. Control is used to: Minimize excessive variability To reduce the potential of confounds (systematic variability not part of the research design) Experimental Control Our goal: To test the possibility of a systematic relationship between the variability in our IV and how that

affects the variability of our DV. T = NRexp + NRother + R Nonrandom (NR) Variability NRexp: Manipulated independent variables (IV) Our hypothesis: the IV will result in changes in the DV NRother: extraneous variables (EV) which covary with IV Condfounds Random (R) Variability Imprecision in measurement (DV) Randomly varying extraneous variables (EV) Experimental Control

Variability in a simple experiment: T = NRexp + NRother + R Treatment group NR other NR exp R Absence of the treatment Control group (NRexp = 0) NR other R

perfect experiment - no confounds (NRother = 0) Experimental Control: Weight analogy Variability in a simple experiment: T = NRexp + NRother + R Control group Treatment group NR exp R R Difference Detector Our experiment is a difference detector

Experimental Control: Weight analogy If there is an effect of the treatment then NRexp will 0 Control group Treatment group R NR exp R Difference Detector Our Ourexperiment experimentcan candetect detectthe the

effect effectof ofthe thetreatment treatment Experimental Control: Weight analogy Potential Problems Confounding Excessive random variability Difference Detector Things making detection difficult

Confound If an EV co-varies with IV, then NRother component of data will be present, and may lead to misattribution of effect to IV IV DV Co-vary together EV Potential Problems Confound Hard to detect the effect of NRexp because the effect looks like it could be from NRexp but could be due to the NRother

R NR other NR exp R Difference Detector Experiment Experimentcan candetect detectan aneffect, effect, but butcant canttell tellwhere

whereititisisfrom from Confounding Confound Hard to detect the effect of NRexp because the effect looks like it could be from NRexp but could be due to the NRother These Thesetwo twosituations situationslook look the thesame same R

NR other R NR NR exp other R Difference Detector There Thereisisan aneffect effectof

ofthe theIV IV Confounding R Difference Detector There Thereisisnot notan aneffect effectofofthe theIV IV Excessive random variability

If experimental control procedures are not applied Then R component of data will be excessively large, and may make NRexp undetectable Potential Problems If R is large relative to NRexp then detecting a difference may be difficult R R NR exp Difference Detector Experiment Experimentcant

cantdetect detectthe the effect effectof ofthe thetreatment treatment Excessive random variability But if we reduce the size of NRother and R relative to NRexp then detecting gets easier So try to minimize this by using good measures of DV, good manipulations of IV, etc. R NR exp

R Difference Detector Our Ourexperiment experimentcan candetect detectthe the effect effectof ofthe thetreatment treatment Reduced random variability How do we introduce control?

Methods of Experimental Control Constancy/Randomization Comparison Production Controlling Variability Constancy/Randomization If there is a variable that may be related to the DV that you cant (or dont want to) manipulate Control variable: hold it constant Random variable: let it vary randomly across all of the experimental conditions Methods of Controlling Variability

Comparison An experiment always makes a comparison, so it must have at least two groups Sometimes there are control groups This is often the absence of the treatment Training group No training (Control) group Without control groups if is harder to see what is really happening in the experiment It is easier to be swayed by plausibility or inappropriate comparisons

Useful for eliminating potential confounds Methods of Controlling Variability Comparison An experiment always makes a comparison, so it must have at least two groups Sometimes there are control groups This is often the absence of the treatment Sometimes there are a range of values of the IV 1 week of Training group 2 weeks of Training group 3 weeks of

Training group Methods of Controlling Variability Production The experimenter selects the specific values of the Independent Variables 1 week of Training group 2 weeks of Training group 3 weeks of Training group Need to do this carefully

Suppose that you dont find a difference in the DV across your different groups Is this because the IV and DV arent related? Or is it because your levels of IV werent different enough Methods of Controlling Variability So far weve covered a lot of the about details experiments generally Now lets consider some specific experimental designs. Some bad (but common) designs Some good designs

1 Factor, two levels 1 Factor, multi-levels Between & within factors Factorial (more than 1 factor) Experimental designs Bad design example 1: Does standing close to somebody cause them to move? hmm thats an empirical question. Lets see what happens if So you stand closely to people and see how long before

they move Problem: no control group to establish the comparison group (this design is sometimes called one-shot case study design) Poorly designed experiments Bad design example 2: Testing the effectiveness of a stop smoking relaxation program The participants choose which group (relaxation or no program) to be in Poorly designed experiments

Bad design example 2: Non-equivalent control groups Self Assignment Independent Variable Dependent Variable Training group Measure No training (Control) group Measure participants

Random Assignment Problem: Problem:selection selectionbias biasfor forthe thetwo two groups, groups,need needtotodo do random assignment random assignmenttotogroups groups Poorly designed experiments

Bad design example 3: Does a relaxation program decrease the urge to smoke? Pretest desire level give relaxation program posttest desire to smoke Poorly designed experiments Bad design example 3: One group pretest-posttest design participants Dependent Variable Independent Variable

Dependent Variable Pre-test Training group Post-test Measure Post-test No Training Add Pre-test Measure group another factor Problems include: history, maturation, testing, and more

Poorly designed experiments Good design example How does anxiety level affect test performance? Two groups take the same test Grp1 (moderate anxiety group): 5 min lecture on the importance of good grades for success Grp2 (low anxiety group): 5 min lecture on how good grades dont matter, just trying is good enough 1 Factor (Independent variable), two levels Basically you want to compare two treatments (conditions) The statistics are pretty easy, a t-test 1 factor - 2 levels

Good design example How does anxiety level affect test performance? Random Assignment Anxiety Dependent Variable Low Test Moderate Test

participants 1 factor - 2 levels Good design example How does anxiety level affect test performance? One factor Use a t-test to see if anxiety low moderate 60 80 test performance

these points are statistically different T-test = Observed difference between conditions Difference expected by chance low Two levels 1 factor - 2 levels moderate anxiety

Advantages: Simple, relatively easy to interpret the results Is the independent variable worth studying? If no effect, then usually dont bother with a more complex design Sometimes two levels is all you need One theory predicts one pattern and another predicts a different pattern 1 factor - 2 levels Disadvantages:

True shape of the function is hard to see Interpolation and Extrapolation are not a good idea Interpolation test performance What happens within of the ranges that you test? low 1 factor - 2 levels moderate anxiety Disadvantages: True shape of the function is hard to see

Interpolation and Extrapolation are not a good idea Extrapolation test performance What happens outside of the ranges that you test? low moderate high anxiety 1 factor - 2 levels For more complex theories you will typically need more complex designs (more than two levels of one IV) 1 factor - more than two levels

Basically you want to compare more than two conditions The statistics are a little more difficult, an ANOVA (Analysis of Variance) 1 Factor - multilevel experiments Good design example (similar to earlier ex.) How does anxiety level affect test performance? Two groups take the same test Grp1 (moderate anxiety group): 5 min lecture on the importance of good grades for success Grp2 (low anxiety group): 5 min lecture on how good grades dont matter, just trying is good enough

Grp3 (high anxiety group): 5 min lecture on how the students must pass this test to pass the course 1 Factor - multilevel experiments Random Assignment participants Anxiety Dependent Variable Low Test Moderate Test

High Test 1 factor - 3 levels low mod high 60 80 60 test performance anxiety

low mod high anxiety 1 Factor - multilevel experiments Advantages Gives a better picture of the relationship (function) Generally, the more levels you have, the less you have to worry about your range of the

independent variable 1 Factor - multilevel experiments 2 levels test performance test performance 3 levels low moderate anxiety low mod high

anxiety Relationship between Anxiety and Performance Disadvantages Needs more resources (participants and/or stimuli) Requires more complex statistical analysis (analysis of variance and pair-wise comparisons) 1 Factor - multilevel experiments The ANOVA just tells you that not all of the groups

are equal. If this is your conclusion (you get a significant ANOVA) then you should do further tests to see where the differences are High vs. Low High vs. Moderate Low vs. Moderate Pair-wise comparisons

Recently Viewed Presentations

  • RECN 345 Fundamentals of Sport and Exercise Science

    RECN 345 Fundamentals of Sport and Exercise Science

    Improvements are thought to be neuromuscular in origin as there is not much change in muscle mass with children. Suggested higher reps and low-moderate weight is best e.g. 3 sets of 10-15 reps per exercise at moderate load (2-3 sessions...
  • Chapter Title - WordPress.com

    Chapter Title - WordPress.com

    Chapter 6 A segment margin is computed by subtracting the traceable fixed costs of a segment from its contribution margin. The segment margin is a valuable tool for assessing the long-run profitability of a segment. Allocating common costs to segments...
  • Linac RF Modulators New Specifications Trevor Butler Oct

    Linac RF Modulators New Specifications Trevor Butler Oct

    LE Linac Modulators Present Situation. Modulators are a major source of downtime in the Linac. 57 % of downtime over the past 10 years. DC Power supplies - Built directly to the frame and need to be repaired in place,...
  • The Bridge

    The Bridge

    Rooted in a strong evidence base research into desistance theory has shown that with offenders with chaotic lifestyles, if areas of their lives are properly addressed, then this significantly reduce the risk of reoffending. ... Some of you will have...
  • Using Data Types - Washington University in St. Louis

    Using Data Types - Washington University in St. Louis

    return Math.abs(lum(a)- lum(b)) >= 128.0;} 208. 255. 28. 14. 105. 47. Head this rule next time you give a Powerpoint presentation or design a web page. Blue = 0 51 153. Green = 0 128 0. Red = 204 0...
  • Bayonet Charge - Resources for Miss Archer's GCSE classes

    Bayonet Charge - Resources for Miss Archer's GCSE classes

    The poem focuses on a nameless soldier in the First World War (1914-18). It describes the experience of 'going over-the-top'. This was when soldiers hiding in trenches were ordered to 'fix bayonets' (attach the long knives to the end of...
  • Estudio y representacion de funciones

    Estudio y representacion de funciones

    ¿Cuántos televisores deben fabricarse para que el beneficio (ingresos menos gastos) sea máximo? Ejercicios propuestos P11.Una pelota es lanzada verticalmente hacia arriba desde lo alto de un edificio. La altura que alcanza viene dada por la fórmula h = 80...
  • National Ski Patrol

    National Ski Patrol

    lift evacuation, lift evacuation training, and the selection of equipment to be used in conjunction with such evacuation or training is the sole responsibility of ski area management. Patrollers will participate in lift evacuation and lift evacuation training only as...