Quiz 3 Flashcards
(104 cards)
Subjects x A null hypotheses and conclusions
Ho: u(sub t1) = u(sub t2) = u (sub t3) = u (sub t4) = u (sub t5)
-There is no statistically significant difference among population means of the fire trials with regard to the number of words correctly recalled.
Conclusion for Trials (only IV): There is a statistically significant difference among the five trials with regard to the number of words correctly recalled.
Subjects x A x B null hypotheses conclusions
- u (sub sat) = u (sub sun)
- There is no statistically significant difference between the population means of saturday and sunday with regard to the number of hours of tv watched.
- u (sub AM = u (sub PM)
- There is no statistically significant difference between the population means of AM and PM with regard to the number of hours of tv watched.
- There is no statistically significant day x time interaction in the population.
Conclusion: Needed for “day”, “time”, and “day x time”
Subjects/A x B) null hypotheses and conclusions
- u (sub R) = u (sub DC) = u (sub BF)
- There are no statistically significant difference among the population means in regard to their ….
- u (sub BP) = u (sub NBP)
- Similar to first one
- There is no bug problem x pesticide interaction in the population
- Conclusion: Needed for “problems”, “pesticide”, and “prob x pest”
What are carry over effects (Logical Assumptions)
- General Carry Over Effects: general changes in the organism (fatigue, boredom, maturation, adaptation)
- Specific Carry Over Effects: effect of treatment 1 is followed by treatment 2 is different than the effect of treatment 2 followed by treatment 1
- Think of dilated pupils example (different if bright to dim light vs. dim to bright light)
How do you get rid of carry over effects?
- Pre experimental practice
- Space out trials
- Randomize order of treatments
What is a correlation?
-Linear relationship between two variables
What is the conclusion for p (rho) = 0?
Same as it was for F-test.
There is a statistically significant negative correlation between anger and self-esteem scores such that the higher the anger score, the lower is the self-esteem score.
What are the characteristics for Fisher’s z’ transformation?
- Distributes approximately normal
2. Standard error of z’ = 1/square root of N-3
When can you infer causality in a correlation?
- If x causes y, then y cannot cause x
2. If x causes y, then no third variable can cause either x or y
What are the assumptions to correlation?
- Normality in the arrays
- Homogeneity of variance in the arrays (homoscedasticity)
- Linearity
What happens when the assumptions to correlation are violated?
Still pretty robust
What happens when you restrict the range in a correlation?
- When you restrict range in a linear relationship, you underestimate what the true population correlation is
- When you restrict the curvilinear relationship, you overestimate what the true population correlation is
- *NEVER say there is no relationship, because it could be curvilinear
What is linear regression? Formula?
- Where you predict Y from X
- ”regression” means prediction
What is multiple regression?
- Multiple predictors predicting y
- R= multiple correlation (0 → +1) (never negative)
What are the limits to multiple correlation?
- Def: R; the strength of relationship between the dependent variable or criterion (Y variable) and the predictors (or the X variables)
- Limits: There are no negative values, unlike r, and as the number of predictors increase, then the value of R will go up, and this number will increase by chance alone, so you want a few independent predictors in order to optimize value of R – lots of sample size and few excellent predictors that are uncorrelated with each other
How do you test the difference between two independent correlations?
- Ho: rho sub1 = rho sub2
Pg. 184(stats book)
What is a point-biserial correlation?
- When one of the variables is a true dichotomy and the other is continuous
- Correlation in which one variable is a true dichotomy and the other continuous
What is a biserial correlation?
- When the dichotomous variable is artificial (e.g., tall-short, pass-fail, old-young) and the other variable is continuous
- Correlation in which there’s one artificial dichotomy and the other is continuous
Rules for determining the type of design?
- Is there only 1 score per subject?
a. Yes: independent groups design (one, two, or three-way)
b. No: repeated measures (go on to question 2) - Does every subject participate in every group AND every condition?
a. Yes: Subject x A or Subjects x A x B
b. No: (i.e., there is a grouping factor) Subjects/A x B (mixed design)
Helpful Tips:
- How many IVs are there?
- Draw diagram of design
- Read carefully! Be cognizant of words like “each,” “or,” “and,” “either.”
Coefficient of Determination
-r^2
Array
-All y values for a given x
Error terms for Subject x A x B
- For effects not involving “subjects,” use the subjects x that effect interaction sa the error term
- For effects involving “”subjects,” use the largest order interaction as the error term
What are the conclusions (one-way)?
- Ex:
- H(sub o): There is no statistically significant difference between the population means of males and females with regard to their GPAs.
- H(sub o): u(sub m) = u(sub f)
-With null hypothesis, you always state that there is no statistically significant difference.
What are the characteristics of Orthogonal Comparisons?
- A priori hypotheses
- ∑a(sub i) = 0 (valid comparison)
∑a(sub i)b(sub i) = 0 (independence) - Each comparison is on 1 degree of freedom
- You get as many comparisons as g-1 degrees of freedom
- Most powerful test of the three
- Significance level = alpha