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Copy of EPSY6210_20100201
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by PatriciaSimpson@unt.edu 9 years, 5 months ago
EPSY 6210
02.01.2010
(2nd class)
normal curves can take lots of different shapes (get rid of the "bell curve" idea)
 normal distribution is a symmetrical curve
Turned in First Homework:
here's what we learned from it...
additive constants (alter dataset by adding/subtracting a specific amount):
 mean = goes up (or down) by the same amount as the constant added/subtracted to/from the dataset
 SD = same (their variability is the same)
 SD for population = same (see above)
 skewness = same (it doesn't have stronger or weaker central tendencysame PATTERN on graph); if you have a normal (symmetrical) distribution, then your skewness should be 0)
 kurtosis = same (measures normality; may be calculated differently in SPSS or other methods)
 covariance = same
 pearson's r = same
 this is fairly clearly indicated by the scatterplot (graph)
multiplicative constants:

mean = multiplied by the amount of the constant

SD = multiplied by the amount of the constant

SD for population = multiplied by the amount of the constant, then absolute value (essentially; actually done by squaring) statistical difference (spreadoutness) can't be negative.

skewness = same (but possibly with a different positive/negative sign; the pattern won't change, in shape, but may change in direction) (multiplying doesn't change central tendency/symmetry)

kurtosis = same (multiplying doesn't change normality ("shape")) (doesn't change muchit does slightly change the distribution) (???)

covariance = multiplied by the amount of the constant (can see in the scatterplotthe spreadoutness between the Y datapoints is larger, although the pattern is the same)

covariance is similar to variance (which is SD squared, or simply without the last square root step)

covariance = the variance of two variables, crossed together (what is the pattern of their relationship together)

the covariance will be affected by a change in the SD, because it does not remove the influence of SD anywhere in its formula

if multiplied by a negative constant, it will change direction on the scatterplot (and thus be negative covariance result)

pearson's r = same (because we removed the influence of the SD)
additive & multiplicative constants together:
 it changes first by the additive constant, then by the multiplicative one (so see above)
more info on these concepts:
 Z scores = remove the influence of the SD (divide by SDthat's how you remove its influence)
 the property of all Z scores: the mean = 0 and the SD = 1.
 we standardize Z scores to make that happen; this puts the scores in a standard metric
 SD = shows the datapoints relation to the rest of the dataset (gives it context and thus meaning)
 pearson r (correlation) = standardized covariance (put the scores/datapoints on the same metriccan compare different groups of data now.)  you standardize it by removing the influence of the SDs... to put it in a broader (standardized) context.
 property of all r = ranges from 1 to +1
 because pearson's r is standardized (and covariance is not), pearson's r is used more often in journal articlesyou can't compare variables using just the covariance. (Because r falls within 1 to 1, it's easy to see the r in an article and understand what that means (a strong correlation (1), a weak/nonexistent correlation (0), or an inverse correlation (1)).)
"Some people like to think about this crap." (lol)
when in doubt, graph something.
 if x = 1, 2, 3, 4
 and y = 2, 4, 6, 8
 then the sample size is 4 (four people tested on 2 variables)
 these variables are positively related (they go up together)
 graph it: a straight line, going up a tad steeply
 it's a perfect pattern; as x goes up, y doubles
 if you can do mathematical stuff to x (like multiplying by 2), and turn it into y exactly, you have a perfect relationship
 in this case, 2x = y
 in another instance, 2x + 1 = y (linear equation; because when graphed, it makes a line)
 a few cases can dramatically influence our results (on a graph, a few more datapoints can change a curvilinear pattern (best fit) to a linear pattern (line of best fit)).
this points to a major point of multiple regression: it's all about predicting a relationship between variables
 we want to hammer at x until we can find a value (prediction) as close to y as possible.
 y with a carrot on top ("y hat") = predicted score
 should be as close to y as possible IF you want a strong, positive correlation.
 if the pattern/relationship is NOT perfect (linear), then r = less strong
r = if x gets bigger as y gets bigger, then r is between 0 and +1 (positive value).
centroid (cartesian coordinate): on the graph, the plotted mean of x and the plotted mean of y
 the line of best fit MUST go through the centroid (even though it might not go through any others)
 the means of x and y are the numbers that best represent x and y... therefore the line of best fit must go thru both means.
line of best fit:
 goes thru centroid
 trying to get x as close to y as possible (better predictor, thus a more perfect line and thus closer to r = 1)
 y hat = a + bx ...so we can USE this formula to calculate y hats (predicted scores) for future x scores (no error and y hat = y? then r = 1)
 y  (y hat) = "error" = tells us how close a prediction we have
 this can be negative or positive, depending on its direction (if y hat is bigger or smaller than y)
 four variables: x, y, y hat, and error
 plot x with y hat and you get a line (because it's a linear equation)
all of our analyses (this semester) are related to each other;
 they are all correlational
 they all yield rsquaredtype effect sizes
 they all apply weights to observed variables to create synthetic (unobserved) variables
 the synthetic/unobserved variables become the focus of the analysis
SPSS/PASW

"what's the relationship between x and y?" (pearson r)

"does x predict y?" (regression; this is more relevant when we have more than one predictor)

SPSS: analyze  descriptive stats  descriptives  calculates various descriptive stats (we've done this before)

tip: click/look around and try to find things (he won't always tell us how to calculate things in SPSS)
 save the file: format is ".sav" for data files (the "Daddy" file)
 SPSS (regression) = analyze  regression  linear  (y is usually dependent variablethe outcome of interest); (x is the independent variable or predictor)  statistics (pick descriptive)
 then click SAVE (y hat = predicted values, unstandardized; error = residuals, unstandardized (standardized forms transform them into Z scores))
 then click PASTE (NOT "ok")  this builds a command file
 "Syntax Editor" (syntax or command file) opens in a new window
 can add comments to show myself what I'm doing by typing an asterisk and space, then comment, and end each comment/command with a period (* .)
 file, save as: (give it same name as the datafiledifferent extension); file extention = .sps (the "Mommy" file)
 need to put the Daddy & Mommy files together to get a "Baby" file
 highlight what you want to run; click the "play" arrow button on the top menu (it runs the selection)
 that produces the Output file (the "Baby" file)
 ANOVA = really our regression summary table; (regression = between; residuals = within)
 click back to the dataset; PRE = y hat; RES = error
 if you run the same regression a second time, you'll get two more variable columns, appended with "2" instead of "1"  values will be the same, simply calculated them a second time
 it's lined up in the dataset table just like you'd calculate it: y  pres (yhat) should = res (error)
 if you want to check it graphically, graphs  scatterplot  for the variables you want to check
 add fit line  linear  apply (ignore the curves)
TO DO:
 email Paul and ask him to put SPSS/PASW on work computer
 let Annie know what he says
 email Dr. Henson about graphical book on figuring out basic statistical concepts
 read handouts from Dr. Henson (last week, and workshop)
 do homework
 look at book from Annie / library
 look at textbook...
 start research topic  paper / project
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