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Final

PSY248 Study Guide - Final Guide: Semen Analysis, Variance Inflation Factor, Linear CombinationPremium

20 pages157 viewsSpring 2017

Department
Psychology
Course Code
PSY248
Professor
Eugene Chekaluk
Study Guide
Final

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CORRELATION/REGRESSION
Before statistics;
Step 1: understand the research question
Step 2: how are DV and IV measured?
Step 3: choose method of analysis (correlation/regression)
Conduct correlation/regression analysis;
Part 1: univariate
Part 2: bivariate
Part 3: regression and check assumptions
Correlation asks is there a RELATIONSHIP between two variables
Regression refers to PREDICTION
UNIVARIATE
Step 1: understand research question
Example:
IV = age of carer
DV = carer distress
1. What is the level of carer distress? Does carer distress vary? (univariate)
o Produce descriptive statistics
2. Is carer distress (linearly) related to age of carer? (bivariate)
o Produce correlational statistics
3. Does knowledge about age help predict level of carer distress? (regression)
o Produce regression
This is NON-EXPERIMENTAL research
Step 2: how are the DV/IV measured?
We look at
o How we intended to measure (questionnaire)
o What we actually ended up with (results data)
Decide level of measurement categorical, ordinal or interval
Then we need this to decide what statistical analysis to use
This leads to either correlation/regression = IV/DV continuous/interval/numeric and normal
For instance:
o Age: categorical (young, middle, old), distress: categorical (low, medium, high) = chi-
square appropriate
o Age: categorical (young, middle, old), distress: numerical = one-way ANOVA
o Age: numerical, distress: numerical = correlation/regression
Specific Health Questionnaire; consisted of 15 items
o Study had age categories (one of 9) but we are going to treat them as continuous
variables (numeric)
Step 3: choose method of analysis
Produce histograms and describe them based on 5 assumptions
Assumptions of normality:
Central tendency
o Typical or average score, centre of distribution, peak in the distribution does it exist?
Variability (SD and range)
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o Do all the cases tend to score at about the same point or are they widely scattered
width of distribution
Skewness
o Symmetry vs. lopsidedness of distribution
o Positive (right) skew, negative (left) skew, symmetric distributions have no skew
o Symmetry/Standard error of skew = symmetrical (between -2 and +2 = unskewed)
Kurtosis
o Flatness or peakness of a distribution. Platykurtic (flat), leptokurtic (very peaked) and
mesokurtic (a normal distribution)
o Kurtosis = kurtosis/standard error of kurtosis
Between -2 and +2 = mesokurtic
Modal characteristics (modality)
o Frequency of peaks as unimodal, bimodal or multimodal
o A distribution with no mode is a uniform or rectangular distribution
o In general, the presence of more than one frequency peak (mode) in a distribution
means that the data represent several relatively homogenous subgroups within the
larger sample being studied
o You want your distribution to be unimodal indicates homogeneity
USING SPSS
Use the frequencies command to produce graphical and numeric summaries of all five
possible DVs
Analyse descriptive stats frequencies
o Ask for all the 5 diff histogram characteristics
Pasted syntax created by filling out point and click dialogue boxes
Ask questions based on graphical summary
o Central tendency yes
o Variability yes
o Kurtosis mesokurtic
o Skewness no skew
o Modality unimodal
BIVARIATE
Create scatterplot and Pearson’s r in SPSS
Use SPSS point and click to produce scatterplot
Graphs (legacy dialogs) scatter simple define
Simple scatterplot put DV on Y axis, IV on X axis
Scatterplot (7 Assumptions)
1. Linear Relationship (straight line)
Increase or decrease in the same direction, and at the same rate
2. Monotonic Relationship
Increase or decrease in the same relative direction, but not at the same rate
3. Outliers
4. Gaps
5. Direction of the function that describes relationship between 2 variables
Positive, negative, or no relationship
6. Effect of X on Y (slope): the steeper the slope, the greater the effect
Do the points cluster around the imaginary straight line?
7. Correlation (strength of relationship)
±0.3 weak
±0.5 moderate
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±0.7 strong
Provided we are happy that all 7 steps are okay, we can appropriately summarise the relationship
numerically by calculating a (Pearson) correlation
Analyse Correlate Bivariate
Pearson correlation
Numeric summary table appears of Pearson’s correlation coefficient between the two
variables
-.5 = confirms negative relationship from scatterplot (found in Pearson correlation/SHTOT);
if they were both -1; all dots would be plotted on the line perfectly
o Also explains weak/moderate correlation
As a rule of thumb, say:
o Correlations between 0 and 0.29 (pos and neg) are ‘weak’
o Correlations between 0.30 and 0.59 are ‘moderate’
o Correlations between 0.60 and 1.00 are ‘strong’
Significance depends on two things
o The size of the relationship AND
o The sample size
Five points about correlations:
1. Note that SPSS reports that our correlation is significant ( p <0.0005)
Significance = population correlation is not equal to zero
If sig. 2 tailed states .000 = you cannot conclude that it is 0% as it may go beyond the
decimals
2. If asked, SPSS will always calculate a linear correlation even when appropriate to do so.
Always inspect bivariate scatterplot first to determine that a linear correlation is appropriate.
3. R = 0.00 does not always mean NO correlation, it means no LINEAR correlation
4. Always report ranges of X and Y we do not know what happens to relationship beyond
range of our data
5. Correlation does not imply causation
REGRESSION
Regression = prediction
F or t statistic represents the prediction (F = )
o If it is significant:
Write and interpret regression equation (of line going through scatterplot)
Comment on and interpret %
Check 4 assumptions of regression
Syntax SPSS:
REGRESSION /MISSING LISTWISE /STATISTICS COEFF OUTS R ANOVA
/CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT shtot /METHOD=ENTER age
1. Equation of the straight line
Unstandardised B coefficients conform to general equation of a line
Regression equation
o Predicted distress = y-axis intercept + slope (age/independent variable)
Y-axis intercept = the constant (identified from coefficient table); this number
will be the point where the line would cross the y-axis
Always make sure you write PREDICTED (as there can still be error)
Use info under B to write down those values into the equation
E.g. predicted distress = 38.25+(-2.62)(age)
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