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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;
â€¢ Step 1: understand the research question
â€¢ Step 2: how are DV and IV measured?
â€¢ Step 3: choose method of analysis (correlation/regression)
Conduct correlation/regression analysis;
â€¢ Part 1: univariate
â€¢ Part 2: bivariate
â€¢ Part 3: regression and check assumptions
â†’ Correlation asks is there a RELATIONSHIP between two variables
â†’ Regression refers to PREDICTION
UNIVARIATE
Step 1: understand research question
Example:
â€¢ IV = age of carer
â€¢ DV = carer distress
â€¢ 1. What is the level of carer distress? Does carer distress vary? (univariate)
o Produce descriptive statistics
â€¢ 2. Is carer distress (linearly) related to age of carer? (bivariate)
o Produce correlational statistics
â€¢ 3. Does knowledge about age help predict level of carer distress? (regression)
o Produce regression
â€¢ This is NON-EXPERIMENTAL research
Step 2: how are the DV/IV measured?
â€¢ We look at
o How we intended to measure (questionnaire)
o What we actually ended up with (results â€“ data)
â€¢ Decide level of measurement â€“ categorical, ordinal or interval
â€¢ Then we need this to decide what statistical analysis to use
â€¢ This leads to either correlation/regression = IV/DV continuous/interval/numeric and normal
â€¢ 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
â€¢ Specific Health Questionnaire; consisted of 15 items
o Study had age categories (one of 9) â€“ but we are going to treat them as continuous
variables (numeric)
Step 3: choose method of analysis
â€¢ Produce histograms and describe them based on 5 assumptions
Assumptions of normality:
â€¢ Central tendency
o Typical or average score, centre of distribution, peak in the distribution â€“ does it exist?
â€¢ 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 â€“
width of distribution
â€¢ 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)
â€¢ 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
â–ª Between -2 and +2 = mesokurtic
â€¢ 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 â€“ indicates homogeneity
USING SPSS
â€¢ Use the frequencies command to produce graphical and numeric summaries of all five
possible DVs
â€¢ Analyse â†’ descriptive stats â†’ frequencies
o Ask for all the 5 diff histogram characteristics
â€¢ Pasted syntax created by filling out point and click dialogue boxes
â€¢ Ask questions based on graphical summary
o Central tendency â€“ yes
o Variability â€“ yes
o Kurtosis â€“ mesokurtic
o Skewness â€“ no skew
o Modality â€“ unimodal
BIVARIATE
â€¢ Create scatterplot and Pearsonâ€™s r in SPSS
Use SPSS point and click to produce scatterplot
â€¢ Graphs (legacy dialogs) â†’ scatter â†’ simple â†’ define
â€¢ Simple scatterplot â†’ put DV on Y axis, IV on X axis
Scatterplot (7 Assumptions)
1. Linear Relationship (straight line)
â€¢ Increase or decrease in the same direction, and at the same rate
2. Monotonic Relationship
â€¢ 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
â€¢ Positive, negative, or no relationship
6. Effect of X on Y (slope): the steeper the slope, the greater the effect
â€¢ Do the points cluster around the imaginary straight line?
7. Correlation (strength of relationship)
â€¢ Â±0.3 â€“ weak
â€¢ Â±0.5 â€“ moderate
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â€¢ Â±0.7 â€“ strong
â†’ Provided we are happy that all 7 steps are okay, we can appropriately summarise the relationship
numerically by calculating a (Pearson) correlation
Analyse â†’ Correlate â†’ Bivariate
â€¢ Pearson correlation
â€¢ Numeric summary table appears of Pearsonâ€™s correlation coefficient between the two
variables
â€¢ -.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
â€¢ As a rule of thumb, say:
o Correlations between 0 and 0.29 (pos and neg) are â€˜weakâ€™
o Correlations between 0.30 and 0.59 are â€˜moderateâ€™
o Correlations between 0.60 and 1.00 are â€˜strongâ€™
â€¢ Significance depends on two things
o The size of the relationship AND
o The sample size
1. Note that SPSS reports that our correlation is significant ( p <0.0005)
â€¢ Significance = population correlation is not equal to zero
â€¢ 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 â€“ we do not know what happens to relationship beyond
range of our data
5. Correlation does not imply causation
REGRESSION
â€¢ Regression = prediction
â€¢ F or t statistic represents the prediction (F = ï¿½î¬¶)
o If it is significant:
â–ª Write and interpret regression equation (of line going through scatterplot)
â–ª Comment on and interpret îœ´î¬¶%
â–ª 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
â€¢ Unstandardised B coefficients â†’ conform to general equation of a line
â€¢ Regression equation
o Predicted distress = y-axis intercept + slope (age/independent variable)
â–ª Y-axis intercept = the constant (identified from coefficient table); this number
will be the point where the line would cross the y-axis
â–ª Always make sure you write PREDICTED (as there can still be error)
â–ª Use info under B to write down those values into the equation
â–ª E.g. predicted distress = 38.25+(-2.62)(age)
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