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\author{jagglepop}
\pdfinfo{
  /Title (psych-2260.pdf)
  /Creator (Cheatography)
  /Author (jagglepop)
  /Subject (Psych 2260 Cheat Sheet)
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    {\parbox{\dimexpr\textwidth-2\fboxsep\relax}{\noindent
        \hspace*{-6pt}\includegraphics[width=5.8cm]{/web/www.cheatography.com/public/images/cheatography_logo.pdf}}
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\begin{tabulary}{11cm}{L}
    \vspace{-2pt}\large{\bf{\textcolor{DarkBackground}{\textrm{Psych 2260 Cheat Sheet}}}} \\
    \normalsize{by \textcolor{DarkBackground}{jagglepop} via \textcolor{DarkBackground}{\uline{cheatography.com/212492/cs/46171/}}}
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  \mymulticolumn{2}{p{5.377cm}}{\bf\textcolor{white}{Cheatographer}}  \\
  \vspace{-2pt}jagglepop \\
  \uline{cheatography.com/jagglepop} \\
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  \mymulticolumn{1}{p{5.377cm}}{\bf\textcolor{white}{Cheat Sheet}}  \\
   \vspace{-2pt}Not Yet Published.\\
   Updated 23rd April, 2025.\\
   Page {\thepage} of \pageref{LastPage}.
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  \mymulticolumn{1}{p{5.377cm}}{\bf\textcolor{white}{Sponsor}}  \\
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  \vspace{-5pt}
  %\includegraphics[width=48px,height=48px]{dave.jpeg}
  Measure your website readability!\\
  www.readability-score.com
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\end{multicols}}




\begin{document}
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\begin{multicols*}{4}

\begin{tabularx}{3.833cm}{x{1.7165 cm} x{1.7165 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{3.833cm}}{\bf\textcolor{white}{Ch. 3}}  \tn
% Row 0
\SetRowColor{LightBackground}
\textasciicircum{}{\emph{Correlation Coefficient:}} \# that tells degree of correlation {\bf{(r)}}\textasciicircum{} & \textasciicircum{}{\emph{Strength:}} small {\bf{±.10}} med {\bf{±.30}} large {\bf{±.50}}\textasciicircum{} \textasciicircum{}{\bf{r={[}Σ(z\textasciitilde{}x\textasciitilde{})(z\textasciitilde{}y\textasciitilde{}){]}/N-1}}\textasciicircum{} \tn 
% Row Count 5 (+ 5)
% Row 1
\SetRowColor{white}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Linear Correlation:}} Line indicating relation is roughly a straight line\textasciicircum{}} \tn 
% Row Count 7 (+ 2)
% Row 2
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Curvilinear correlation:}} Not Straight\textasciicircum{}} \tn 
% Row Count 8 (+ 1)
% Row 3
\SetRowColor{white}
\textasciicircum{}{\emph{Cross-product:}} Multiplying a score on one variable by a score on another\textasciicircum{} & \textasciicircum{}{\emph{Cross-product Z score: Using z-scores instead}}\textasciicircum{} \tn 
% Row Count 12 (+ 4)
% Row 4
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Variables:}} predictor is x and criterion is y\textasciicircum{}} \tn 
% Row Count 13 (+ 1)
% Row 5
\SetRowColor{white}
\textasciicircum{}{\emph{Prediction Model:}} Using z-scores to make predict\textasciicircum{} & \textasciicircum{}{\bf{Z\textasciitilde{}y\textasciitilde{}= (β)(Z\textasciitilde{}x\textasciitilde{})}}\textasciicircum{} \tn 
% Row Count 16 (+ 3)
% Row 6
\SetRowColor{LightBackground}
\textasciicircum{}{\emph{Raw Score Predict:}}\textasciicircum{} & \textasciicircum{}{\emph{Form 1:}} {\bf{Predicted Y=a+(b)(x)}}\textasciicircum{} \textasciicircum{}{\emph{Form 2:}} {\bf{Predicted Y=(SD\textasciitilde{}y\textasciitilde{})(Predicted Z\textasciitilde{}x\textasciitilde{})+M\textasciitilde{}y\textasciitilde{}}}\textasciicircum{} \tn 
% Row Count 21 (+ 5)
% Row 7
\SetRowColor{white}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Correlation Matrix:}} Table of correlations that's set up so each variable is listed down the left and across the top ex.\textasciicircum{}} \tn 
% Row Count 24 (+ 3)
% Row 8
\SetRowColor{LightBackground}
\textasciicircum{}{\emph{Multiple Regression:}} Making predictions w/ multi correlations\textasciicircum{} & \textasciicircum{}{\bf{Z\textasciitilde{}y\textasciitilde{}=(β\textasciitilde{}1\textasciitilde{})(Z\textasciitilde{}x1\textasciitilde{})+(β\textasciitilde{}2\textasciitilde{})(Z\textasciitilde{}x2\textasciitilde{})+(β\textasciitilde{}3\textasciitilde{})(Z\textasciitilde{}x3\textasciitilde{})...}}\textasciicircum{} \tn 
% Row Count 28 (+ 4)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{:}}\textasciicircum{}}  \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{3.833cm}{x{1.7165 cm} x{1.7165 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{3.833cm}}{\bf\textcolor{white}{Ch. 10}}  \tn
% Row 0
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Chi-Square Tests:}} For when the variable of interest is a nominal vari. The scores they achieve represent frequencies\textasciicircum{}} \tn 
% Row Count 3 (+ 3)
% Row 1
\SetRowColor{white}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Frequencies:}} How many ppl/observations fall into diff categories\textasciicircum{}} \tn 
% Row Count 5 (+ 2)
% Row 2
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Chi-Square Test for Goodness of Fit:}} Chi-Square test involving levels of a single nom vari\textasciicircum{}} \tn 
% Row Count 7 (+ 2)
% Row 3
\SetRowColor{white}
\textasciicircum{}{\emph{Goodness of Fit:}} {\bf{X2=∑(O-E)2/E}}\textasciicircum{} & \textasciicircum{}{\emph{**O- Observed Frequency E- Expected Frequency }}\textasciicircum{} \tn 
% Row Count 10 (+ 3)
% Row 4
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{df for X2 test:}} {\bf{df=NCategories-1}}\textasciicircum{}} \tn 
% Row Count 11 (+ 1)
% Row 5
\SetRowColor{white}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Chi-Square T for Independence:}} Chi-Square test involving 2 variables each w/ several categories\textasciicircum{}} \tn 
% Row Count 13 (+ 2)
% Row 6
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Independence:}} Refers to a lack of a relation between 2 nom vari\textasciicircum{}} \tn 
% Row Count 15 (+ 2)
% Row 7
\SetRowColor{white}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Ind Means X2 Expected frequencies:}} Makes \# that rep cell {\bf{E=(R/N)(C)}}\textasciicircum{}} \tn 
% Row Count 17 (+ 2)
% Row 8
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Figuring X2 for Ind:}} It is the same as goodness of fit but uses scores from each cell of the contingency table\textasciicircum{}} \tn 
% Row Count 20 (+ 3)
% Row 9
\SetRowColor{white}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{df for X2 for Ind:}} {\bf{df=(NColumns-1)(NRows-1)}}\textasciicircum{}} \tn 
% Row Count 22 (+ 2)
% Row 10
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{For cutoff scores:}} use {\bf{table A4}} to find cutoff scores\textasciicircum{}} \tn 
% Row Count 24 (+ 2)
% Row 11
\SetRowColor{white}
\textasciicircum{}{\emph{Phi Coefficient():}} Measure of association between to dichotomous nom vari. Effect size for a X2 for Ind w/ a 2x2 contingency table\textasciicircum{} & \textasciicircum{}{\bf{=√X2/N}}\textasciicircum{} \tn 
% Row Count 31 (+ 7)
\end{tabularx}
\par\addvspace{1.3em}

\vfill
\columnbreak
\begin{tabularx}{3.833cm}{x{1.7165 cm} x{1.7165 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{3.833cm}}{\bf\textcolor{white}{Ch. 10 (cont)}}  \tn
% Row 12
\SetRowColor{LightBackground}
\textasciicircum{}{\emph{Cramer's Phi:}} Extension of Phi, used when the contingency table is larger than 2x2 AKA Cramer's V and denoted as {\bf{C}} or {\bf{Vc}}\textasciicircum{} & \textasciicircum{}{\bf{C=√X2/(N)(dfSmaller)}}\textasciicircum{} \tn 
% Row Count 7 (+ 7)
% Row 13
\SetRowColor{white}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Data Transformation:}} Math proc used on each score is a samp, usuall done to make samp dist closer to norm\textasciicircum{}} \tn 
% Row Count 10 (+ 3)
% Row 14
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Square-Root Transformation:}} Taking the √ of each score in a sample to make the distribution closer to normal\textasciicircum{}} \tn 
% Row Count 13 (+ 3)
% Row 15
\SetRowColor{white}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Log Transformation:}} Taking a logarithm of each score to make the samp dist closer to norm\textasciicircum{}} \tn 
% Row Count 15 (+ 2)
% Row 16
\SetRowColor{LightBackground}
\textasciicircum{}{\emph{Rank-Order Transformation:}} Changing the set of scores to ranks so that the lowest score is 1, next lowest is 2... so on\textasciicircum{} & \textasciicircum{}{\emph{Rank-Order Test:}} Hyp Test proc that uses rank-ordered scores. Sometimes called dist-free \seqsplit{tests/non-parametric} tests\textasciicircum{} \tn 
% Row Count 22 (+ 7)
% Row 17
\SetRowColor{white}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Rank-Order Tests Corresponding to Parametric Tests:}}\textasciicircum{}} \tn 
% Row Count 24 (+ 2)
% Row 18
\SetRowColor{LightBackground}
\textasciicircum{}{\emph{Mann-Whitney U:}} Rank-order test {\bf{U1={[}(N1)(N2){]}+{[}N1(N1+1)/2)-∑R1}}  // {\bf{U2={[}(N1)(N2){]}+{[}N2(N2+1)/2)-∑R2}}\textasciicircum{} & \textasciicircum{}{\emph{Where:}} U1/U2- U1 Stat N1/N2- Sample size of each group ∑R1/R2- Sum of rank orders for each condition\textasciicircum{} \tn 
% Row Count 30 (+ 6)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{:}}\textasciicircum{} √ σ μ ∑}  \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{3.833cm}{x{1.7165 cm} x{1.7165 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{3.833cm}}{\bf\textcolor{white}{Ch. 3}}  \tn
% Row 0
\SetRowColor{LightBackground}
\textasciicircum{}{\emph{Correlation Coefficient:}} \# that tells degree of correlation {\bf{(r)}}\textasciicircum{} & \textasciicircum{}{\emph{Strength:}} small {\bf{±.10}} med {\bf{±.30}} large {\bf{±.50}}\textasciicircum{} \tn 
% Row Count 4 (+ 4)
% Row 1
\SetRowColor{white}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Linear Correlation:}} Line indicating relation is roughly a straight line\textasciicircum{}} \tn 
% Row Count 6 (+ 2)
% Row 2
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Curvilinear correlation:}} Not Straight\textasciicircum{}} \tn 
% Row Count 7 (+ 1)
% Row 3
\SetRowColor{white}
\textasciicircum{}{\emph{Cross-product:}} Multiplying a score on one variable by a score on another\textasciicircum{} & \textasciicircum{}{\emph{Cross-product Z score: Using z-scores instead}}\textasciicircum{} \tn 
% Row Count 11 (+ 4)
% Row 4
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Variables:}} predictor is x and criterion is y\textasciicircum{}} \tn 
% Row Count 12 (+ 1)
% Row 5
\SetRowColor{white}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Prediction Model:}} Using z-scores to make predict\textasciicircum{}} \tn 
% Row Count 14 (+ 2)
% Row 6
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{} \tn 
% Row Count 14 (+ 0)
% Row 7
\SetRowColor{white}
Formulas & \textasciicircum{}{\bf{r={[}Σ(z\textasciitilde{}x\textasciitilde{})(z\textasciitilde{}y\textasciitilde{}){]}/N-1}}\textasciicircum{}\textasciicircum{}{\bf{Z\textasciitilde{}y\textasciitilde{}= (β)(Z\textasciitilde{}x\textasciitilde{})}}\textasciicircum{} \tn 
% Row Count 17 (+ 3)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{:}}\textasciicircum{}}  \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{3.833cm}{x{1.7165 cm} x{1.7165 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{3.833cm}}{\bf\textcolor{white}{Ch. 3}}  \tn
% Row 0
\SetRowColor{LightBackground}
\textasciicircum{}{\emph{Correlation Coefficient:}} \# that tells degree of correlation {\bf{(r)}}\textasciicircum{} & \textasciicircum{}{\emph{Strength:}} small {\bf{±.10}} med {\bf{±.30}} large {\bf{±.50}}\textasciicircum{} \tn 
% Row Count 4 (+ 4)
% Row 1
\SetRowColor{white}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Linear Correlation:}} Line indicating relation is roughly a straight line\textasciicircum{}} \tn 
% Row Count 6 (+ 2)
% Row 2
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Curvilinear correlation:}} Not Straight\textasciicircum{}} \tn 
% Row Count 7 (+ 1)
% Row 3
\SetRowColor{white}
\textasciicircum{}{\emph{Cross-product:}} Multiplying a score on one variable by a score on another\textasciicircum{} & \textasciicircum{}{\emph{Cross-product Z score: Using z-scores instead}}\textasciicircum{} \tn 
% Row Count 11 (+ 4)
% Row 4
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Variables:}} predictor is x and criterion is y\textasciicircum{}} \tn 
% Row Count 12 (+ 1)
% Row 5
\SetRowColor{white}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Prediction Model:}} Using z-scores to make predict\textasciicircum{}} \tn 
% Row Count 14 (+ 2)
% Row 6
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{} \tn 
% Row Count 14 (+ 0)
% Row 7
\SetRowColor{white}
Formulas & \textasciicircum{}{\bf{r={[}Σ(z\textasciitilde{}x\textasciitilde{})(z\textasciitilde{}y\textasciitilde{}){]}/N-1}}\textasciicircum{}\textasciicircum{}{\bf{Z\textasciitilde{}y\textasciitilde{}= (β)(Z\textasciitilde{}x\textasciitilde{})}}\textasciicircum{} \tn 
% Row Count 17 (+ 3)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{:}}\textasciicircum{}}  \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{3.833cm}{x{1.7165 cm} x{1.7165 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{3.833cm}}{\bf\textcolor{white}{Ch. 4}}  \tn
% Row 0
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Inferential Statistics:}} Conclusions that go beyond the particular group of research participants studied\textasciicircum{}} \tn 
% Row Count 3 (+ 3)
% Row 1
\SetRowColor{white}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Normal curve/dis:}} Variables follow a unimodal, roughly symmetrical, bell-shaped dist\textasciicircum{}} \tn 
% Row Count 5 (+ 2)
% Row 2
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Central Limit Theorem:}} Principle that the distribution of the sums/means of scores taken at random from any dist. of indiv. will tend to form norm curve\textasciicircum{}} \tn 
% Row Count 9 (+ 4)
% Row 3
\SetRowColor{white}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Haphazard Selection:}} Picking for convenience (Ie, whoever happens to be available)\textasciicircum{}} \tn 
% Row Count 11 (+ 2)
% Row 4
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Population Parameters:}} M, SD2 and SD of a pop\textasciicircum{}} \tn 
% Row Count 12 (+ 1)
% Row 5
\SetRowColor{white}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Sample Stats:}} M, SD2 and SD figured for scores in a sample\textasciicircum{}} \tn 
% Row Count 14 (+ 2)
% Row 6
\SetRowColor{LightBackground}
\textasciicircum{}{\emph{Relative Freq:}} \# of times smt happens relative to \# it could happen\textasciicircum{} & \textasciicircum{}{\emph{Probability:}} {\bf{p=Possible successful outcomes/All possible outcomes}}\textasciicircum{} \tn 
% Row Count 18 (+ 4)
% Row 7
\SetRowColor{white}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Response rate:}} Proportion of individuals approached for the study who actually participated in the study \textasciicircum{}} \tn 
% Row Count 21 (+ 3)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{:}}\textasciicircum{}}  \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{3.833cm}{x{1.51052 cm} x{1.92248 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{3.833cm}}{\bf\textcolor{white}{Ch. 5}}  \tn
% Row 0
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Theory:}} Set of priciples that attempt to explain 1+ facts/relationships/events\textasciicircum{}} \tn 
% Row Count 2 (+ 2)
% Row 1
\SetRowColor{white}
\textasciicircum{}{\emph{Hypothesis testing process:}}\textasciicircum{} & \textasciicircum{}Step 1- Restate Question (research/null hypotheses?) Step 2- Determine chara of comparison distribution Step 3- Determine cutoff sample score Step 4- Determine samples score on the comparison distribution Step 5- Decide whether or not to accept/reject the null hypothesis\textasciicircum{} \tn 
% Row Count 15 (+ 13)
% Row 2
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Comparison Distribution:}} Represents the population situation if the null hypothesis is true\textasciicircum{}} \tn 
% Row Count 17 (+ 2)
% Row 3
\SetRowColor{white}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Meta-analysis:}} Combo of results from multiple diff studies\textasciicircum{}} \tn 
% Row Count 19 (+ 2)
% Row 4
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Directional Hypothesis:}} Study that focuses on a specific direction of effect\textasciicircum{}} \tn 
% Row Count 21 (+ 2)
% Row 5
\SetRowColor{white}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Decision Errors:}} Correct procedures leading to faulty results\textasciicircum{}} \tn 
% Row Count 23 (+ 2)
% Row 6
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Type I Error:}} Conclude the study supports research hypothesis when it is actually is false\textasciicircum{}} \tn 
% Row Count 25 (+ 2)
% Row 7
\SetRowColor{white}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Type II Error:}} Extreme p-value that leads to rejecting a null hypothesis that should actually be accepted\textasciicircum{}} \tn 
% Row Count 28 (+ 3)
% Row 8
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Not Significant:}} NS\textasciicircum{}} \tn 
% Row Count 29 (+ 1)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{3.833cm}{x{1.7165 cm} x{1.7165 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{3.833cm}}{\bf\textcolor{white}{Ch. 8}}  \tn
% Row 0
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{T test for independent means:}} using scores obtained from 2 sep groups that're indep of each other\textasciicircum{}} \tn 
% Row Count 3 (+ 3)
% Row 1
\SetRowColor{white}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Distribution between means:}} comp dist used in a t test for ind M. We are not using diff scores and are instead comp 1 groups M to the other groups M\textasciicircum{}} \tn 
% Row Count 7 (+ 4)
% Row 2
\SetRowColor{LightBackground}
\textasciicircum{}{\emph{Weighted Avg:}} An average weighted by the amount of info that each sample provides\textasciicircum{} & \textasciicircum{}{\emph{Pooled estimate of pop SD2:}} {\bf{S2Pooled={[}(df1/dftotal)(S12){]}+{[}(df2/dftotal)(S22){]}}}\textasciicircum{} \tn 
% Row Count 12 (+ 5)
% Row 3
\SetRowColor{white}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{SD2 of dist of diff between Ms:}} For pop1: {\bf{SM12=S2Pooled/N1}} For pop2: {\bf{SM22=S2Pooled/N2}}\textasciicircum{}} \tn 
% Row Count 14 (+ 2)
% Row 4
\SetRowColor{LightBackground}
\textasciicircum{}{\emph{SD2 of dist of diff between Ms: S2Difference}}\textasciicircum{} & \textasciicircum{}{\bf{S2Difference=SM12+SM22}}\textasciicircum{} \tn 
% Row Count 17 (+ 3)
% Row 5
\SetRowColor{white}
\textasciicircum{}{\emph{SD of the dist of diff between Ms:}} SDifference\textasciicircum{} & \textasciicircum{}{\bf{SDifference=√S2Difference}}\textasciicircum{} \tn 
% Row Count 20 (+ 3)
% Row 6
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Df for ttest for for ind M:}} {\bf{dftotal=df1+df2}}\textasciicircum{}} \tn 
% Row Count 22 (+ 2)
% Row 7
\SetRowColor{white}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{ttest for ind M:}} {\bf{t=(M1-M2)/SDiffference}}\textasciicircum{}} \tn 
% Row Count 23 (+ 1)
% Row 8
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Hyp Test Proc:}} {\bf{Find S12+S22-\textgreater{}S2Pooled-\textgreater{}SM12+SM22-\textgreater{}S2Difference-\textgreater{}SDifference-\textgreater{}Cutoff-\textgreater{}M1+M2-\textgreater{}t}}\textasciicircum{}} \tn 
% Row Count 26 (+ 3)
% Row 9
\SetRowColor{white}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Effect Size for IndM T:}} {\bf{Est Eff Size=(M1-M2)/SPooled}}\textasciicircum{}} \tn 
% Row Count 28 (+ 2)
% Row 10
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Harmonic M: Gives equivalent sample size to groups that have equal group sizes (used for est eff size when group sizes aren't even)}} {\bf{Harmonic M={[}(2)(N1)(N2){]}/(N1+N2)}}\textasciicircum{}} \tn 
% Row Count 32 (+ 4)
\end{tabularx}
\par\addvspace{1.3em}

\vfill
\columnbreak
\begin{tabularx}{3.833cm}{x{1.7165 cm} x{1.7165 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{3.833cm}}{\bf\textcolor{white}{Ch. 8 (cont)}}  \tn
% Row 11
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{t test shown in research:}} t(dftotal)=(tscore), p\textless{}.01 \textasciicircum{}} \tn 
% Row Count 2 (+ 2)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{:}}\textasciicircum{} √ σ μ ∑}  \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{3.833cm}{x{1.7165 cm} x{1.7165 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{3.833cm}}{\bf\textcolor{white}{Ch. 6}}  \tn
% Row 0
\SetRowColor{LightBackground}
\textasciicircum{}{\emph{Distribution of Means {\bf{(DoM)}}:}} The distribution of the means of each of many samples of = size and all randomly selected from the same population\textasciicircum{} & \textasciicircum{}{\emph{3 Chara of DoM:}} 1. Its M 2. Its spread (SD2+SD) 3. Its shape\textasciicircum{} \tn 
% Row Count 8 (+ 8)
% Row 1
\SetRowColor{white}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Rules:}} Rule 1- PopMm (M of DoM)=PopM (M of pop) Rule 2a- {\bf{Pop SD2M=SD2/N}} Rule 2b- Pop SDM=√SD2M Rule 3- The shape of a DoM is approx norm if either a) Each sample has 30+ part b) The dist of the pop of indiv is norm\textasciicircum{}} \tn 
% Row Count 13 (+ 5)
% Row 2
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Z Test:}} The Z score that is checked against the normal curve\textasciicircum{}} \tn 
% Row Count 15 (+ 2)
% Row 3
\SetRowColor{white}
\textasciicircum{}{\emph{Effect Size:}} The amount that pops (exp and non exp) are separated/don't overlap\textasciicircum{} & \textasciicircum{}{\emph{Cohen's d:}} d=(μ1 {\bf{(M of exp group)}}-μ2{\bf{(M of known pop)}})/σ {\bf{(SD of known pop)}}\textasciicircum{} \tn 
% Row Count 20 (+ 5)
% Row 4
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{d effect size:}} {\bf{small}} 0\textless{}d\textless{}0.2 {\bf{med}} 0.2\textless{}d\textless{}0.8 {\bf{large}} d\textgreater{}0.8\textasciicircum{}} \tn 
% Row Count 22 (+ 2)
% Row 5
\SetRowColor{white}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Type I Error:}} Rejecting the null hypothesis when the null hypothesis is actually true\textasciicircum{}} \tn 
% Row Count 24 (+ 2)
% Row 6
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Type II Error:}} Accepting the null hypothesis when the null hypothesis is false, aka beta error\textasciicircum{}} \tn 
% Row Count 26 (+ 2)
% Row 7
\SetRowColor{white}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Type III Error:}} Concluding that there is a sig diff in one direction when the true effect is in the other direction\textasciicircum{}} \tn 
% Row Count 29 (+ 3)
% Row 8
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Statistical Power:}} Likelihood that a study will correctly detect a real treatment effect. In other words, the stat pow is the likelihood that the study will correctly reject a null hypothesis\textasciicircum{}} \tn 
% Row Count 33 (+ 4)
\end{tabularx}
\par\addvspace{1.3em}

\vfill
\columnbreak
\begin{tabularx}{3.833cm}{x{1.7165 cm} x{1.7165 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{3.833cm}}{\bf\textcolor{white}{Ch. 6 (cont)}}  \tn
% Row 9
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Hypothesis testing steps:}} {\bf{Step 1-}} Develop Hypothesis ie- H0: μ1≤μ2 H1: μ\textgreater{}μ2 {\bf{Step 2-}} Determine chara of comp pop σM=σ/√N {\bf{Step 3-}} Determine cutoff score {\bf{Step 4-}} Determine samples score on the comp dist Z=(M-μM)/σM {\bf{Step 5:}} Decide whether to reject or accept the null hypothesis\textasciicircum{}} \tn 
% Row Count 7 (+ 7)
% Row 10
\SetRowColor{white}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Power Distribution Steps:}} {\bf{Step 1:}} Turn Z cutoff score into raw score M=(Z)(σM)+μM {\bf{Step 2:}} Figure the zscore for the cuttoff M, Z=(M-μM)/(σM) {\bf{Step 3:}} Use Table A-1 to determine prob of getting the resulting score from step 2 Power=1-beta\textasciicircum{}} \tn 
% Row Count 13 (+ 6)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{:}}\textasciicircum{} √ σ μ}  \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{3.833cm}{x{1.7165 cm} x{1.7165 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{3.833cm}}{\bf\textcolor{white}{Ch. 7}}  \tn
% Row 0
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{T Tests:}} Hyp test procedures where pop SD2 is unknown(Aka students t)\textasciicircum{}} \tn 
% Row Count 2 (+ 2)
% Row 1
\SetRowColor{white}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{1 sample t test:}} scores from one sample where the comp pop has a known M but unknown SD2\textasciicircum{}} \tn 
% Row Count 4 (+ 2)
% Row 2
\SetRowColor{LightBackground}
\textasciicircum{}{\emph{1 samp t hyp test:}} In {\bf{step 2}} we have to find the unbiased estimate of the pop SD2 {\bf{S2={[}∑(X-M)2{]}/df}}, in {\bf{step 3}} we use table A-2 instead and for {\bf{step 4}} we need to calculate a t-score {\bf{t=(M-Pop M)/SM}} to compare against our cutoff score\textasciicircum{} & \textasciicircum{}{\emph{Degrees of Freedom:{\bf{df=n-1}}}}\textasciicircum{} \tn 
% Row Count 17 (+ 13)
% Row 3
\SetRowColor{white}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Repeated-Measures design:}} Research situation where 2 scores are taken from each person in the sample (within-subjects design)\textasciicircum{}} \tn 
% Row Count 20 (+ 3)
% Row 4
\SetRowColor{LightBackground}
\textasciicircum{}{\emph{t test for dependent means:}} Each person has 2 scores, we use diff scores for the participants (1 score-the other) and we assume pop M is 0\textasciicircum{} & \textasciicircum{}{\emph{For the t test for dep M, calculate diff scores before doing hyp test}}\textasciicircum{} \tn 
% Row Count 28 (+ 8)
% Row 5
\SetRowColor{white}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Est. Effect Size (for t test w dep M):}} Mean of diff scores/sd of pop of diff scores {\bf{Est Eff Size=M/S}}\textasciicircum{}} \tn 
% Row Count 31 (+ 3)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{:}}\textasciicircum{} √ σ μ ∑}  \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{3.833cm}{x{1.7165 cm} x{1.7165 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{3.833cm}}{\bf\textcolor{white}{Ch. 9}}  \tn
% Row 0
\SetRowColor{LightBackground}
\textasciicircum{}{\emph{ANOVA:}} Stat procedurefor testing SD2 among the Ms of \textgreater{}2 groups\textasciicircum{} & \textasciicircum{}{\emph{The null hyp for anova is that the several pops being compared have the same M}}\textasciicircum{} \tn 
% Row Count 5 (+ 5)
% Row 1
\SetRowColor{white}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Within-group est of the pop SD2:}} Avging pop SD2 est from each sample into a single pooled est. Gives an avg of est figured entirely from the scores within each of the samp\textasciicircum{}} \tn 
% Row Count 9 (+ 4)
% Row 2
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Between-group est of the pop SD2:}} Est of the SD2 in each pop from the SD2 among the Ms of the samples\textasciicircum{}} \tn 
% Row Count 12 (+ 3)
% Row 3
\SetRowColor{white}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Treatment effect:}} Diff treatment received by the groups causes the groups to have diff Ms\textasciicircum{}} \tn 
% Row Count 14 (+ 2)
% Row 4
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{F Ratio:}} The between-groups est divided by the within-groups est\textasciicircum{}} \tn 
% Row Count 16 (+ 2)
% Row 5
\SetRowColor{white}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{F Distribution:}} Math defined curve that is the comp dist used in an ANOVA\textasciicircum{}} \tn 
% Row Count 18 (+ 2)
% Row 6
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Before testing, find M and S2 for each group of part}}\textasciicircum{}} \tn 
% Row Count 20 (+ 2)
% Row 7
\SetRowColor{white}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Within-groups SD2 est:}} {\bf{S2Within=(S12+S22+...Slast2)/NGroups}}\textasciicircum{}} \tn 
% Row Count 22 (+ 2)
% Row 8
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Grand M:}} The overall M of all our scores {\bf{GM=∑M/NGroups}}\textasciicircum{}} \tn 
% Row Count 24 (+ 2)
% Row 9
\SetRowColor{white}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Est of SD2 of the Dist of Ms:}} {\bf{SM2={[}∑(M-GM)2{]}/dfbetween}}\textasciicircum{}} \tn 
% Row Count 26 (+ 2)
% Row 10
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Comparison of fig the SD2 of a dist of Ms from the SD2 of a dist of indiv:}} from dist of indiv-\textgreater{}dist of M - {\bf{S2M=S2/N}} dist of M-\textgreater{}Dist of indiv - {\bf{S2Between=(S2M)(N)}}\textasciicircum{}} \tn 
% Row Count 30 (+ 4)
\end{tabularx}
\par\addvspace{1.3em}

\vfill
\columnbreak
\begin{tabularx}{3.833cm}{x{1.7165 cm} x{1.7165 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{3.833cm}}{\bf\textcolor{white}{Ch. 9 (cont)}}  \tn
% Row 11
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{F Ratio:}} Ratio of between-group est of pop SD2 to the within-group est of pop SD2 {\bf{F=S2Between/S2Within}} and use {\bf{table A-3}} for comp\textasciicircum{}} \tn 
% Row Count 3 (+ 3)
% Row 12
\SetRowColor{white}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Between-groups df:}} Numerator df {\bf{dfBetween=NGroups-1}}\textasciicircum{}} \tn 
% Row Count 5 (+ 2)
% Row 13
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Within-groups df:}} Denominator df {\bf{dfWithin=df1+df2+...dfLast}}\textasciicircum{}} \tn 
% Row Count 7 (+ 2)
% Row 14
\SetRowColor{white}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Hyp Test Proc:}} {\bf{Find S2 + M for each group-\textgreater{}S2Within-\textgreater{}GM-\textgreater{}dfBetween-\textgreater{}dfWithin-\textgreater{}S2M-\textgreater{}S2Between-\textgreater{}F}}\textasciicircum{}} \tn 
% Row Count 10 (+ 3)
% Row 15
\SetRowColor{LightBackground}
\textasciicircum{}{\emph{Effect size for ANOVA:}} {\bf{R2}}\textasciicircum{} & \textasciicircum{}{\bf{R2={[}(S2Between)(dfBetween){]}/{[}(S2Between)(dfBetween){]}+{[}(S2Within)(dfWithin){]}}}\textasciicircum{} \tn 
% Row Count 15 (+ 5)
% Row 16
\SetRowColor{white}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{R2 Power Meaning:}} {\bf{small}} .01 {\bf{med}} .06 {\bf{large}} .14\textasciicircum{}} \tn 
% Row Count 17 (+ 2)
% Row 17
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Factorial ANOVA:}} ANOVA for factorial research design\textasciicircum{}} \tn 
% Row Count 19 (+ 2)
% Row 18
\SetRowColor{white}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Interaction Effect:}} X = interaction (effect of one variable impacts the results on the other)\textasciicircum{}} \tn 
% Row Count 21 (+ 2)
% Row 19
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Two-way ANOVA:}} Considers the effect of 2 variables that separate groups\textasciicircum{}} \tn 
% Row Count 23 (+ 2)
% Row 20
\SetRowColor{white}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Grouping Variables/Ind Variables:}} Variables that separate groups\textasciicircum{}} \tn 
% Row Count 25 (+ 2)
% Row 21
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{One-Way ANOVA: Consider the effect of only one grouping}}\textasciicircum{}} \tn 
% Row Count 27 (+ 2)
% Row 22
\SetRowColor{white}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Diff ANOVA Means:}} {\bf{Cell Ms-}} M of scores in each cell {\bf{Marginal Ms-}} M of 1 grouping variable (vertical/horizontal grouping)\textasciicircum{}} \tn 
% Row Count 30 (+ 3)
\end{tabularx}
\par\addvspace{1.3em}

\vfill
\columnbreak
\begin{tabularx}{3.833cm}{x{1.7165 cm} x{1.7165 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{3.833cm}}{\bf\textcolor{white}{Ch. 9 (cont)}}  \tn
% Row 23
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{Dependent Variable:}} Represents the effect of the exper proc\textasciicircum{}} \tn 
% Row Count 2 (+ 2)
% Row 24
\SetRowColor{white}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{One-Way ANOVA in Research:}} {\bf{Ftest(dfBetween, dfWithin)=F ratio score, p\textless{}.01}}\textasciicircum{}} \tn 
% Row Count 4 (+ 2)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{3.833cm}}{\textasciicircum{}{\emph{:}}\textasciicircum{} √ σ μ ∑}  \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}


% That's all folks
\end{multicols*}

\end{document}