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\author{Robyn.jll}
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  /Subject (Psychological Statistics Cheat Sheet)
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    \vspace{-2pt}\large{\bf{\textcolor{DarkBackground}{\textrm{Psychological Statistics Cheat Sheet}}}} \\
    \normalsize{by \textcolor{DarkBackground}{Robyn.jll} via \textcolor{DarkBackground}{\uline{cheatography.com/146401/cs/31664/}}}
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  \mymulticolumn{1}{p{5.377cm}}{\bf\textcolor{white}{Cheat Sheet}}  \\
   \vspace{-2pt}Not Yet Published.\\
   Updated 13th April, 2022.\\
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\begin{multicols*}{3}

\begin{tabularx}{5.377cm}{x{2.04057 cm} x{2.93643 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{First look at the Data}}  \tn
% Row 0
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Population & entire group that you want to draw conclusions about. \tn 
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Sample & he specific group that you will collect data from. The size of the sample is always less than the total size of the population \tn 
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Mean & average (μ mean of population; x̄ mean of sample) \tn 
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Median & separates the sample (Mittelpunkt) \tn 
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Mode & highest score \tn 
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Variance & measures dispersion around the mean \tn 
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Standart Deviation (SD) & estimates the SD of the sampling distribution \tn 
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 & FORMULA \tn 
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Standard Error & Square root of the variance (σ SD of population; s SD of sample) \tn 
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 & s/√n \tn 
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Confidence Intervalls (CI) & This is the range of values you expect your estimate to fall between if you redo your test, within a certain level of confidence. Confidence, in statistics, is another way to describe probability \tn 
% Row Count 33 (+ 9)
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\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{First look at the Data (cont)}}  \tn
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Quantitative data & s expressed in numbers and graphs and is analyzed through statistical methods. \tn 
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Qualitative data & is expressed in words and analyzed through interpretations and categorizations. \tn 
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\mymulticolumn{2}{x{5.377cm}}{{\bf{Hypothesis Testing}}} \tn 
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H0 & the null hypothesis of a test always predicts no effect or no relationship between variables \tn 
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H1 & alternative hypothesis states your research prediction of an effect or relationship \tn 
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\mymulticolumn{2}{x{5.377cm}}{{\bf{Randomisation}}} \tn 
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completely randomized design & every subject is assigned to a treatment group at random. \tn 
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 & Ex. Subjects are all randomly assigned a level of phone use using a random number generator. \tn 
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randomized block design & subjects are first grouped according to a characteristic they share, and then randomly assigned to treatments within those groups \tn 
% Row Count 31 (+ 6)
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\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{First look at the Data (cont)}}  \tn
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 & Ex. Subjects are first grouped by age, and then phone use treatments are randomly assigned within these groups. \tn 
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\mymulticolumn{2}{x{5.377cm}}{{\bf{Between-subjects vs. within-subjects}}} \tn 
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\seqsplit{between-subjects} design & AKA independent measures design or classic ANOVA design \tn 
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 & individuals receive only one of the possible levels of an experimental treatment. \tn 
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 & EX. Subjects are randomly assigned a level of phone use (none, low, or high) and follow that level of phone use throughout the experiment. \tn 
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within-subjects design & AKA repeated measures design \tn 
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 & every individual receives each of the experimental treatments consecutively, and their responses to each treatment are measured. \tn 
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 & EX. Subjects are assigned consecutively to zero, low, and high levels of phone use throughout the experiment, and the order in which they follow these treatments is randomized. \tn 
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\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Different Scales of Measurement}}  \tn
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Nominal Categories & do not correspond to numerical value \tn 
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 & Ex. British Team, German Team, ... \tn 
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Ordinal Measurement or Ranks & scores can be ordered from smallest to largest, only a rank order is implied \tn 
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 & Ex. 1st, 2nd, 3rd, ... \tn 
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Interval Measurement & size of the difference between scores is an indication of magnitude \tn 
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 & Ex. Bill was 5 seconds behind the winner, ... (equal interval scale of measurement - interval of 1 second) \tn 
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Ratio Measurement & like Interval Measurement, but allows ratios to be meaningfully calculated between scores \tn 
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 & Ex. Tom took 50 seconds and Bill took 100 seconds -\textgreater{} Tom is twice as fast as Bill \tn 
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\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Types of Variables}}  \tn
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Dependent Variable & Variables that represent the outcome of the experiment. \tn 
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 & Ex. Any measurement of plant health and growth: in this case, plant height and wilting. \tn 
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Independent Variable & Variables you manipulate in order to affect the outcome of an experiment \tn 
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 & Ex. The amount of salt added to each plant's water. \tn 
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Controlled Variable & Variables that are held constant throughout the experiment. \tn 
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 & Ex. The temperature and light in the room the plants are kept in, and the volume of water given to each plant. \tn 
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Confounding Variable & A variable that hides the true effect of another variable in your experiment. This can happen when another variable is closely related to a variable you are interested in, but you haven't controlled it in your experiment. \tn 
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\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Types of Variables (cont)}}  \tn
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 & Ex. Pot size and soil type might affect plant survival as much or more than salt additions. In an experiment you would control these potential confounders by holding them constant. \tn 
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Latent variables & A variable that can't be directly measured, but that you represent via a proxy. \tn 
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 & Ex. Salt tolerance in plants cannot be measured directly, but can be inferred from measurements of plant health in our salt-addition experiment. \tn 
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Composite variables & A variable that is made by combining multiple variables in an experiment. These variables are created when you analyze data, not when you measure it. \tn 
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\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Types of Variables (cont)}}  \tn
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 & Ex. The three plant health variables could be combined into a single plant-health score to make it easier to present your findings. \tn 
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\mymulticolumn{2}{x{5.377cm}}{{\bf{Quantitative Variables}}} \tn 
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Discrete/ integer variables & Counts of individual items or values. \tn 
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 & Ex. Number of students in a class; Number of different tree species in a forest \tn 
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Continuous variables (aka ratio variables) & Measurements of continuous or non-finite values. \tn 
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 & Ex. Distance, Volume, Age \tn 
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\mymulticolumn{2}{x{5.377cm}}{{\bf{Categorial Variables}}} \tn 
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Binary/dichotomous variables & Yes/no outcomes \tn 
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Nominal variables & Groups with no rank or order between them. \tn 
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 & Ex. Species, Names, Colors, Brands \tn 
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Ordinal variables & Groups that are ranked in a specific order. \tn 
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\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Types of Variables (cont)}}  \tn
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 & Ex. Finishing place in a race, Rating scale responses in a survey \tn 
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\begin{tabularx}{5.377cm}{x{1.9908 cm} x{2.9862 cm} }
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\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Sampling}}  \tn
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\mymulticolumn{2}{x{5.377cm}}{{\bf{Probability sampling methods}}} \tn 
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\mymulticolumn{2}{x{5.377cm}}{Probability sampling means that every member of the population has a chance of being selected. It is mainly used in quantitative research. If you want to produce results that are representative of the whole population, probability sampling techniques are the most valid choice.} \tn 
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Simple random sampling & every member of the population has an equal chance of being selected. Your sampling frame should include the whole population. \tn 
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Systematic sampling & is similar to simple random sampling, but it is usually slightly easier to conduct. Every member of the population is listed with a number, but instead of randomly generating numbers, individuals are chosen at regular intervals. \tn 
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Stratified sampling & involves dividing the population into subpopulations that may differ in important ways. It allows you draw more precise conclusions by ensuring that every subgroup is properly represented in the sample.  To use this sampling method, you divide the population into subgroups (called strata) based on the relevant characteristic (e.g. gender, age range, income bracket, job role). \tn 
% Row Count 39 (+ 16)
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\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Sampling (cont)}}  \tn
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Cluster sampling & also involves dividing the population into subgroups, but each subgroup should have similar characteristics to the whole sample. Instead of sampling individuals from each subgroup, you randomly select entire subgroups \tn 
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\mymulticolumn{2}{x{5.377cm}}{{\bf{Non-probability sampling methods}}} \tn 
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\mymulticolumn{2}{x{5.377cm}}{In a non-probability sample, individuals are selected based on non-random criteria, and not every individual has a chance of being included.  This type of sample is easier and cheaper to access, but it has a higher risk of sampling bias. That means the inferences you can make about the population are weaker than with probability samples, and your conclusions may be more limited. If you use a non-probability sample, you should still aim to make it as representative of the population as possible.  Non-probability sampling techniques are often used in exploratory and qualitative research. In these types of research, the aim is not to test a hypothesis about a broad population, but to develop an initial understanding of a small or under-researched population.} \tn 
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Convenience sampling & A convenience sample simply includes the individuals who happen to be most accessible to the researcher.  This is an easy and inexpensive way to gather initial data, but there is no way to tell if the sample is representative of the population, so it can't produce generalizable results. \tn 
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\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Sampling (cont)}}  \tn
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Voluntary response sampling & Similar to a convenience sample, a voluntary response sample is mainly based on ease of access. Instead of the researcher choosing participants and directly contacting them, people volunteer themselves (e.g. by responding to a public online survey).  Voluntary response samples are always at least somewhat biased, as some people will inherently be more likely to volunteer than others. \tn 
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Purposive sampling & This type of sampling, also known as judgement sampling, involves the researcher using their expertise to select a sample that is most useful to the purposes of the research.  It is often used in qualitative research, where the researcher wants to gain detailed knowledge about a specific phenomenon rather than make statistical inferences, or where the population is very small and specific. An effective purposive sample must have clear criteria and rationale for inclusion. \tn 
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Snowball sampling & If the population is hard to access, snowball sampling can be used to recruit participants via other participants. The number of people you have access to "snowballs" as you get in contact with more people. \tn 
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\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Data Cleansing}}  \tn
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\mymulticolumn{2}{x{5.377cm}}{Data cleansing involves spotting and resolving potential data inconsistencies or errors to improve your data quality.} \tn 
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\mymulticolumn{2}{x{5.377cm}}{{\bf{Type I vs Type II error}}} \tn 
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\mymulticolumn{2}{x{5.377cm}}{Type I error (false positive)} \tn 
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\mymulticolumn{2}{x{5.377cm}}{Type II error (false negative)} \tn 
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% That's all folks
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