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\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Terminology}}  \tn
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Effect Size & Magnitude/Strength of Relationship \tn 
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Statistical Significance & measures the probability of the null hypothesis being true compared to the acceptable level of uncertainty regarding the true answer. Basically, how likely the finding is attributable to a specific cause and not to chance. \tn 
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Standard Error & St E = Standard deviation of sampling distribution. Indicates how different a population mean is likely to be from a sample mean \tn 
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Sampling Error & When the sample does not represent the entire population of data \tn 
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Confidence Interval & Shows us the probability that a parameter will fall between a pair of values around the mean- basically, shows you the range of values your estimate may fall between if you redo your test within a certain level of confidence \tn 
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Z score & Is the difference between that individual's score and the mean of the distribution, divided by the  standard deviation of the distribution. It represents the number of standard deviations the score is from  the mean. \tn 
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Statistical power & In research design, it means the probability of rejecting the null hypothesis given the sample size and  expected relationship strength. \tn 
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Alpha value & Represents the probability of obtaining your results due to chance. Calculate it by taking 1 - C1 \% \tn 
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Probability value (p value) & likelihood of the observed value of a statistic, if the H0 were true. \tn 
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\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Ethics}}  \tn
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Ethics & an evolving set of guidelines to assist the researcher in conducting ethical research. \tn 
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3 areas of research ethics & Relationship between society and science, Professional issues, Treatment of research participants \tn 
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Relationship between society and science 8 Diener, E., \& Crandall, R. (1978). Ethics in social and behavioral research. University of Chicago Press. & About the extent to which societal concerns and cultural values should direct the course of scientific investigation (e.g., government funding, corporate support) \tn 
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Professional issues & Research misconduct = fabricating, falsifying, or plagiarizing the proposing, performing, reviewing, or reporting of research results \tn 
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Treatment of research participants & Fundamental issue = treatment of and care for participants \tn 
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APA Code of Conduct & Beneficence \& non-maleficence, Fidelity \& responsibility, Integrity, Justice, Respect for people's rights and dignity \tn 
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Beneficence and nonmaleficience & Beneficence = Acting for the benefit of others Nonmaleficence = Do no harm to others Minimise the risks + maximise the benefits of research \tn 
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Fidelity and responsibility & Refers to how we interact with others – We need to establish a trusting relationship with research participants. Issues of informed \seqsplit{consent/confidentiality/deception} \tn 
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Integrity & We should strive to be honest, accurate, and truthful in all professional activities • Poorly conducted research is unethical. Findings should be reported honestly and disseminated widely \tn 
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Justice & The benefits and burdens of research should be distributed as fairly as possible. E.g, who receives benefits of new treatment \tn 
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Respect for people's rights and dignity 21 & Respect for the rights and dignity of people. Respect for their autonomy. E.g., right to withdraw, coercion \tn 
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APA ethics section 8 & Institutional approval, Informed Consent, Deception, Debriefing, Coercion/Right to withdraw, \seqsplit{Confidentiality/Anonymity/Privacy} \tn 
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Institutional Approval & Institutions with active research programs require research to be reviewed by an IRB/HREC \tn 
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Informed consent & All aspects of research must be disclosed and must be comprehensible to participants 2. Participation should be voluntary, free from coercion; participants must be able to make rational judgement \tn 
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Informed consent cont & Active versus Passive consent Active = verbally agreeing and signing a form consenting to participate • With children, guardians return forms (failure to return = denying consent) Passive = Consent is indicated by a guardian not returning the form (failure to return = consent). Use active whenever possible. \tn 
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Deception & Some types of research require deception. Active deception- deliberately misleading participants with false info. Passive deception- withholding info \tn 
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Coercion, right to withdraw & Coercion = Feeling pressured to participate Right to withdraw = Participants must always feel free to decline participating and/or to stop participating at any time \tn 
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Privacy, anonymity, confidentiality & Privacy = controlling other people's access to information about a person Anonymity = keeping the identity of a participant unknown Confidentiality = not revealing information obtained from a participant to anyone outside of the research team \tn 
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Ethics of Animal Research & Concern animal welfare (improving animals' lving conditions and reducing number of animals used in research)but NOT animal rights \tn 
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Ethics of Animal Research Guidelines & Justification of research, Personnel, Care and housing of animals, Acquisition of animals, Experimental procedures, field research, eduational use of animals \tn 
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Ethical dilemmas & No determined formula/rule, decision is a subjective judgment \tn 
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IRB & Institutional Review Board \tn 
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Ethical issues during authorship communication & Justice (who receives credit for research), Fidelity and scientifc Integrity (accurate and honest reporting) \tn 
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Steps to adhere to ethical considerations & Making changes to your research design, prescreening to identify and eliminate high-risk participants,  and providing participants with as much information as possible during informed consent and debriefing. You need to monitor participants' reactions,  be alert for potential violations of confidentiality, and maintain scholarly integrity through the publication  process. \tn 
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\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Characteristics (C) / Assumptions (A) of Research}}  \tn
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Control (C) & Holding constant or eliminating extraneous variables to establish cause-and-effect relationships. \tn 
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Operationalism (C) & Defining scientific concepts by the specific operations used to measure them. This includes multiple operationalism, where constructs are represented by multiple measures. \tn 
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Replication (C) & The reproduction of results from one study in additional studies to verify findings. \tn 
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Uniformity or Regularity in Nature (A) & The assumption that there are consistent and lawful relationships in nature. \tn 
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Reality in Nature (A) & The belief that the phenomena studied by scientists are real and observable. \tn 
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Discoverability (A) & The assumption that these regularities and realities can be discovered through scientific investigation. \tn 
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Research Settings & Field Experiments, Laboratory Experiments, Internet Epxeriments &  \tn 
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Field experiments (RS) & \seqsplit{Artificiliaty} not a problem, but cannot control extraneous variables like in a lab &  \tn 
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Laboratory experiments (RS) & Ability to control extranueous variables, but introduce \seqsplit{artificiality} and poor ecological validity &  \tn 
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Internet experiments (RS) & Easy access, large samples and low cost, but lack of experimenter control, \seqsplit{self-selection}, drop out and multiple participant submissions &  \tn 
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Descriptive Research (T) & Observing, recording and describing behaviour &  \tn 
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\seqsplit{Relational/Predictive} Research (T) & Describing and \seqsplit{detecting/predicting} \seqsplit{relationships} &  \tn 
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Causal Research (T) & Describing behaviour, predicting \seqsplit{relationships} AND exploring \seqsplit{cause-and-effect} &  \tn 
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Qualitative Research (A) & \seqsplit{Non-numerical}, interpretive approach. Assumes a dynamic, negotiated soccialy consttructed reality. Data is written or spoken words, \seqsplit{observationws} of behaviour, pictorial or visual matter. Data analysis is thematic analysis with focus on \seqsplit{subjective/personal} meaning &  \tn 
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Quantiative Research (A) & Numerical data. Though \seqsplit{sophisticated} \seqsplit{non-experimental} approaches attempt to identify causal \seqsplit{relationships} • Can help identify \seqsplit{factors/relationships} to then form hypotheses to be tested with experimental research &  \tn 
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Mixed Methods (A) & Mixes Quantitative and Qualitative Research for more complete account &  \tn 
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Quantiative Experimental & Before making causal claim, three criteria: Co-variation (changes must be correlated), Temporal ordering (cause must precede effect), no Alternate Explanations &  \tn 
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\seqsplit{Between-subjects} design & Different participants exposed to each level of IV &  \tn 
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\seqsplit{Within-subjects} design & All participants exposed to all levels of the IV. Can mitigate confounding participant variables, which helps better establish \seqsplit{cause-and-effect}  Best used with proper \seqsplit{counterbalancing}. Also subject to carryover effects. &  \tn 
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Ads/Disads of Experimental Research & Causal inference, ability to manipulate variables, control & Does not test effects of extraneous variables, \seqsplit{artificiality}, inadequate method of scientific inquiry \tn 
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Quantitative \seqsplit{Non-experimental} & No manipulation of the IV, descriptive research, identifies \seqsplit{factors/relationships} to form hypotheses to then be tested through experimental &  \tn 
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Types of Quan \seqsplit{Non-Experimental} & \seqsplit{Correlational} study, Natural manipulation, \seqsplit{cross-sectional} and longitudinal &  \tn 
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Ads/Dis-Ads of Each Type & Research objectives of description and prediction, Research objectives of description and prediction, Multiple Groups/Time points to consider & Sometimes false assumption of causation, false assumption of causation, \seqsplit{cross-sectional/longitudinal} do not always produce similar results \tn 
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\seqsplit{Strenghts/Weaknesses} of Qualitative Research & Many different data collection methods, good for \seqsplit{describing/understanding}, provides data to develop theory & Difficult to Generalise, varying \seqsplit{interpretations}, objective hypothesis testing procedures not always used \tn 
% Row Count 26 (+ 9)
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\seqsplit{Directional/One-tailed} Hypothesis & Group A would have a higher mean on X than Group B. OR. There would be a \seqsplit{positive/negative} relationship between X and Y. &  \tn 
% Row Count 36 (+ 10)
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\seqsplit{Non-Directional/} Two-tailed Hypothesis & Groups A and B would differ on X. OR there would be a relationship between X and Y. &  \tn 
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Null Hypothesis. & A statement of no relationship among variables, or no differences between conditions. &  \tn 
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Content Validity & Ensures the test covers the full range of the concept being measured. &  \tn 
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Construct Validity & Measures how well the test reflects the theoretical concept it is designed to assess. &  \tn 
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\seqsplit{Criterion-Related} Validity: & Evaluates how well the test predicts outcomes based on another measure. &  \tn 
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Face Validity & Assesses whether the test appears to measure what it is supposed to measure based on subjective judgment &  \tn 
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External Validity & Examines if the study's results can be generalized to other settings, people, times, and measures. &  \tn 
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Internal Validity & Ensures the study accurately measures the relationship between variables without interference from other factors. &  \tn 
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Outcome Validity & Refers to how well a test or measure predicts or correlates with an outcome or behavior that it is supposed to influence or relate to in the real world. It's closely related to predictive validity but focuses on the practical implications of the test's results in real-world outcomes. &  \tn 
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P-Value & The p-value is a measure of the probability of obtaining test results at least as extreme as the results actually observed, assuming that the null hypothesis is true.  It quantifies the likelihood that the observed data would occur if the null hypothesis were correct. The null hypothesis typically represents a statement of no effect or no difference. &  \tn 
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Experimental Research & First feature is that the researchers' manipulation of the independent variable (conditions), and second feature is that the researcher exerts control over variables other than the IV and DV (extraneous variables) &  \tn 
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Statistical Validity & Concerns the proper statistical treatment of data and the souwndness of the researchers' statistical conclusions &  \tn 
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\seqsplit{Non-experimental} Research & Research that lacks the manipulation of an IV, but simply involves measuring variables as they naturally occur. Use when the research question relates to a single variable rather than a statistical relationship, or if it's a non-causal statistical relationship, or if the IV cannot be manipulated otherwise &  \tn 
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Types of \seqsplit{Non-Experimental} Research & \seqsplit{Correlational} Research (measuring two variables with little/no control over extraneous variables), \seqsplit{Observational} Research (focuses on making observations of behaviour in natural or labs etting without manipulating anything &  \tn 
% Row Count 18 (+ 18)
% Row 33
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\seqsplit{Counterbalancing} & Testing different participants in different orders. Best is Complete CB, but random CB can be used when the number of conditions in an experiment is large. &  \tn 
% Row Count 30 (+ 12)
\end{tabularx}
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\vfill
\columnbreak
\begin{tabularx}{5.377cm}{x{1.55618 cm} x{1.51041 cm} x{1.51041 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{3}{x{5.377cm}}{\bf\textcolor{white}{Research Approaches (cont)}}  \tn
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Four Main Types of Validity & are internal validity, external validity, statistical, construct &  \tn 
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% Row 35
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Concurrent validity & When the criterion is measured at the same time as the construct &  \tn 
% Row Count 10 (+ 5)
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Predictive validity & When the criterion is measured at some point in the future (after the construct has been measured) &  \tn 
% Row Count 18 (+ 8)
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Convergent validity & Criteria can also include other measures of the same construct &  \tn 
% Row Count 23 (+ 5)
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Reliability & The consistency of a measure. &  \tn 
% Row Count 26 (+ 3)
% Row 39
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Three types of Consistency & Over time (test-retest reliability), across items (internal consistency), and across different researchers (inter rater \seqsplit{reliability).} &  \tn 
% Row Count 37 (+ 11)
\end{tabularx}
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\vfill
\columnbreak
\begin{tabularx}{5.377cm}{x{1.55618 cm} x{1.51041 cm} x{1.51041 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{3}{x{5.377cm}}{\bf\textcolor{white}{Research Approaches (cont)}}  \tn
% Row 40
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Statistical significance & Conclusion that an observed finding (e.g., a \seqsplit{differencebetween} groups or conditions) would be very unlikely if the null hypothesis \seqsplit{weretrue.•} Practical significance = Clinical significance = Claim made when a \seqsplit{statisticallysignificant} finding seem large enough to be important. &  \tn 
% Row Count 22 (+ 22)
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\end{tabularx}
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\begin{tabularx}{5.377cm}{x{2.4885 cm} x{2.4885 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Relationship between Variables}}  \tn
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Statistical Methods to assess if two things are related or not & Correlation, Chi-Square and Regression \tn 
% Row Count 4 (+ 4)
% Row 1
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Scatterplots & Used to examine relationship between 2 quantitative varaibles. X-axisi: IV. Y-axis: DV. \tn 
% Row Count 9 (+ 5)
% Row 2
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Pearson's r correlation & Measures degree and direction of linear relationship between quantitative variables. r=0 does NOT necessarily indicate absence of relationship though. Also known as bivariate correlation, and is based on covariance between variables. \tn 
% Row Count 21 (+ 12)
% Row 3
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Covariance & How much each variable varies together \tn 
% Row Count 23 (+ 2)
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Homoscedasticity & Error variance is assumed to be the same at all points along linear relationship \tn 
% Row Count 27 (+ 4)
% Row 5
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Contingency Table & Used to examine relationship between categorical variables \tn 
% Row Count 30 (+ 3)
\end{tabularx}
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\vfill
\columnbreak
\begin{tabularx}{5.377cm}{x{2.4885 cm} x{2.4885 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Relationship between Variables (cont)}}  \tn
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Pearon's r effect size classifications & r = .10 Small effect • r = .30 Medium effect • r = ≥ .50 Large effect \tn 
% Row Count 4 (+ 4)
% Row 7
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Correlation coefficient (Pearson's r): Proportion of variance & To calculate the proportion of variance in one variable that can be accounted for by variance in the second, simply square Pearson's r. r2 = .01 Small effect r2 = .09 Medium effect r2 ≥ .25 Large effect \tn 
% Row Count 15 (+ 11)
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\end{tabularx}
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\begin{tabularx}{5.377cm}{x{2.4885 cm} x{2.4885 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Descriptive Statistics}}  \tn
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Descriptive Statistics & Includes Frequency Distribution, Graphic Representations, Central Tendency and Variability \tn 
% Row Count 5 (+ 5)
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Frequency Distributions & Data arrangement where we show the frequencies of each unique data value \tn 
% Row Count 9 (+ 4)
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Graphic Representations & Bar Graphs, Histograms, Line Graphs, Scatterplots \tn 
% Row Count 12 (+ 3)
% Row 3
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Bar Graphs & Vertical bars used to depict frequencies of categorical independent variable (eg both groups) \tn 
% Row Count 17 (+ 5)
% Row 4
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Histograms & Used to depict frequencies and distribution of quantiative variable. X-axis is the quantiative variable, y-axis is frequency \tn 
% Row Count 24 (+ 7)
% Row 5
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Line graph & Showing trend of connecting quantiative data. X-axis quantiative, y-axis frequencies . \tn 
% Row Count 29 (+ 5)
% Row 6
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Line graph can also be used to show interaction effects (e.g., pre-test post-test data) & x-axis is a categorical variable, y-axis is frequencies of each variable \tn 
% Row Count 34 (+ 5)
\end{tabularx}
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\vfill
\columnbreak
\begin{tabularx}{5.377cm}{x{2.4885 cm} x{2.4885 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Descriptive Statistics (cont)}}  \tn
% Row 7
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Scatterplot & Graphical Depiction of relationship between 2 quantitative variables. X-axis IV, Y-axis DV. \tn 
% Row Count 5 (+ 5)
% Row 8
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Central Tendency & Tells us what is typical for a quantiative variable through mean, median and mode \tn 
% Row Count 10 (+ 5)
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Variability & Tells us how spread out values of a quantiative variable are \tn 
% Row Count 13 (+ 3)
% Row 10
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Mode & Used best as a representation when data is normally distributed (e.g., symmetrical around mean) \tn 
% Row Count 18 (+ 5)
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Median & Center point of an ordered set of numbers \tn 
% Row Count 21 (+ 3)
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Mean & Arithmetic average \tn 
% Row Count 22 (+ 1)
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3 types of Variability & Range, Variance, Standard Deviation \tn 
% Row Count 24 (+ 2)
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Range & Highest data score minus lowest data score \tn 
% Row Count 27 (+ 3)
% Row 15
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Variance and Standard Deviation & Superior to range because they take into account ALL of the data values and provide info about dispersion. Variance = the average deviation of data values from their mean in squared units Standard deviation = the square root of the variance \tn 
% Row Count 39 (+ 12)
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\end{tabularx}
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\begin{tabularx}{5.377cm}{p{0.4177 cm} x{1.2531 cm} x{1.2531 cm} x{1.2531 cm} }
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\mymulticolumn{4}{x{5.377cm}}{\bf\textcolor{white}{Six Data Collection Methods}}  \tn
% Row 0
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\seqsplit{Observations} & Researcher watches and records \seqsplit{events/behaviours}. \seqsplit{Naturalistic} or Laboratory \seqsplit{Observations} & Provides firsthand information, allows for study of natural behaviour, captures non-verbal cues, usually \seqsplit{exploratory/open-ended} & Reactive effect if repsondents know they are being observed, \seqsplit{investigator} effects (personal bias), data analysis is \seqsplit{time-consuming} \tn 
% Row Count 11 (+ 11)
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\seqsplit{Questionnaires} & Measures \seqsplit{participants'} opinions and provides \seqsplit{self-reported} demographic info. \seqsplit{Closed-ended} or open-ended \seqsplit{questionnaires} & Efficient for large sample, \seqsplit{standardised} format for easy comparison & Response bias, limited depth of info, potential for \seqsplit{misinterpretation} \tn 
% Row Count 21 (+ 10)
% Row 2
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\seqsplit{Existing} Data & Collection of data that was left behind/used for something different before the current research. Documents, physical data, etc. & \seqsplit{cost-effective}, time-saving, allows for \seqsplit{longitudinal} studies & data may be \seqsplit{incomplete/outdated}, lack of control over data collection methods \tn 
% Row Count 32 (+ 11)
\end{tabularx}
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\begin{tabularx}{5.377cm}{p{0.4177 cm} x{1.2531 cm} x{1.2531 cm} x{1.2531 cm} }
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\mymulticolumn{4}{x{5.377cm}}{\bf\textcolor{white}{Six Data Collection Methods (cont)}}  \tn
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\seqsplit{Interview} & Can be through multiple mediums \seqsplit{(face-to-face}, phone, etc). Can be synchronous (happens in real-time) or \seqsplit{asynchronous} (over-time) & Good for measuring attitudes, allows for probing, in-depth info, useful for hypothesis testing & People might not recall important info, reactive effects, \seqsplit{investigator} effects, expensive and \seqsplit{time-conusming} \tn 
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% Row 4
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Focus \seqsplit{Groups} & Collection of data in a group situation where moderator leads discussion with a small group & Useful for exploring ideas and concepts, provides window into internal thinking, in-depth info, can be taped & Can be ex, difficult to find good moderator, reactive and \seqsplit{investigator} effects, measurement validity low \tn 
% Row Count 20 (+ 9)
% Row 5
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Tests & Data collection instruments designed to measure something. \seqsplit{Standardised} (existing, tested in previous research) or \seqsplit{Researcher-constructed} (new, often \seqsplit{specifically} developed to test for variables) & Provides measures of many \seqsplit{characteristics}, usually alr developed, \seqsplit{availability} of data to reference, easy data analysis & Can be ex, reactive participant effects, might not be appropriate for certain samples, open-ended Qs not avail \tn 
% Row Count 37 (+ 17)
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\end{tabularx}
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\begin{tabularx}{5.377cm}{x{2.4885 cm} x{2.4885 cm} }
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\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Inferential Statistics}}  \tn
% Row 0
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Inferential Statistics & allows researchers to make generalizations about a population based on sample data. It helps in estimating population parameters and testing hypotheses. \tn 
% Row Count 8 (+ 8)
% Row 1
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Sampling Error & the difference between the sample statistic and the actual population parameter. It is a natural occurrence in sampling and is important to understand for accurate data interpretation. \tn 
% Row Count 18 (+ 10)
% Row 2
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Sampling Distributions & These are probability distrihutions that can be constructed for any sample statistic \tn 
% Row Count 23 (+ 5)
% Row 3
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Estimation & This involves using sample data to estimate population parameters. There are two types: \tn 
% Row Count 28 (+ 5)
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Point Estimation & Provides a single value estimate of a population parameter. \tn 
% Row Count 31 (+ 3)
\end{tabularx}
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\begin{tabularx}{5.377cm}{x{2.4885 cm} x{2.4885 cm} }
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\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Inferential Statistics (cont)}}  \tn
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Interval Estimation & Provides a range (confidence interval) within which the parameter is expected to lie. \tn 
% Row Count 5 (+ 5)
% Row 6
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Confidence Intervals & A confidence interval gives a range of values that is likely to contain the population parameter with a certain level of confidence (e.g., 95\%). \tn 
% Row Count 13 (+ 8)
% Row 7
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Null Hypothesis Significance Testing (NHST) & method for testing a hypothesis by determining the probability of observing the sample data if the null hypothesis is true. It involves setting a significance level (alpha) to decide whether to reject the null hypothesis. \tn 
% Row Count 25 (+ 12)
% Row 8
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Type I Error & Occurs when the null hypothesis is incorrectly rejected (false positive). \tn 
% Row Count 29 (+ 4)
% Row 9
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Type II Error: & Occurs when the null hypothesis is not rejected when it is false (false negative). \tn 
% Row Count 34 (+ 5)
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\end{tabularx}
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\begin{tabularx}{5.377cm}{x{2.4885 cm} x{2.4885 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Sampling}}  \tn
% Row 0
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Sampliing & Can be qualitative or quantitative \tn 
% Row Count 2 (+ 2)
% Row 1
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Statistics & a numerical characteristic of sample data \tn 
% Row Count 5 (+ 3)
% Row 2
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Parameter & a numerical characteristic of a population \tn 
% Row Count 8 (+ 3)
% Row 3
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Sampling error & differences between sample values and the true population parameters. There's always some degree of sampling error. If you need 0 error = you can't sample = conduct a census = collecting data from everyone in the population) \tn 
% Row Count 20 (+ 12)
% Row 4
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Sampling frame & a list of all the elements in a population \tn 
% Row Count 23 (+ 3)
% Row 5
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Response rate & the percentage of individuals selected to be in the sample who actually participate in the study \tn 
% Row Count 28 (+ 5)
% Row 6
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Quantitative Sampling & Can be Random or Non-Random \tn 
% Row Count 30 (+ 2)
\end{tabularx}
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\begin{tabularx}{5.377cm}{x{2.4885 cm} x{2.4885 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Sampling (cont)}}  \tn
% Row 7
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Random Sampling & When your goal is to generalize findings to a larger population and you need to minimize bias.Using a random process to select members of the population for inclusion in the sample.All members of the population have an equal chance of inclusion in the sample.  Can only be used if we can identify every member of the population.Closely tied to the external validity of research and reduces bias while increasing generalisability and statistical validity. This is representative. \tn 
% Row Count 24 (+ 24)
% Row 8
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Non-Random Sampling & When studying specific subgroups, when resources are limited, or when the research requires depth over breadthSelecting participants for inclusion in a sample nonrandomly.All members of the population DO NOT have an equal chance of being included in the sample. It is cost-effective and efficient and allows for targetted sampling of a subgroup without a population. This is biased. \tn 
% Row Count 44 (+ 20)
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\begin{tabularx}{5.377cm}{x{2.4885 cm} x{2.4885 cm} }
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\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Sampling (cont)}}  \tn
% Row 9
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Random Sampling Types & Simple Sampling, Systematic Sampling, Stratified Sampling, Cluster Sampling \tn 
% Row Count 4 (+ 4)
% Row 10
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Non-Random Sampling Types & \seqsplit{Opportunity/Convenience}, Quota, Purposive, Snowball \tn 
% Row Count 7 (+ 3)
% Row 11
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Simple Sampling, Systematic Sampling, Stratified Sampling, Cluster Sampling & Pure mathematical sampling, starting at a random point and then selecting every Nth case, random sampling from homogenous strata of population, random sampling from XX randomly selected clusters \tn 
% Row Count 17 (+ 10)
% Row 12
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\seqsplit{Opportunity/Convenience}, Quota, Purposive, Snowball & Selecting individuals based on availability, seeking out a specific numerical number of cases in predetermined categories using non-random methods, using a range of methods to obtain participants with specific characteristics, identifying further cases from elements already in sample \tn 
% Row Count 32 (+ 15)
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\begin{tabularx}{5.377cm}{x{2.4885 cm} x{2.4885 cm} }
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\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Sampling (cont)}}  \tn
% Row 13
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EPSEM & Equal probability selection method (EPSEM). choosing a sample in a manner in which everyone has an equal chance of being selected \tn 
% Row Count 7 (+ 7)
% Row 14
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Proportional stratified sampling & where the sample proportions are made to be the same as the population proportions. IS an EPSEM \tn 
% Row Count 12 (+ 5)
% Row 15
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Disproportional stratified sampling & where the sample proportions are made to be different from the population proportions. NOt an EPSEM \tn 
% Row Count 17 (+ 5)
% Row 16
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One-stage cluster sampling & randomly select clusters and using all individuals within. E.g., randomly select 15 psychology classrooms using all individuals in each classroom \tn 
% Row Count 25 (+ 8)
% Row 17
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\mymulticolumn{2}{x{5.377cm}}{Two-stage cluster sampling} \tn 
% Row Count 26 (+ 1)
% Row 18
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Qualitative Sampling & Usually purposive, can include: Maximum variation sampling, Extreme Case Sampling, Homogeneous Sample Selection, Typical-case Sampling, Critical-case Sampling, Negative-case Sampling, Opportunistic Sampling \tn 
% Row Count 37 (+ 11)
\end{tabularx}
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\begin{tabularx}{5.377cm}{x{2.4885 cm} x{2.4885 cm} }
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\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Sampling (cont)}}  \tn
% Row 19
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Random Assignment: & Using a random process to allocate units/elements of the sample to levels of an independent variable.Closely tied to the internal validity of research. Primary use is that it Addresses \seqsplit{group-nonequivalence} \tn 
% Row Count 11 (+ 11)
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\end{tabularx}
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% That's all folks
\end{multicols*}

\end{document}