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Summary Statistics 3 Lectures

Name: Statistics 3 Lectures
SKU: doc_3843806
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Author: bernarditarichards

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Vrije Universiteit Amsterdam (VU)

Statistics 3 Lectures LECTURE 1: Recapitulation LECTURE 2: ANOVA / Regression LECTURE 3: Factorial ANOVA LECTURE 4: ANCOVA LECTURE 4: Mediation / Moderation LECTURE 6: MANOVA/ Repeated Measures

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‭STATISTICS 3 2023‬
‭VRIJE UNIVERSITEIT - BACHELOR PSYCHOLOGY‬
‭WEEK 1‬

‭●‬ ‭LECTURE 1:‬‭Recapitulation‬

‭WEEK 2:‬

‭●‬ ‭LECTURE 2:‬‭ANOVA / Regression‬

‭WEEK 3:‬

‭●‬ ‭LECTURE 3:‬‭Factorial ANOVA‬

‭WEEK 4:‬

‭●‬ ‭LECTURE 4:‬‭ANCOVA‬

‭WEEK 5:‬

‭●‬ ‭LECTURE 4:‬‭Mediation / Moderation‬

‭WEEK 6:‬

‭●‬ ‭LECTURE 6:‬‭MANOVA/ Repeated Measures‬

,‭..‬ ‭Lectures 1: RECAPITULATION‬ ‭WEEK 1‬
‭DEFINITION STATISTICS‬
‭●‬ ‭“‬‭Statistics is the science of collecting, organizing and interpreting numerical facts, which we call data‬‭.”‬
‭●‬ ‭Important‬‭matters for the application of statistics‬‭(“Applied Statistics”):‬
‭1.‬ ‭Selecting a sample‬‭from a population‬
‭a.‬ ‭Random vs non-random selection‬
‭2.‬ ‭Deciding whether a sample is‬‭representative‬
‭a.‬ ‭You want to generalize your findings to the population, not just this sample‬
‭3.‬ ‭Deciding between‬‭Descriptive‬‭or‬‭Inferential Statistics‬
‭4.‬ ‭Measurement levels (NOIR)‬‭and‬‭types of variables (categorical/quantitative)‬
‭5.‬ ‭Selecting the correct‬‭statistical analysis‬
‭6.‬ ‭Experimental‬‭versus‬‭non-experimental‬‭research design‬

‭●‬ ‭A&F: Statistics consists of a‬‭body of methods for obtaining and analyzing data‬‭, to:‬
‭1.‬ ‭Design‬ ‭[research studies that‬‭]‬
‭2.‬ ‭Describe‬ ‭[the data to]‬
‭3.‬ ‭Make inferences‬ ‭based on these data.‬
‭Two types:‬

‭Descriptive Statistics‬ ‭Inferential Statistics‬

‭ escriptive‬ ‭statistics‬ ‭summarize‬ ‭sample‬ ‭or‬ I‭nferential‬ ‭statistics‬ ‭make‬ ‭predictions‬ ‭about‬ ‭population‬
D
‭population‬ ‭data‬ ‭with‬ ‭numbers,‬ ‭tables,‬ ‭and‬ ‭graphs‬ ‭parameters, based on a (random) sample of data‬
‭(e.g., box plots)‬ ‭(Estimate population parameter based upon the sample statistic)‬

‭DATA, POPULATION, SAMPLE, RELIABILITY, VALIDITY‬

‭Doing research by means of‬‭data‬‭: observation of characteristics‬

‭Population‬ T‭ he‬‭total set of participants‬‭, relevant for the‬ E‭ .g.‬‭Population parameter‬‭: average hour of self‬
‭research question‬ ‭study per week of‬‭all‬‭students‬‭.‬

‭Sample‬ a‭ ‬‭subset of the population‬‭about who the‬ E‭ .g.‬‭Sample statistic‬‭: average hour of self study‬‭per‬
‭data is collected‬ ‭week of a randomly selected‬‭sample of‬‭800‬
‭students‬

‭Good data is necessary to answer the research question:‬
‭●‬ ‭Reliability‬‭(Precision => consistency!)‬
‭●‬ ‭Validity‬ ‭(Bias)‬

,‭DESCRIPTIVE STATISTICS‬
‭Variables, measurement levels and range‬
‭Variable‬‭:‬‭measures‬‭characteristics‬‭that can‬‭differ‬‭between subjects‬
‭●‬ ‭Types‬‭:‬‭behavior-, stimulus-, subject-, physiological variables‬‭(eg: reaction time, height, level of aggression)‬
‭●‬ ‭Distribution‬‭:‬‭Summary of one variable‬
‭●‬ ‭Association‬‭:‬‭Summary of Multiple Variables‬

‭Measurement scales‬‭(NOIR):‬

‭N‭o
‬ minal‬ ‭ NORDERED‬‭categories, no ordering, no‬ C
U ‭ ountries‬
‭Categorical‬ ‭meaningful distances, no absolute zero‬ ‭Eye color‬
‭ ategorical /‬
C ‭Biological sex‬
‭Qualitative‬
‭O‭r‬ dinal‬ ‭ RDERED‬‭categories, ordering, no‬
O ‭ ancing Skills categorized as “beginners”,‬
D
‭Categorial‬ ‭meaningful distances, no absolute zero‬ ‭“intermediate”, “advanced”‬
‭ point Likert scale from very satisfied to‬
5
‭very dissatisfied‬
‭disagree/neutral/agree‬

‭I‬‭nterval‬ c‭ ategories, ordering, meaningful‬ T‭ emperature in Celsius (because‬
‭Continuous‬ ‭distances (equal distances between‬ ‭Temperature of 0 does not mean ab‬
‭measures), no absolute zero‬ ‭absence of temperature since there is still‬
‭minus temperatures)‬
‭ uantitative/‬
Q
‭Numerical‬ =‭ equal distance between consecutive‬
(‭ „Parametric‬ ‭values‬
‭Methods“)‬
‭R‬‭atio‬ c‭ ategories, ordering, meaningful‬ ‭ istances between 2 cities; Number of‬
D
‭Continuous‬ ‭distances, an absolute zero‬ ‭Years a person has smoked (0 years is‬
‭absence); Age‬
=‭ equal distance and‬‭true zero point =(‬ ‭Kelvin - Temperature‬
‭absence of the characteristic)‬

‭Range‬‭:‬

‭Discrete‬ ‭measurement unit that is indivisible (1 not 1.2)‬ ‭Example: brothers/sisters‬

‭Continuous‬ ‭infinitely divisible measurement unit‬ ‭Example: body height‬

‭Summarized‬

, ‭Graphs of Descriptive Statistics‬

‭Bar Graph‬ ‭Nominal /Ordinal‬ ‭Bars‬‭apart‬‭from each other‬
‭ isplaying‬
D
‭Frequencies of‬
‭categorical data‬

‭Histogram‬ ‭Interval / Ratio‬ ‭ ars‬‭next to‬‭each other (groups are‬
B
‭ isplaying‬
D ‭connected!)‬
‭Frequencies of‬ ‭ ecause can divide groups into fixes‬
B
‭quantitative data‬ ‭sizes such as income of 1000 - 2000‬

‭Pie Chart‬ ‭Categorical data‬ ‭ isplays the share/ percentage per‬
D
‭Nominal / Ordinal‬ ‭category‬
‭ ot suitable for many values (e.g.,‬
N
‭displaying income of 63 employees)‬
‭Not suitable for continuous data‬

‭Line Graph‬ ‭Interval/ Ratio‬ ‭ sed to show “a trend” of two‬
U
‭variables at a ratio level‬

‭Box Plot‬ ‭ isplays center (= by the median) &‬
D
‭variation of data (= box & lines)‬

‭Scatterplot‬ ‭Interval/ Ratio Level‬ ‭ isplays association / relation‬
D
‭between 2 variables: positive/‬
‭negative‬

‭Difference to Line Graph:‬
‭LG: for trends /SP: for relations‬

S‭ team and‬ ‭Quantitative Data‬
‭Leaf Plots‬

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