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# Week 2 Notes Psych 315

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This 4 page Class Notes was uploaded by kgottuk on Sunday October 9, 2016. The Class Notes belongs to Psych 315 at University of Washington taught by Dr. Dana Nelson in Fall 2016. Since its upload, it has received 77 views. For similar materials see Statistics of Psychology in Psychology (PSYC) at University of Washington.

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Date Created: 10/09/16

Notes Week 2 Variable: something that can have different values Constant: something with only one value Measurement/Qualitative Data: numerical data from measurements (ex: height) Discrete: countable; has exact values (# of kids: can only have 1 full kid at a time) Continuous: uncountable; goes on forever (height: decimals can go on forever) Categorical/Qualitative Data: cases organized by categories that are then counted Ordered: examples include military rank, student class, etc. Unordered: examples include color, fruits, etc. Nominal: Unordered categories (name, color) Ordinal: Ordered categories (student class) Interval: Ordered categories with equal distance between measurements (˚F, altitude) Near-Interval: mix between ordinal and interval (rating scales, test scores) Ratio: similar to interval but with an absolute zero (# of kids, height) Frequency Distribution: shows # of times something is observed in a single category - How to order: o Nominal: most frequent at top o Ordinal: categories ordered by rank o Interval and Ratio: scores without subjects still listed Grouped Frequency Distribution: shows # of times something is observed class intervals o Class intervals must include everything within measured range o Intervals must be measured the same (80-84, 85-89, etc.) o Must include everything regardless of frequency score (still needed if f = 0) Limits: class interval boundaries Apparent Limits: distance from smallest unit of meas. to largest in class interval Real Limits: ½ of smallest unit below class interval to ½ above class unit o Of score: 97 ± .5 = 96.5, 97.5 o Of interval: 95-99 95 - .5 = 94.5, 99 + .5 = 99.5 94.5-99.5 Interval Width (i): upper real limit – lower real limit = i o i = 99.5 - 94.5 = 5 Chart 1 Grades (Apparent Real Limits Frequencies (f )Cumulative Freq. Rel f rel f cum rel f Limits) Dist. (%) (prop.) (%) 95 - 99 94.5 - 99.5 4 26 = … 15.3 .154 99.7 90 - 94 89.5- 94.5 5 22 = … 19.2 .192 84.4 85 - 89 84.5 - 89.5 3 17 = … 11.5 .115 65.2 80 - 84 79.5 - 84.5 6 14 = … 23.1 .231 53.7 75 - 79 74.5 - 79.5 1 8 = … 3.8 .038 30.6 70 - 74 69.5 - 74.5 4 7 = (1+2+0+0+4) 15.3 .153 26.8 65 - 69 64.5 - 69.5 0 3 = (1+2+0+0) 0 .000 11.5 60 - 64 59.5 - 64.5 0 3 = (1+2+0) 0 .000 11.5 55 - 59 54.5 - 59.5 2 3 = (1+2) 7.7 .077 11.5 50 - 54 49.5 - 54.5 1 1 = (1) 3.8 .038 3.8 N= 26 99.7 .997 Not 100 or 1.00 due to rounding errors Cumulative Frequency Distribution: shows all frequencies below upper real limit of the interval Relative Frequency Distribution: exhibits frequency in terms of percentage or proportion based on category Cumulative Relative Frequency Distribution: shows all percentages below upper real limit of the interval Stem Leaves 1 00 Where: Stem and Leaf Plots 2 2358 1 0 =10 o shows frequencies with no information lost 3 2 3 2 =32 4 678 All Graphs Need 5 2335879 o title and labeled axis 6 22243 o axis’ that start at 0 (specifically y-axis) o x-axis may be broken but not the y-axis o size (3:4) Graphing Quantitative Data - Histogram o Height (y-axis): frequency/relative frequency o Length (x-axis): class interval width Either with real limits put under bars or midpoints of the intervals (i) o Similar to bar graph but has no gaps - Line Graphs: dots only placed on x-axis when f = 0 o Y-axis: freq. / rel. freq. X-axis: midpoint of interval - Frequency Polygons: create closed shapes by the line starting and ending on the x-axis - Cumulative Percentage Curve: graph showing only dots connected above upper real limits of each class interval o Y-axis: cumulative % or proportion X-axis: upper real limits Line must touch x-axis Graphing Qualitative Data - Bar Diagram: like histogram but space between bars o Space shows the data is not continuous o Y-axis: f, %, or proportion X-axis: qualitative categories - Pie Chart: each piece shows relative frequency, though hard to compare pieces Distribution Shapes Bell-Shaped (Unimodal) Normal Skewed Positively Skewed (right) Positively Skewed (right) J-Shaped Rectangle Bimodal Bimodal: two humps Central Tendency - Mode (Mo): category or number that occurs the most (33456777) o If grouped, add the bottom and top # in group and divide by 2 o Affected by width and location of intervals o Used for all measurement scales though not often used o Best for central tendency of nominal data and quantitative discrete data - Median (Mdn): middle-most score or category (1,2,3,4,5,6,7,8,9) o If even # of scores/categories, add the two middle-most scores and divide by 2 o Best to use with skewed distributions (does not change due to outliers) o Use for any but nominal - Mean: add up all scores and divide by number of scores present (1,2,3,4,5,) 15/5 = 3) x̅= sample µ = population ∑ = sum of all scores (sigma) n = sample size N = population size X X X n (sample) N (population) Sample = sum of all scores Population = sum of all scores sample size population size o Trimmed Mean: mean with a certain percentage of lowest and top scores taken out as to reduce the possibility of skewed data. x = {1,2,3,4,5,6,7,8,9,10} x = {1,2,3,4,5,6,7,8,9,1000} x = 55/10 = 5.5 x = 1045/10 = 104.5 ̅ ̅ x̅10% trim = 44/8 = 5.5 x̅10% trim = 44/8 = 5.5 Order of Operation X= {3, 6, 8, 10, 12} Y = {10, 5, 7, 11, 12} n 2 (X X) i1 i x = (3+6+8+10+12)/ 5 = 50/5 = 10 ̅ 2 x̅ x - ̅ 2(x - ̅ ) 3 3 – 10 = -7 -7 = 49 6 6 – 10 = -4 -4 = 16 8 8 – 10 = -2 -2 = 4 10 10 – 10 = 0 0 = 0 2 12 12 – 10 = 2 2 = 4 ∑ = -11 ∑ = 73 (sum of all squares) Week 1 Review Experimental Design: systematically manipulates change using IV (cause) and DV (effect) Nonexperimental Design: natural environment study; no manipulation by experimenter using predictor (before) and criterion (after) Descriptive Statistics: Information from sample cannot be inferred to population Inferential Statistics: Information from sample can be inferred to population - Requires random sampling (remember sample is not always representative of population so the bigger the sample, the better chance of it being representative of the population)

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