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by: Julia_K

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# Study Guide for Exam 1 PSYCH-UA 10 - 001

Julia_K
NYU

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Included in this study guide are thorough descriptions of key terms discussed in chapters 1 through 4. (I combined lecture notes with important information from the textbook). Please keep in mind ...
COURSE
Statistics for the Behavioral Sciences
PROF.
Elizabeth A. Bauer
TYPE
Study Guide
PAGES
5
WORDS
CONCEPTS
Statistics, Math
KARMA
50 ?

## Popular in Psychlogy

This 5 page Study Guide was uploaded by Julia_K on Saturday February 20, 2016. The Study Guide belongs to PSYCH-UA 10 - 001 at New York University taught by Elizabeth A. Bauer in Spring 2016. Since its upload, it has received 232 views. For similar materials see Statistics for the Behavioral Sciences in Psychlogy at New York University.

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Date Created: 02/20/16
Statistics for the Behavioral Sciences STUDY GUIDE – Exam 1 (Chapters 1­4) Continuous vs Discrete Scale 1. Continuous – infinite number of values; no gaps between adjacent values (ex: time,  weight, height) 2. Discreet – generally means there’s a finite number of values; there are gaps in between  adjacent values (ex: students in a classroom) Independent vs Dependent Variables 1. Independent – the variable being manipulated/observed in an experiment 2. Dependent – the variable used to assess whether the independent variable makes a  difference. Assesses any varying results. Parametric vs Non­parametric Statistics 1. Parametric – rely on parameters, their distributions, and calculations (ex: deviation from  a mean) 2. Non­parametric – rely on inferential and descriptive statistics rather than parameters. Scales of Measurement (Organizing Data) : 1. Nominal  a. Categorical or qualitative data b. Values are names (dogs, cats, political party, religious affiliation, etc.) c. Commonly displayed on bar graphs 2. Ordinal  a. Values have a numerical/specific order b. *Spaces between ordinals may NOT be the same [ex: on an anxiety scale test, the difference between 8 and 9 is not the same as the  difference between 4 and 5] c. Value examples: class rankings, small­medium­large 3. Interval a. Values have a numerical/specific order  b. The intervals between adjacent values are equal (unlike Ordinal data) c. Value examples: degrees F or C 4. Ratio a. Order matters, intervals are equal, but values have 0 points of absence. This  means a characteristics is always present, and can be scaled on a ratio. Comparing ratio to interval: take Celsius and Kelvin for instance. Both have a value of 0  degrees. But Celsius can’t be a ratio measurement because 0 degrees C does not indicate an  absence of heat. It only indicates a freezing point of water. For this reason, you can’t say that 40  degrees C is twice as hot as 20 degrees C, because that implies 20 degrees is less hot than 40. Kelvin is different. 0 degrees Kelvin actually indicates an absence of heat, so it can be used on a  ratio scale. Frequency Tables, Graphs, and Distributions Q: How would researches conveniently organize large amounts of numerical data?              A: Methods of Distribution, including Sample Frequency Distribution, Graphing, Frequency  Polygon, Group Frequency Distribution, and the Stem and Leaf Plot 1. Sample Frequency Distribution (SFD) a. Shows the frequency of each score  b. Has 2 columns : The left column is the array (scores listed down from highest to  lowest) and the right column is the frequency of each score (how many students  picked each score). c. Sum of the frequencies should equal the total number of students (N).  So, ∑f =N. d. The SFD can help identify the Cumulative Frequency (cf), or the number of  scores at or below a particular value. Ex) The cf of score 1 = the frequency of that score (3 people) + the frequency of  the scores below it (0 people). So, cf(1)=3. Note: To see how much one score is higher than the rest on a chart, you would  find the listed score, go straight across to the cf column, and then go one score  down. That shows how many people picked a lower score, and this goes for  finding the “cumulative relative frequency” as well, mentioned later. e. The cf can help identify the Relative Frequency (rf), which converts the  frequency into portions out of the whole (ex) .08 of the group chose a score of 7) So, rf=(f/N) f. The rf helps identify the Cumulative Relative Frequency (crf), which shows  what fraction of the scores are lower than a particular value.  So, crf = (cf/N) g. Finally, we can find the Cumulative Percentage (cpf) by doing: [crf x 100]. This tells us the exact score percentage. The Percentile Rank would show % of scores  at or below a particular value.  Note: To find this on a chart, look at a given score, then go straight across to the  number listed in the cpf column.  2. Graphs  a. Bar graph – height of the bar indicates frequency of occurrence b. Bar graphs are used for more discreet and vague data because the bars don’t  touch. Can be used for nominal data. c. Histograms – bars touch (implies a continuity, a variable such as height). They  show the real limits of a number. 3. Frequency Polygon a. Connects the highest values of the histogram.  b. On a graph: drawing a dot on the highest value of each bar on a histogram, and  then connecting those dots. c. Cumulative Frequency Polygon (Ogive Shape)     ­ Cumulative Frequency always slopes up or stays constant. It can’t go down  because it only measures what values are equal to or below the given value. ­The x­axis shows the upper real limits of the value. What are upper real limits? When you graph the value 5, for example, you want  to make sure you’re covering all the possible frequencies of that value, so your  “upper real value” measurement becomes 5.5. d. Cumulative Percentage Polygon – where on the graph does the percentile (in  between 2 values) fall? 4. Group Frequency Distribution a. Works similarly to the SFD method. b. This method is used when the data is too varying, so you would group them into  intervals/classes. With this method, specific individual scores are lost. c. Apparent limits (20) vs Real limits (20.5) d. Interval width (i) = how wide the group is, or the range. The range = Upper real  limit – lower real limit e. # of intervals = (range)/(interval width) f. A histogram is used to graph this 5. Stem and Leaf Plot a. These plots are continuous and don’t lose the score data, rather they plot them all  out. They are grouped by the same initial first digit, so 12, 13, and 14 would all be grouped in one line. b. Left side = stem (first digit of score) c. Right side = leaves (all numbers following the first digit) Percentile Rank: tells us what percent of scores are at or below a given score. (90% of students  scored at or below 85 on the exam). Percentile: a score identified by its percentile rank ^ (students scoring at or below 85 fall in the  90  percentile). Skewed Distributions: If the majority of the scores are grouped on one side of the scale, you call that a skewed  distribution. Mean is pulled towards the skew. Two types:  1. Positive – limit is at the bottom of the graph (usually floor­effect). Mean is greater than  median/mode. 2. Negative – limit is at the top of the graph (usually ceiling effect). Mean is less than  median/mode. The median is most reliable when looking at pos/neg skewed distributions because it better  represents the majority of the population, whereas the mean is pulled towards the skew. Central Tendency of a Skewed Distribution: ­In a normal, symmetrical distribution, the median, mean, and mode are equal. When the mode  changes and the graph is skewed, the mean is pulled in the direction of the skew  (adding/removing scores affects the mean). The median, however, is more robust – it shifts when you add/remove scores, but the shift usually compensates for the added scores. So it’s not greatly affected by the skewing. (Also, the median always stays between the mean and mode).  Central Limit Theorem: the overall spread of identically distributed variables on a graph will  be an approximate representation of the normal curve.    Straight from Professor’s ppt slide: Properties of the mean 1. If a constant is added to (or subtracted) from every score in a distribution, the  mean is increased (or decreased) by that constant. 2. If every score is multiplied (or divided) by a constant, the mean will be multiplied (or divided) by that constant. 3. The sum of the deviations from the mean will always equal zero. 4. The sum of the squared deviations from the mean will be less than the sum of  squared deviations around any other point in the distribution. Properties of the standard deviation 1. If a constant is added to (or subtracted from) every score in a distribution, the  standard deviation will not be affected. 2. If every score is multiplied (or divided) by a constant, the standard deviation will  be multiplied (or divided) by that constant. 3. The standard deviation from the mean will be smaller than the standard deviation  from any other point in the distribution. Z­Score • Standard score (Z score) says how many standard deviation scores above/below the mean is your score. Positive = above and Negative = below.  • Measure of location in relation to other people’s scores. Allows you to compare different  distributions. X   z  • Formula:  

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