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Stats Study Guide 1

by: Nicole Creekmore

Stats Study Guide 1 PSYS 241

Nicole Creekmore
GPA 4.0

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About this Document

A study guide for the introduction to statistics. Chapters 1-4
Dr. Tagler
Study Guide
50 ?




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Popular in Psychology (PSYC)

This 4 page Study Guide was uploaded by Nicole Creekmore on Thursday September 15, 2016. The Study Guide belongs to PSYS 241 at Ball State University taught by Dr. Tagler in Fall 2016. Since its upload, it has received 5 views. For similar materials see Statistics in Psychology (PSYC) at Ball State University.


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Date Created: 09/15/16
Define/describe ● Variable ○ Something that when measured has no fixed value ● Descriptive statistics ○ Organizing and summarizing information about a set of  observations (collected data) ○ Statistics that are used to describe or summarize the  characteristics of a set of scores ○ Central tendency, variability ● Inferential statistics ○ Tests, estimations, generalizing beyond observations ○ Using sample data to make judgements about populations ● Random Sampling (Selection) ○ Each member of the population has an equal chance of  being selected for the sample with the goal to obtain a sample that is  similar to the overall population from which it is taken so a sample can be  generalized to the population ● Random Assignment ○ Participants are randomly assigned to experimental groups,  a method of equating experimental groups ● Observational study (correlational research) ○ ● Experimental method ○ Allows a researcher to test cause­effect relationships via  manipulation of an independent variable and a measurement of a  dependent variable ● Independent variable ○ The variable manipulated by the researcher, randomly  assign participants to groups to avoid confounding variables“cause” ● Dependent variable ○ The variable measured by the researcher, the outcome of  interest “effect” ● Confounding Variable ○ An unexpected variable that may  alter results ● Four measurement scales: ○ Nominal ■ The measurement of variables that are  qualitative, categorical in nature, using words or numbers as  arbitrary labels for classification or identification ○ Ordinal ■ Measuring some quantity (magnitude), but only in terms of rank order ○ Interval ■ A quantity being measured on a scale with  equally sized intervals but no true zero point  ■ Fahrenheit, celsius, iq test ○ Ratio ■ A quantity being measured on a scale with  equal intervals AND a true zero point ■ Time, income, height, weight, length, kelvin ● Quantitative data ○ numbers ●  Qualitative data ○ Not numerical ● Outlier ○ Extreme scores ● Percentile Rank ○ The score at which a given % of the scores in a distribution  fall at or below ○ Median is the 50th percentile ● Skewed distributions ○ Have more scores on one end and “tails” of fewer extreme  scores ■ Positive ● The mean exceeds the median ● Tail is right of the mean ■ Negative ● The median exceeds the mean ● Tail is left of the mean ○ *if there is a lot of skew, trust the MEDIAN, if there is not a  lot of skew, use the MEAN, only use the MODE when there is nominal  data* ● Measures of central tendency ○ Descriptives stats that summarize a set of scores by  determining where the “center” of a distribution is ○ The identification of a “single point” around which most (or at least most typical) scores are located ● Mean ○ Arithmetic mean, “average”, the sum of the scores divided by the number of scores ○ Desirable­ uses all of the scores, when the distribution is  skewed, the mean gets “pulled” toward the tail ● Median ○ Middle score, value at the 50th percentile ○ Rank order data, odd number­ middle number, even  number­ compute mean of 2 middle scores ○ Particularly useful when dealing with a skewed distribution  (e.g., income), doesn’t use all of the information in the set of scores ● Mode ○ Most frequently occurring score, with nominal data this is the only measure of central tendency that works ○ Not useful in uniform distributions, does not take into  account any scores other than those highest in frequency ● Measures of variability ○ Descriptive statistics that indicate the extent to which the  scores in a distribution differ or vary, measures of “spread” ● Population Variance ○ The sum of the squared deviations divided by the number of  squared deviations ○ `σ² is population variance N ● Population standard deviation ○ The square root of variance; the square root of the sum of  the squared deviations divided by the number of squared deviations  ○ `σ is population standard deviation N  ● Sample Variance ○ The sum of the squared deviations divided by the number of  squared deviations ○ S2 is sample variance N­1 ● Sample standard deviation ○ The square root of variance; the square root of the sum of  the squared deviations divided by the number of squared deviations  ○ S is sample standard deviation N­1 ● Range ○ The difference between the lowest and the highest in a  distribution, provides information only about the maximum amount of  spread among scores, overly influenced by extreme values, does not  provide information about typical spread ○ Highest score­lowest score ● interquartile range ○ Defines the range of values between the 25th percentile (1st  quartile) and the 75th percentile (3rd quartile), completely ignores the  influence of the most extreme scores ○ 75th percentile­25th percentile ● Deviation scores ○ The distance that each score is from the mean of the set of  scores the sum of the deviations equals zero ○ Computed by subtracting the mean from each score ● Z­scores ○ Computed by subtracting the mean from a raw score and  dividing by the standard deviation ● standard scores ○ Indicate in standard deviation units how far a single raw  score in a set of scores from the mean ● Properties of z–scores ○ mean=0 ○ Standard deviation=1 ○ variance=1 ○ The shape of the distribution is the same as the shape of the original scores ■ If a set of scores are skewed or kurtotic,  converting them to z­scores results in a set of z­scores that are  identical in terms of skewness and kurtosis ● The unit normal distribution (standard normal curve) ○ If raw data are normally distributed, converting to z­scores  results in this ● The empirical rule using the standard normal table ○ In a unit normal distribution: ■ 68.26% of scores are between z=+­1.00 ■ 95.44% of scores are between z=+­2.00 ■ 99.74% of scores are between z=+­3.00 ● convert a z score to original score ○ z=(x­mean)/sd solve for x ● convert a z score to T, IQ, or any other standard score (transformed  standard scores) ○ zscore(sd)+mean=IQ or T etc.


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