Midterm Study Guide
Midterm Study Guide SOC 200
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This 4 page Study Guide was uploaded by Maddy Nogar on Thursday September 29, 2016. The Study Guide belongs to SOC 200 at Central Michigan University taught by Elizabeth Whitaker in Fall 2016. Since its upload, it has received 20 views.
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Date Created: 09/29/16
Soc 200 Exam 1 Study Guide Intro to Research Concepts Inductive v. Deductive Research o Inductive Research: use observations to develop a theory Question ask gather data develop theory o Deductive Research: begin with a theory that you want to test through analysis of observations Quantitative v. Qualitative Research o Qualitative Research: gather nonnumerical data like texts, interviews, photos, and recordings to help understand social life Ex: ethnography, historic comparative research, focus groups o Quantitative Research: translates social world into numbers which can be studied mathematically Ex: surveys, experiments, content analysis Role of statistics in the Research Process Correlation v. Causality o Correlation: a change in one factor coincides with a change in the other, two factors change together (positive/ negative) o Causality: a change in one factor leads to or causes a change in another factor Variables Level of Measurement o Nominal: Yes/ No, categories Cannot add, must count each individual value Ex: religion, animals, major o Ordinal: Natural order Categories with a natural order Ex: class standing, age group o IntervalRatio: number values with no order/category Ex: number of cats, books read this summer Descriptive Statistics – purpose Frequency Distributions Be able to construct one for all levels of measurement Be able to read and interpret one N: number of cases f: frequency proportion o p=(f/n) o always a number between 0 and 1 percent o %= (f/n) x100 cumulative frequency: total number of cases having a given score or a score that is lower o sum of the frequencies up or down the column begin with SMALLEST CATEGORY cumulative percent: add percents as you move down the column class intervals – why, what type of variables, how, mutually exclusive, exhaustive o approves and clarifies the % of data in a frequency distribution o Intervalratios or ordinal o categories or groups o usually of equal size to aid in interpretation ex: scores of tests: 90100, 8089, 7079… o Guidelines: Must be mutually exclusive Must be exhaustive from lowest to highest Designed to reveal or emphasize pattern Possible to have too few or too many Whole numbers preferred to decimals Charts Why o Dramatic, appealing, show basic impressions Which type for which levels of measurement o For Nominal Variables Show relative size of groups using frequencies and percents Pie o Univariable o Show different frequencies/ percents among NOMINAL Bar o Univariable o NOMINAL or ORDINAL o Must be put in order Line Histogram o ORDINAL or INTERVAL RATIO Measures of Central Tendency Why a single value to represent the data Which type for which levels of measurement Mode o BEST FOR NOMINAL, but works for all Definition most frequently occurring value in a distribution Determine from an arrayeyeball it (what appears most) Determine from a freq distributionlook for greatest frequency and then choose the variable value associated with that high frequency Definition of Bimodal two modes Median o Appropriate for ORDINAL or INTERVAL Definitionthe middle case of a distribution How to determine median case and from that the actual median value (n+1)/2=MEDAIN CASE, the number/ category associated with this is the MEDIAN VALUE Determine from an array Determine from freq distribution Mean o For INTERVAL RATIO o Most informed value Formula from an array (sum of raw scores)/(number of cases) Determine from an array ´ Symbols X mean o and ∑ sum Outlier Definition the case that is different than all the others Effect on Mean pulls the mean to give a poor summary of the data overall Skewlocation of extreme values relative to the core set of data o The measures of central tendency are not equivalent Measures of Dispersion Whyan indication of how scores are distributed around the center Range o Advantages good for IR/ ordinal, basic measure of dispersion o Disadvantagescrude and subjective to outliers o Interpretation only include two values (high and low) Interquartile Range o Formula=mean – single case o Meaning of Q1 (25%), Q2 (50%), Q3 (75%), Q4 (100%) IQR is the value b/w 75%25%, takes away outliers o Advantages takes away outliers o Interpretation report range at the core of data Characteristics of the Mean o Balance point/Fulcrumequal amount of deviation on either side of the mean o Least squares principle the mean is the value where if you square the value of each deviation and add them all up, the final sum is minimized Calculation of single case dispersion from the mean: (X ) Calculating Sum of dispersions – what will this equal? Calculating ‘Sum of Squares’ from an array Variance o Formula(standard deviation)^2 o Symbol S^2 o Calculate from an array o Interpretation – average squared deviation o Disadvantage as measure of central dispersion o Be able to compare two variances & determine which has greater spread o Be able to calculate from a std deviation Standard Deviation o Formula o Symbol o Calculate from an array o Be able to compare two std deviations & determine which has greater spread o Be able to calculate from a variance
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