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SOC202H1 (21)

CH 2 textbook notes

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Scott Schieman

CH2 – ORGANIZING DATA TO MINIMIZE STATISTICAL ERROR  Accuracy of scientific ideas tested by empirical predictions o Unit of Measure – a set interval or distance between quantities  Errors – known degrees of imprecision  LEVEL OF MEASURE OF A VARIABLE – Identifies the variable’s o Error reduction relies on understanding predictive relationships measurement properties, which determine the kind of mathematical among variables operations (addition, etc.) that can be appropriately used with it and the o STATISTICAL ERROR – Know degrees of imprecision in the procedures statistical formulas that can be used w/ it in testing theoretical hypotheses used to gather and process information o NOMINAL VARIABLES – name categories codes merely indicate a difference in category, class, quality, or kind Controlling Sampling Error  Statistical analysis involves sampling  No meaningful rank in magnitude, numbers arbitrarily chosen  No sense of degree w/ nominal variables o Sampling Error – inaccuracy in prediction about a population that  Dichotomous Variable – variable with only two categories results from the fact that we do not observe every subject in the o ORDINAL VARIABLES – Nominal w/ allowing ranks (high to low) population  Observation – a measurement of a single person  Can be named categories or numerical scores  Ex. Social class (upper-lower), education level (senior, junior)  Summary Calculation – summing up a group of measurements, based on a  Ex. Likert scoring to survey questions set of observations o INTERVAL VARIABLES – Numerical scores w/ defined unit of measure o Research interests usually w/ summary statements of the group  Allow add, subtract, multiple, divide scores, compute averages  Usually small # subjects to draw conclusions on larger pop.  Differences in amount, quantity, degree numerically  A POPULATION – a large group of people (or objects) of particular interest  Ex. Fahrenheit, interval between degrees same that we desire to study and understand  Vs. Ordinal, has set unit of measure  A SAMPLE – a small group of the population; the sample is observed and measured and then used to draw conclusions on the population  Subtraction btwn ordinal = difference in rank not distance btwn scores  A PARAMETER – a summary calculation of measurements made on all o RATIO VARIABLES – Interval w/ true zero point, zero means none subjects in a population  Ex. Weight, height, GPA, distance, population size o Determine true parameter need survey entire population  Interval may have zero but is arbitrary  A STATISTIC – a summary calculation of measurements made on a sample  Check ratio vs. interval, check if a ratio is meaningful to estimate a parameter of the population  Ratio – the amount of one observation in relation to another o A estimate, tool to draw conclusions about a population in general  Ex. Weight, 40g to 20g has 2:1 ratio  Conclusion from sample not absolutely correct, only estimations st nd rd rd rd st  Degree of error/confidence in predictions determinable w/ logic  Ex. 1 , 2 & 3 , 3 isn’t 3 times worse than 1  Dummy Coding – change nominal/ordinal into interval/ratio w/ artificial o Statistical Generalization – conclusions about a population made w/ numerical scores –ex. Index coding proper statistical procedures  Unit of measure – for interval/ratio variables only o Statistical Estimate – report of a summary measurement based on:  1. Systematic sampling & precise measurements o Fixes set interval for the numerical values used as scores for an  2. Reported w/ known degrees of error & confidence interval/ratio variable –ex. Kg, cm, mL o Different from level of measurement o Guesstimate – a report of a summary measurement based on limited and usually subjective personal experiences, anecdotal evidence, or Coding and Counting Observations hasty casual observation  Codebook – a concise description of the symbols that signify each score of  Stereotype – a false generalization, guided by feelings each variable  Probability Theory – the analysis and understanding of chance occurrences o Use number symbols for categories b/c easier counting & sorting o allows compute confidence/accuracy degrees w/ conclusion on pop. o Response coding may introduce measurement error, req. precision o Allows comput
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