research methods - social statistics

8/13/2019 Research Methods - Social Statistics

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RESEARCH METHODS: statistics

WHY STATISTICS?

A statistically literate person can: Do much more than merely calculate correct answerssuch a person understands the relevance of statistics

for social research, can select an appropriate statisticfor a given prupose and a given set of data, and cananalyze and interpret the meaning of that statistic

Comprehend and critically appreciate research reports

written by others Statistical analysis is relevant only for those

research projects where the information collectedis represented in numbers


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STATISTICSA TOOL FOR

SOCIAL RESEARCH

2 basic functions collecting, organizing and presenting data

analyzing data

Data analysis Descriptive statistics

summarizing and describing distribution of single variablemeasures of concentration and dispersion

understanding the relationship between 2 or morevariablesmeasures of association

Inferential statisticsgeneralizing from a sample toa population


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MEASUREMENT OF VARIABLES

Variable

a characteristic of interest for our researchthat varies in modalities/values

Numerical and atributive characteristics, numerical vs.categorical variables (e.g. number of visitors vs. architecture

styles) Discrete vs. continuous variables (e.g. household size vs. a

persons weight) Levels of measurement

Nominal (e.g. theatres in a city) Ordinal (e.g. primarysecondaryuniversity education) Interval (e.g. no 0 pointtemperature) Ratio (e.g. income)


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DESCRIPTIVESMEANS

Calculated - arithmetic meanshows tendencyof data to group arround single valuelowtolerance towards extreme values

Positionalresistant towards extreme values

Modethe value with the highest frequency,

Medianthe value that splits ordered series of data

into two equal parts


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DESCRIPTIVESDISPERSION

An average of 100 could be built from 90, 100and 110, but also from 1, 100 and 190

Standard deviation (STDV) is a measure ofdispersion and is derived as squared root fromvariance (average squared deviation of individualdata from arithmetic mean)


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PROBABILITY

The occurence of certain modality or value could beobserved not only through frequences, but throughprobabilities as well

Probability is relative frequency applied to large number

of cases (repetitions of measuring) For several modalities or values we can build

distribution of frequences and distribution ofprobabilities

We can present a probability distribution in simplemanner through expected value (mean) and STDV Knowing probability distribution lets us predict


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INFERENCEESTIMATION

Process of making conclusion about population valuesbased on sample values

Unbiased estimate is the one that exactly matches the

population value Since we can select many samples from a single

population estimates are usually biased

However, we know that sample values tend to form

normal curve, i.e. they tend to concentrate arroundmean99% come into -3 to +3 STDV

Point estimate and confidence interval


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INFERENCE TESTING

STATISTICAL HYPOTHESIS

For each statistic (any measure or coefficientthat varies across possible samples) calculated onsample we have to test validity (if its value

stands for whole population)This validity depends on level of variability of

the statistic and on size of the sample

We make conclusions on certain level ofsignificance, usually .01 and .05, which meansthat our estimate is valid for 99% or 95% of allpossible samples derived from the population

research methods - social statistics

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