SOC200 - Quantitative Analysis (Chapter 14 +15)

Question 1

QUANTITATIVE ANALYSIS

Answer 1

approach to analyzing social science data in which:
observations represented + manipulated numerically
to describe + explain phenomena represented by those observations

Question 2

QUANTITATIVE ANALYSIS

Answer 2

increase of cheap processing power in recent decades has increased possibilities of quantitative analysis

Question 3

QUANTITATIVE ANALYSIS

Answer 3

convenience has increased demand among researchers, governments for quantitatively analyzed data
Computers a must: Better tools, Execution makes it a must

Question 4

Coding in Quantitative Analysis

Answer 4

For computers to recognize the data you want to analyze, all elements comprising your data must be assigned a distinct number
levels of data will affect type of coding type of analysis you can use
Software needs to recognize data into categories

Question 5

Coding in Quantitative Analysis

Answer 5

Nominal, Ordinal, Interval/Ratio

Question 6

Coding Nominal Level Data

Answer 6

can code categories of nominal data with any numbers you want, BUT you have to use analyses that will NOT treat values as count data
code each category with numbers
Missing values – signify space in data with some number: assigning nonresponse with unique code
Restricted to certain types of analysis based on data level
Can’t use mean for nominal data

Question 7

Coding Ordinal Level Data

Answer 7

can code the categories of ordinal data with any numbers you want BUT for convenience, categories are usually coded with consecutive numbers
make it more intuitive and simple

Question 8

Coding Ordinal Level Data

Answer 8

Though ordinal data have rank, you have to use analyses that will NOT treat the values as count data because rank may not be equal distances between each category
Same coding options as nominal

Question 9

Coding Interval/Ratio Data

Answer 9

Each category has value comprised of continuous number
data has the most potential for analysis
possesses an inherent number that you can use for coding

Question 10

Coding Interval/Ratio Data

Answer 10

can later collapse it into ordinal/nominal form for less sophisticated analysis
# representing an actual value
Still need to specify missing value: negative number because it couldn’t possibly be part of the data

Question 11

Ultimate Goal of Coding in QA

Answer 11

To reduce broad array of info to more limited + manageable set of attributes that will make up variable
Important Guideline: coding to maintain great deal of detail helps keep your options open in a later analysis

Question 12

Main Approaches to Coding in Quantitative Analysis: Approach 1

Answer 12

well-developed coding scheme derived from research purpose

using existing coding scheme can save you time + effort, developed by someone else

Question 13

Main Approaches to Coding in Quantitative Analysis: Approach 2

Answer 13

Generating codes directly from observing data

inductive approach

Question 14

The Codebook – The Ultimate Reference to your Data

Answer 14

searcher’s reference for how to code data they are collecting (when the researcher is actually collecting data)

Question 15

The Codebook – The Ultimate Reference to your Data

Answer 15

researcher’s reference for locating variables + interpreting codes in data during analysis (when the researcher is analyzing secondary data)
Reference for what the data means

Question 16

Common Codebook Contents

Answer 16

Variable identified with abbreviated name
Should contain full definition of variable
Should explain attributes comprising each variable
Should indicate numeric label assigned to each attribute for data manipulation purposes

Question 17

Common Codebook Contents

Answer 17

Name: variable abbreviated
Label attached to each value
know what type of data we have (nominal, ordinal, interval)
N: Total # of cases + frequencies of each category
Percentages of frequencies
Properties: location in spreadsheet + type of data

Question 18

Data “Cleaning”

Answer 18

Detecting, correcting/eliminating coding errors in data
Missing values that shouldn’t be there
1. Values have to be the ones specified in codebook
2. Understand your data: checking for values logically impossible given data

Question 19

Contingency Cleaning

Answer 19

checking that cases which have logical limits to certain responses, have data that falls within those limits

Question 20

Contingency Cleaning

Answer 20

Some programs check for errors during/after data entry

Run frequency distribution to check for outlying/odd frequencies of responses

Question 21

Univariate Analysis

Answer 21

univariate data: single variable

univariate analysis: report distribution of cases of single variable

Question 22

Three ways of presenting univariate data:

Answer 22

Distributions – charts/tables showing frequencies of the
categories of a single variable
Central Tendency – “typical” value in your variable
Dispersion – how close data is clustered around its
“typical” value

Question 23

Distributions

Answer 23

Frequency Distributions: show # of cases that have each attribute of variable
Valid Percent: missing values not taken into account

Question 24

Frequency Distribution as a Bar Chart

Answer 24

frequencies by themselves are meaningless
need some basis/context for assessing frequencies (percentage of total cases)
Easier to look at

Question 25

Central Tendency

Answer 25

Which ones you can logically use depends on whether your univariate data is nominal, ordinal, or interval/ratio level + goal of your analysis Mode – Nominal + ordinal: few categories

Question 26

Mean Value

Answer 26

summing values of your observations + dividing by total # of observations Ideal for continuous (interval/ratio level) data (age, temperature, dollars, speed, height, weight)

Question 27

The Mean Value

Answer 27

Problem:can become inaccurate measure of typical value of variable if some cases have extreme values

Question 28

mode

Answer 28

expressing “typical” value of your single variable most frequently observed value Can be used with any of the four levels of data

Question 29

Median Value

Answer 29

value represents 50% of the cases in ranked distribution are above this value, and 50% are below it

Question 30

Median Value

Answer 30

1. Rank all cases by the value of the variable 2. find the case 50% above this case + 50% below Easy to find value when list of cases equals odd number because it will be the value of the middle case

Question 31

Median Value

Answer 31

even number of cases - middle pair of numbers + find value half way betw them by adding them up + dividing by two

Question 32

Important Note on Using Measures of Central Tendency

Answer 32

important to be familiar with the distribution of the data to help you decide on the most meaningful measure of central tendency. Remember, extreme values can affect the mean

Question 33

Measures of Dispersion

Answer 33

how closely values of variable are clustered around “typical value” in the variable (a mean, median, or mode) Scattered/clustered How tightly distributed data

Question 34

The Range

Answer 34

distance separating min + max | The hourly wages in Canada excluding Toronto ranged from a low of $2.00 to a high of $173.08 in 2009

Question 35

Standard Deviation

Answer 35

standard error of sampling distribution: how closely values in sample clustered around pop mean how closely the values in the sample are clustered around sample mean

Question 36

STANDARD DEVIATION AND STANDARD DEVIATION INCREMENTS

Answer 36

Probability theory: certain proportion of data in the sample will fall within a certain distance from its mean value

Question 37

Subgroup Comparisons and Bivariate Analysis

Answer 37

Subgroup comparison of data involves description of 2/more groups simultaneously for comparison purposes Bivariate: look at relationship from 1 variable to another

Question 38

Subgroup Comparisons and Bivariate Analysis

Answer 38

subgroup comparison is more descriptive. Bivariate analysis seeks to show empirical relationships. tables comparing bivariate/multivariate data - contingency tables (pattern in 1 variable is thought to be contingent on other)

Question 39

Table Preparation and Interpretation in a Bivariate Analysis

Answer 39

general agreement that independent variable will appear in columns along top row of table, while dependent variables appear in rows comprising fist column Depends on what you are comparing

Question 40

Table Preparation and Interpretation in a Bivariate Analysis

Answer 40

No standard agreement on displaying percentages in a bivariate table, so use the following general guideline Tables percentaged down (each column = 100%) should be read across Tables percentaged across (each row = 100%) should be read down

Question 41

Logic of Multivariate Analysis

Answer 41

seeing causal/explanatory relationship relationship betw independent + dependent variable is examined with regard to more than 1 IV When you add 3rd variable, does it change relationship betw DV + IV

Question 42

Constructing and Reading Multivariate Tables

Answer 42

What else could determine whether a person is employed part or full time? Perhaps one’s student status also affects this: Females still more prevalent in part time workers regardless of student status Student status has a big impact on part time status