Quantitative Analysis

Question 1

Coding

Answer 1

reorganizing numerical data into a format that is easy to analyze using a computer

Code sheet
- Raw data to grid sheet then transfer data to computer file

Direct-entry method
- As info is collected it is directly entered into a software data package

Optical Scan
- Construct a questionnaire that asks respondents or allows researchers to fill in the correct dots

Bar code
- Convert data into bar codes and use a bar code reader to transfer info into a computer

Question 2

How to clean data

Answer 2

Code Cleaning
- checking for coding errors
- Looking for “impossible codes”

Contingency cleaning
- Check that codes that should correspond across different variables actually correspond

Question 3

Frequency Distributions

Answer 3

Descriptive Statistics - Describe numerical data

Univariate Statistics - Describe one variable

Frequency Distribution - A table that shows the distribution of cases into the categories of one variable

Question 4

Measures of central tendency

Answer 4

Mode - Can be used with nominal, ordinal, interval or ratio data
- distribution can have more than one mode

Bimodal - A distribution with two modes

Multimodal - Distribution with more than one mode

Question 5

Measures of central tendency - Medium

Answer 5

Meausre of central tendancy for one variable indicating the point or score at which half the cases are higher and half are lower

Easiest way to identify the median is to organize the score from highest to lowest and court to the middle

Question 6

Measures of central tendancy - Mean

Answer 6

The mean can only be used with interval or ratio level data

Complute the mean by adding up all the scores than divide by the number of score

Frequency distribution from a “normal” or bell shaped curve (normal distribution)

Skewed distribution - more cases are in the upper or lower scored

Question 7

Problems with the mean

Answer 7

The mean uses all values in a sample including extremely low and high values its vulnerable to being pulled up/down and misrepresenting the values in a sample

Question 8

Measures of Variation

Answer 8

Why care about measures of dispersion
- Reveal a great deal fo information about the differences between distributions

Range - The distance between lowest and highest scores

Range has limitations - Therefore range may exaggerate the dispersion of most scores

Percentiles - Tell the score at a specific place within the distribution

Question 9

Greater clustering

Answer 9

Greater clustering of scores around the mean in distribution for service A indicated less dispersion

Question 10

Flatter Curve

Answer 10

A flatter curve of the distribution for service B indicates more variety or dispersion

Question 11

Standard deviation

Answer 11

A meaure of dispersion for one varibale that indicated an average distance between the scores and the mean

Required an interval or ratio level measurement

It increases in value as the validity of the distribution increases

Question 12

Z scores

Answer 12

Standard deviation and the mean are used to calculate Z-scores

Because they represent standardized scores Z-scores let a researcher compare two or more distributions or groups

Question 13

Bivariate Relationship

Answer 13

Bivaraite Statistics - Statistical measures that involve two variables

Let a researcher consider two variables together and describe the relationship between variables

Correlation - Things vary together to are associated

Independance - There is no association or no relationship between variables. If two variables are independent cases with certain values on one variable do not have any particular value on the other variable

Question 14

Scattergram

Answer 14

Graph which a researcher plots each case of observation, where each axis represents the value of one variable

Used for variables at the interval-or ratio-level rarely for ordinal variables and never for nominal variables

Question 15

Scattergram Forms

Answer 15

1. Independance - no relationship - random pattern
2. Linear Relationship - A straight line an be visualized int he middle of a mazr of cases
3. Curvilinear relationship - means that the centre of a maze of cases would form a U curve, right side up or upside down, or a S curve

Direction - Linear relationships can have a positive or negative direction

Positive - line from lower left to upper right

Negative - Upper left to lower right

Percision - Bivarite relationships differ in their degree of percison

Percison is the amount of spreak in the points don’t he graph

Question 16

Bivariate Tables

Answer 16

They are present the same information as a scattergram but in table form

Cross Tabulation - Cases are organized in the table on the basis of two variables at the same time

Contingency table - Fored by cross-tabulating two or more variables

Question 17

Reading a percentaged table

Answer 17

If there is no relationship in a table the cell percentages look approx equal rows and columns

Question 18

Measure of association

Answer 18

A single number that expresses the strength, and often the direction, of a relationship. It condenses information about a bivariate relationship into a single number

Question 19

Statistical Control

Answer 19

Showing a relationship between two variables is not sufficient to say that an independent variable causes a dependent variable

To assert that a relationship exists
1. Temporal order
2. Association
3. Eliminate other explanations

Question 20

Elaboration Model

Answer 20

Trivariate Tables - Consist of multiple bivariate tables - has a bivariate table of the independent and the depended variable for each category of the control variable, these are called partials

Partials - Tables for three variables that show the association between the independent and dependent variables for each category of a control variable

Question 21

Multipl Regression

Answer 21

Is a multivariate statistical technique that allows us to break down the separate effects of the independent variables on the dependent variable

Resutls tell us
1. How well a set of variables explains a dependent variable
2. The regression results measure the direction and size of the effect of each variable ona. dependent variable

Question 22

Inferential Statisics

Answer 22

Researchers need to know that the relationships they see in samples apply to populations so they can use inferential stats

Inferential statistics rely on probability theory

Question 23

Statistical signifcance

Answer 23

Levels of significance - a way fo talking about the likelihood that results are due to chance factors - a relationshio appears in the sample when there is none in the popualtion

Question 24

Type I

Answer 24

Occurs when the researcher says that a relationship exists when in fact none exist

Question 25

Type II

Answer 25

Occurs when the researcher says there is no relationship when in fact there is