Quantitative Research

• Quantitative Research


  turn observations of social life into numbers that can be analyzed STATISTICALLY

  - is numerical or can be represented using mathematics and statistics

  - involves translating social reality into measurable variables
• Quantitative Methods


  - used to collect empirical data about the social world

  - gives us a good picture of a social phenomenon
• Quantitative Methods - Advantages


  - good method for observing at the mezzo and macro-level of analysis

  - the influence of some social forces can only be observed at these levels

  - generalizability

  - time

  - cost

  - *secondary data analysis
• Quantitative Methods - Disadvantages


  - observations are abstracted away from lived experience

  - observations are limited to the variable value that researchers decide to include
• Data Collection - Original Survey


  asks people various questions related to a research question
• Population


  the universe of cases that the research question is relevant to
• Sample


  a subset of a population that is investigated empirically
• Generalizability


  the extent to which observations made about a sample can be reasonably assumed to represent a population

  - if results are ..., they give us a good picture of what the population looks like
• 2 Factors that Influence Generalizability


  1. The Sampling Procedure

  2. Sample Size -> the larger the sample size, the more likely the results are generalizable
• Sampling Procedures - Random (1) (best way to achieve generalizability)


  each individual in the population has an equal probability of being selected for study
• Sampling Procedures - Representative (2)


  the sample is a reproduction of the population along particular demographic characteristics
• Sampling Procedures - Convenient (3)


  people are sampled based on their availability
• Sampling Procedures - Snowball Sampling (4) (worst way to achieve generalizability)


  people that have been sampled, introduce the researcher to other possible study participants

  - is often the only way to sample difficult to access groups (ex: rich people)

  - usually limited to qualitative research
• Data Collection - Variables


  measurements of some phenomenon that has more than one value or score (that varies)

  - quantitative methods measure the social world as a series of...
• Data Collection Operationalization


  specifies precisely how a concept will be measured

  - translates a concept into a variable or (more often) into a series of variables

  Ex: Social Media Use:

  - Variable 1; hrs/day spent on social media (time)

  - Variable 2: # of times spent/day someone visits a social media site (how often)

  - Variable 3: # of social media sites someone engages with (amount)

  - variable type is determined by how a variable is ... (presented)
• Independent Variable


  the variable that is hypothesized to INFLUENCE the dependent variable
• Dependent Variable


  the variable that is hypothesized to be INFLUENCED by the independent variable
• Data Collection - Secondary Analysis


  when researchers use and analyze existing data in a new way (rather than collecting original data)

  PROS:

  - Sample Size

  - Sampling Technique

  - Cost

  CONS:

  - You don't get to choose the questions
• Data Collection - Data Scraping


  uses computer algorithms to generate data about people's behavior by "scraping" information about their online activity

  - is a useful tool for overcoming social desirability bias
• Social Desirability Bias


  when a person answers questions based on how they wish to appear, rather than how they actually believe

  - can be conscious or unconscious
• Data Analysis - 3 types of variables


  1. Nominal/Categorical

  2. Ordinal

  3. Ratio
• Nominal/Categorical


  - not quantitative

  - values CAN'T be ranked

  Ex:

  - race

  - neighborhood

  - marital status

  - religion

  - favorite Kardashian
• Ordinal


  - CAN be ranked, but there's NO WAY TO MEASURE the precise DIFFERENCE between ranked values

  Ex:

  - Likert scales (strongly agree, disagree, etc)

  - Socioeconomic Status/Class

  - Pain
• Ratio


  - differences between values are measurable

  - precise number

  - naturally quantitative

  - exists a real zero (limit)

  Ex:
• Data Analysis - Descriptive Statistics


  - tells us about the distribution of ONE variable

  - univariate (one variable) statistics
• Central Tendency


  measures of .... attempt to give a quick picture of the content of one variable
• Measures of Central Tendency


  1. Mode

  2. Median

  3. Mean
• Mode


  the variable that's the MOST COMMON, OR has the HIGHEST COUNT

  - For nominal variables, this is the only appropriate measure of central tendency

  - works for nominal/categorical, ordinal and ratio
• Median


  the value that separates the sample into 2 equal halves

  - The "MIDDLE VALUE"

  - the find the middle value: n+1 divided by 2

  - works for ordinal and ratio
• Mean


  the AVERAGE VALUE

  - sum of variable values divided by n (number of cases/sample size)

  - works for ratio
• Outliers


  - extreme cases (variable value is extreme relative to the majority of the distribution)

  - overinfluence the mean
• Descriptive Statistics - Proportion


  tells us the percentage of a variable that falls into one particular variable value

  - related as a value between 0-1
• Inferential Statistics


  measure the relationship between two or more variables
• 2 Types of Inferential Statistics


  1. Bivariate Statistics

  2. Multivariate Statistics
• Bivariate Statistics


  describe the relationship between 2 variables
• Multivariate Statistics


  describe the relationship between 3 or more variables
• Correlation Coefficient


  - measures the relationship between 2 RATIO level variables

  - it's therefore a bivariate statistic

  - related to number between -1 and 1

  - The further away the ... is from 0, the stronger the relationship between the 2 variables

  - 0=0 no relationship
• Positive Correlation


  when an increase in V1 is associated with an INCREASE in V2 (0-1)
• Negative Correlation


  when an increase in V1 is associated with a DECREASE in V2 (-1-0)
• Spurious


  1. x causes y

  2. y causes x

  3. the relationship between x and y is...

  when 2 variables seem to be related but aren't