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    Chapter1 2021

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    Ahmet
    This document provides an overview of key concepts in statistics and the research process. It discusses asking a research question, formulating hypotheses by identifying independent and dependent variables, levels of measurement for variables, and discrete vs. continuous variables. It also covers collecting data through sampling or experiments and analyzing data using descriptive and inferential statistics to evaluate hypotheses. The overall goal of this process is to systematically study social relationships and contribute new evidence to research on social phenomena.

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    Chapter1 2021

    Uploaded by

    Ahmet
    17 views42 pages
    This document provides an overview of key concepts in statistics and the research process. It discusses asking a research question, formulating hypotheses by identifying independent and dependent variables, levels of measurement for variables, and discrete vs. continuous variables. It also covers collecting data through sampling or experiments and analyzing data using descriptive and inferential statistics to evaluate hypotheses. The overall goal of this process is to systematically study social relationships and contribute new evidence to research on social phenomena.

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    This document provides an overview of key concepts in statistics and the research process. It discusses asking a research question, formulating hypotheses by identifying independent and dependent variables, levels of measurement for variables, and discrete vs. continuous variables. It also covers collecting data through sampling or experiments and analyzing data using descriptive and inferential statistics to evaluate hypotheses. The overall goal of this process is to systematically study social relationships and contribute new evidence to research on social phenomena.

    Copyright:

    © All Rights Reserved
    Download as PPTX, PDF, TXT or read online from Scribd
    Download as pptx, pdf, or txt
    17 views42 pages

    Chapter1 2021

    Uploaded by

    Ahmet
    This document provides an overview of key concepts in statistics and the research process. It discusses asking a research question, formulating hypotheses by identifying independent and dependent variables, levels of measurement for variables, and discrete vs. continuous variables. It also covers collecting data through sampling or experiments and analyzing data using descriptive and inferential statistics to evaluate hypotheses. The overall goal of this process is to systematically study social relationships and contribute new evidence to research on social phenomena.

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    © All Rights Reserved
    Download as PPTX, PDF, TXT or read online from Scribd
    Download as pptx, pdf, or txt
    of 42

    Chapter 1: The What and the

    Why of Statistics
    • The Research Process
    • Asking a Research Question
    • The Role of Theory
    • Formulating the Hypotheses
    – Independent & Dependent Variables: Causality
    – Independent & Dependent Variables: Guidelines
    • Collecting Data
    – Levels of Measurement
    – Discrete and Continuous Variables
    • Analyzing Data & Evaluating Hypotheses
    – Descriptive and Inferential Statistics
    • Looking at Social Differences
    Statistics and Data
    • Statistics A set of procedures used by social scientists to
    organize, summarize, and communicate numerical
    information.

    • Only information represented by numbers can be the


    subject of statistical analysis. Such information is called
    data; researchers use statistical procedures to analyze data
    to answer research questions and test theories.
    Making Sense of Statistics
    • 5.5 ??

    • Data are numbers with a context.


    Belief is no substitute for arithmetic.
    (Henry Spencer)
    • Data beat anecdotes.

    Overall picture vs. few (powerful) incidents

    • Data also beat self-proclaimed experts!


    Figures don’t lie but liars will figure.
    (C. Grosvenor)
    • Where the data come from is important.

    • Placebo effect

    • No comparison, no conclusion.
    I have enough money to last me the rest of my life,
    unless I buy something.
    (Jackie Mason)
    • Beware the lurking variable!

    • Height and income

    • Are there any lurking variables in the background


    that influence the association?
    • Gender
    When the facts change, I change my mind. What do
    you do, sir?
    (J.M. Keynes)
    • Variation is everywhere.
    • Variability, and how it is quantifiable with an
    appropriate study:
    – Random assignment in a controlled experiment
    allows cause and effect conclusions.
    – Random sampling in a survey allows us to make
    inferences about the population of interest.
    • Conclusions are not certain.

    • Mammograms and death due to breast cancer


    – “mammography reduces the risk of dying of breast
    cancer by 26%”
    – 95% confidence interval, 17% - 34%
    It is easy to lie with statistics. But it’s easier to lie
    without them.
    (F. Mosteller)
    • Data reflects social values.

    • Perfect objectivity??
    • Statistics shares a social context that influences
    what we decide to measure and how we measure
    it.
    – Unemployment rate
    – Labor force
    The Research Process
    Examine a social relationship,
    Asking the Research study the relevant literature Formulating the
    Question Hypotheses

    Contribute Develop a
    new evidence research
    to literature THEORY design
    and begin
    again

    Evaluating the Analyzing Collecting


    Hypotheses Data Data
    Asking a Research Question
    What is Empirical Research?
    • Research based on information that can be verified by
    using our direct experience.
    • To answer research questions we cannot rely on
    reasoning, speculation, moral judgment, or subjective
    preference
    • Empirical:
    – “Are women paid less than men for the same types of
    work?”

    • Not Empirical:
    – “Is racial equality good for society?”
    • Research questions are expressed in terms of
    relationships.

    • The relationship between attributes or


    characteristics of individuals and groups lies at the
    heart of social scientific inquiry.
    The Role of Theory
    • A theory is an explanation of the relationship
    between two or more observable attributes of
    individuals or groups.

    • Social scientists use theory to attempt to establish


    a link between what we observe (the data) and
    our understanding of why certain phenomena are
    related to each other in a particular way.
    Formulating the Hypotheses
    • Hypotheses:
    – Tentative answers to research questions (subject to
    empirical verification)
    – A statement of a relationship between characteristics that
    vary (variables)

    • Variable:
    – A property of people or objects that takes on two or more
    values
    – Must include categories that are both exhaustive and
    mutually exclusive
    Units of Analysis
    The level of social life on which social scientists
    focus (individuals, groups). Examples:

    • Individual as unit of analysis:


    – What are your political views?
    • Family as unit of analysis:
    – Who does the housework?
    • Organization as unit of analysis:
    – What is the gender composition?
    • City as unit of analysis:
    – What was the crime rate last year?
    Types of Variables
    • Dependent The variable to be
    explained (the “effect”).

    • Independent The variable expected to


    account for (the “cause” of)
    the dependent variable.

    IV  DV
    Cause and Effect Relationships
    Cause and effect relationships between variables
    are not easy to infer in the social sciences.
    Causal relationships must meet three criteria:

    1. The cause has to precede the effect in time


    2. There has to be an empirical relationship
    between the cause and effect
    3. This relationship cannot be explained by other
    factors
    Correlation does not
    mean causation!
    Guidelines for Independent and
    Dependent Variables
    1. The dependent variable is always the property
    you are trying to explain; it is always the object
    of the research.
    2. The independent variable usually occurs earlier
    in time than the dependent variables.
    3. The independent variable is often seen as
    influencing, directly or indirectly, the dependent
    variable.
    Example 1
    People who attend church regularly are more likely to oppose
    abortion than people who do not attend church regularly.
    • Identify the IV and DV
    – independent variable: Church attendance
    – dependent variable: Attitudes toward abortion
    • Identify possible control variables
    Gender Age
    Religious affiliation (Catholic, Baptist, Islamic…)
    Political party identification
    • Are the causal arguments sound?
    – e.g. does party id affect abortion views or vice versa?
    Example 2
    The number of books read to a child per day positively
    affects a child’s word recognition.
    • Identify the IV and DV
    – independent variable: Number of books read
    – dependent variable: Word recognition

    • Identify possible control variables


    Gender Older siblings
    Health status Birth order
    • Are the causal arguments sound?
    – Most likely. It is hard to construct an argument where a 36
    month old child affects the number of books his or her
    parent reads to him/her.
    Collecting Data
    Examine a social relationship,
    Ask the Research study the relevant literature Formulating the
    Question Hypotheses

    Contribute Develop a
    new evidence research
    to literature THEORY design
    and begin
    again

    Evaluating the Analyzing Collecting


    Hypotheses Data
    Data
    Collecting Data

    Researchers must decide three things:

    – How to measure the variables of interest


    – How to select the cases for the research
    – What kind of data collection techniques to
    use
    Levels of Measurement
    Not every statistical operation can be used
    with every variable. The type of statistical
    operations we employ will depend on how our
    variables are measured.

    Nominal
    Ordinal
    Interval-Ratio
    Nominal Level of Measurement
    Numbers or other symbols are assigned to a set of
    categories for the purpose of naming, labeling, or
    classifying the observations.

    • Examples:
    Political Party (Democrat, Republican)
    Religion (Catholic, Jewish, Muslim, Protestant)
    Race (African American, Latino, Native
    American)
    Ordinal Level of Measurement
    Nominal variables that can be ranked from
    low to high.

    • Example: Social Class


    Upper Class
    Middle Class
    Working Class
    Interval-Ratio Level of
    Measurement
    Variables where measurements for all cases
    are expressed in the same units. (Variables
    with a natural zero point, such as height and
    weight, are called ratio variables.)
    • Examples:
    Age
    Income
    SAT scores
    Cumulative Property of Levels
    of Measurement
    • Variables that can be measured at the interval-ratio
    level of measurement can also be measured at the
    ordinal and nominal levels.
    • However, variables that are measured at the
    nominal and ordinal levels cannot be measured at
    higher levels.
    Different or Higher or How Much
    Level Equivalent Lower Higher
    Nominal Yes No No
    Ordinal Yes Yes No
    Interval-ratio Yes Yes Yes
    Cumulative Property of Levels
    of Measurement
    There is one exception, though
    • Dichotomous variables
    – Because there are only two possible values for a
    dichotomy, we can measure it at the ordinal or the
    interval-ratio level (e.g., gender)
    – There is no way to get them out of order
    – This gives the dichotomy more power than other
    nominal level variables
    Discrete and Continuous Variables
    • Discrete variables: variables that have a
    minimum-sized unit of measurement, which
    cannot be sub-divided
    – Example: the number children per family

    • Continuous variables: variables that, in


    theory, can take on all possible numerical
    values in a given interval
    – Example: length
    Analyzing Data:
    Descriptive and Inferential Statistics
    • Population: The total set of individuals, objects,
    groups, or events in which the researcher is interested.
    • Sample: A relatively small subset selected from a
    population.
    • Descriptive statistics: Procedures that help us
    organize and describe data collected from either a
    sample or a population.
    • Inferential statistics: The logic and procedures
    concerned with making predictions or inferences
    about a population from observations and analyses of a
    sample.
    Analyze Data & Evaluate Hypotheses
    Examine a social relationship,
    Asking the Research study the relevant literature Formulating the
    Question Hypotheses

    Contribute Develop a
    new evidence research
    to literature THEORY design
    and begin
    again

    Evaluating the Analyzing Collecting


    Data
    Hypotheses Data
    Begin the Process Again...
    Examine a social relationship,
    Asking the Research study the relevant literature Formulating the
    Question Hypotheses

    Contribute Develop a
    new evidence research
    to literature THEORY design
    and begin
    again

    Evaluating the Analyzing Collecting


    Hypotheses Data Data

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