< Return to Video

10.12 - Intro Review & Good Measurement Pt 2

  • 0:04 - 0:06
    Can you hear us, Rick?
  • 0:06 - 0:08
    Yeah, I can now. I forgot
  • 0:08 - 0:10
    I didn't have on my headphones.
  • 0:12 - 0:15
    I thought it might
    be helpful to go
  • 0:15 - 0:19
    over some of the assignments
    that are due next week.
  • 0:19 - 0:24
    Can everyone see
    the Canvas page?
  • 0:24 - 0:26
    Yes. Great.
  • 0:26 - 0:30
    I opened up Module 4,
  • 0:30 - 0:31
    which will include
    the introduction,
  • 0:31 - 0:33
    and I just wanted to give you
  • 0:33 - 0:35
    a brief overview of that today.
  • 0:35 - 0:40
    I have a video
  • 0:40 - 0:44
    I recorded for another
    research methods class,
  • 0:44 - 0:45
    and I can put that up as well,
  • 0:45 - 0:47
    but we'll be talking
    about it today.
  • 0:47 - 0:53
    That peer review is not
    happening. Here we go.
  • 0:53 - 0:56
    There's an introduction
    template that I wrote that
  • 0:56 - 0:59
    could help you write the paper.
  • 0:59 - 1:04
    I also want you to check out
    the actual submission link.
  • 1:04 - 1:06
    Within the submission link,
  • 1:06 - 1:08
    you'll get more information
  • 1:08 - 1:09
    about how to write
    the introduction.
  • 1:09 - 1:12
    But I think the most
    important component
  • 1:12 - 1:16
    of this will be to take
    a look at the rubric.
  • 1:16 - 1:18
    Because when you take
    a look at the rubric,
  • 1:18 - 1:20
    you'll be able to tell what
  • 1:20 - 1:23
    I'm placing most of
    the attention on.
  • 1:25 - 1:28
    This whole thing is out of 50,
  • 1:28 - 1:30
    but in reality,
  • 1:30 - 1:33
    the introduction will
    only be worth 25 points,
  • 1:33 - 1:34
    so you have to divide it by two.
  • 1:34 - 1:38
    As you can see,
  • 1:38 - 1:40
    just by looking at
    the point values,
  • 1:40 - 1:42
    what really matters is
  • 1:42 - 1:45
    the main body of the intro,
    so the literature review.
  • 1:45 - 1:47
    Most of you are
    already working on
  • 1:47 - 1:49
    that with your Assignment 1B,
  • 1:49 - 1:51
    looking for different
    articles that might be
  • 1:51 - 1:54
    relevant to your
    topic of interest.
  • 1:54 - 1:56
    But let's go through
    the opening paragraph.
  • 1:56 - 1:58
    First, you have to put a title
  • 1:58 - 2:00
    before your first paragraph.
  • 2:00 - 2:02
    That's half a point.
    Should I make it
  • 2:02 - 2:04
    bigger or is it too small?
  • 2:04 - 2:05
    A little bigger would be better.
  • 2:05 - 2:08
    I'll do that. Give me a second.
  • 2:11 - 2:13
    How about there?
  • 2:13 - 2:17
    Is that good? Title before
  • 2:17 - 2:19
    the first paragraph
    is half a point,
  • 2:19 - 2:22
    a catchy opening hook to
    grab the reader's attention.
  • 2:22 - 2:25
    Hailey, what's your topic?
  • 2:27 - 2:29
    It's about sexism in
  • 2:29 - 2:34
    the workplace for women in
    higher positions of power.
  • 2:34 - 2:37
    It could be like a catchy hook.
  • 2:37 - 2:39
    It could be cheesy right
    now. It doesn't matter.
  • 2:39 - 2:48
    [LAUGHTER] This is
    going to be really bad,
  • 2:48 - 2:49
    but I just thought of it
  • 2:49 - 2:52
    like imagine you're
    a woman at work
  • 2:52 - 2:55
    and one of your male colleagues
  • 2:55 - 2:58
    smacks your butt on your way in.
  • 2:58 - 3:02
    Though this is less likely to
    be a reality for women now.
  • 3:02 - 3:05
    This is really common, like
    in the '50s and the '60s.
  • 3:05 - 3:07
    Although overt sexual harassment
  • 3:07 - 3:11
    is less likely to happen now
    to women in the workplace,
  • 3:11 - 3:14
    sexual harassment is
    still an issue for women,
  • 3:14 - 3:16
    and then you can keep talking.
  • 3:16 - 3:18
    If I read that, I'd
    be like, my God, no.
  • 3:18 - 3:21
    I want to know what happens
    to this guy if he smacked
  • 3:21 - 3:24
    my butt. Something like that.
  • 3:25 - 3:29
    State the big picture or
    problem and its importance.
  • 3:29 - 3:34
    Although sexual harassment might
  • 3:34 - 3:37
    not be as overt
    as it was before,
  • 3:37 - 3:39
    women in the workplace
  • 3:39 - 3:41
    still experience
    sexual harassment.
  • 3:41 - 3:44
    That is the importance
    in the big picture.
  • 3:44 - 3:47
    Frames the context of the
    story to be discussed.
  • 3:47 - 3:50
    It depends on what exactly
    you'll be looking at.
  • 3:50 - 3:53
    Hailey, what exactly
    will you be looking at?
  • 3:53 - 3:56
    Like perceptions of sexual
    harassment or the behaviors?
  • 3:56 - 4:01
    I really want to look at how it
  • 4:01 - 4:03
    affects the women and
  • 4:03 - 4:07
    their ability to continue
    on in their career choice.
  • 4:07 - 4:09
    Got it. Maybe you'll
  • 4:09 - 4:12
    recruit women and you'll
    have them recall a time
  • 4:12 - 4:14
    when they've experienced
    sexual harassment
  • 4:14 - 4:17
    or imagine a scenario
    where they experienced
  • 4:17 - 4:21
    sexual harassment and then
  • 4:21 - 4:22
    assess the likelihood
    that they'd
  • 4:22 - 4:24
    be able to continue
    in their job,
  • 4:24 - 4:25
    that they'd want to persist,
  • 4:25 - 4:28
    that they feel
    like they're safe,
  • 4:28 - 4:31
    that they could seek mentors
    and so on and so forth.
  • 4:31 - 4:34
    When you're framing the context
  • 4:34 - 4:35
    of the story to be discussed,
  • 4:35 - 4:38
    you're going to talk about
    what your interest is.
  • 4:38 - 4:42
    Then you'll provide a brief
    outline of the paper.
  • 4:47 - 4:49
    In this research paper,
  • 4:49 - 4:52
    I will be discussing
    historical articles
  • 4:52 - 4:54
    on women's experiences of
  • 4:54 - 4:55
    sexual harassment
    in the workplace,
  • 4:55 - 4:57
    the types of sexual harassment
  • 4:57 - 5:00
    that are typically
    likely to occur,
  • 5:00 - 5:02
    other people's perceptions of
  • 5:02 - 5:04
    sexual harassment
    in the workplace,
  • 5:04 - 5:08
    and finally, women's
    likelihood of
  • 5:08 - 5:10
    persisting in the
    workplace after
  • 5:10 - 5:11
    experiencing a trauma like
  • 5:11 - 5:13
    sexual harassment
    in the workplace.
  • 5:13 - 5:15
    That summary is
    basically going to
  • 5:15 - 5:17
    be the four or five articles
    you're going to talk about.
  • 5:17 - 5:19
    You're just talking
    about the main points,
  • 5:19 - 5:22
    and then boom, you're done
    with the opening paragraph.
  • 5:22 - 5:25
    The problems are things that you
  • 5:25 - 5:28
    might get dock points on.
  • 5:28 - 5:32
    I would try to just get
    as many points here.
  • 5:32 - 5:34
    I won't really dock points here,
  • 5:34 - 5:35
    but I will note these as
  • 5:35 - 5:39
    problems so that you can
    work on it in the future.
  • 5:39 - 5:42
    If your introduction appears
  • 5:42 - 5:43
    instead of the paper's title,
  • 5:43 - 5:45
    no, I want your title.
  • 5:45 - 5:47
    If you don't introduce properly
  • 5:47 - 5:49
    before getting into details,
  • 5:49 - 5:52
    so don't have a
    catchy opening hook,
  • 5:52 - 5:54
    that is not great.
  • 5:54 - 5:57
    The statement of the problem
    is too brief or lacking.
  • 5:57 - 5:59
    Does not provide a
    framework for the story,
  • 5:59 - 6:01
    so you're not telling
    me what you will
  • 6:01 - 6:04
    tell me in the future
    or how you're going to
  • 6:04 - 6:06
    outline or make an argument for
  • 6:06 - 6:10
    your research paper
    and other things.
  • 6:10 - 6:13
    It could be like citing
    issues and things like that.
  • 6:13 - 6:15
    Then for the literature review,
  • 6:15 - 6:18
    this is going to be the
    bulk of your paper.
  • 6:18 - 6:21
    These will be like the
    four or five studies that
  • 6:21 - 6:23
    you'll be talking
    about that you may
  • 6:23 - 6:26
    have already done for
    your Assignment 1B.
  • 6:26 - 6:29
    I want you to describe the
    relevant parts of each study.
  • 6:29 - 6:32
    Hailey, have you read any
    papers for your study yet?
  • 6:32 - 6:37
    I haven't completed
    the assignment yet,
  • 6:37 - 6:40
    but I have five articles
    picked out for it.
  • 6:40 - 6:41
    Cool.
  • 6:41 - 6:49
    One is how male allies can
    help women in the workplace.
  • 6:49 - 6:56
    That one just gives an
    account from male allies
  • 6:56 - 6:58
    in the workplace and how they
  • 6:58 - 7:00
    have helped in the past and how
  • 7:00 - 7:02
    the women want them to
    help in the future.
  • 7:02 - 7:05
    I think that would be great.
  • 7:05 - 7:07
    It might not be one of
    the first paragraphs.
  • 7:07 - 7:09
    It might be one of the
    ending paragraphs,
  • 7:09 - 7:12
    but I would want you to evaluate
  • 7:12 - 7:13
    the importance of each study
  • 7:13 - 7:14
    and describe the relevant parts.
  • 7:14 - 7:18
    How did they collect
    data from these males?
  • 7:18 - 7:20
    How did they identify allies?
  • 7:20 - 7:24
    What behaviors did they
    describe as allied behavior?
  • 7:24 - 7:27
    How did women perceive
    these behaviors?
  • 7:27 - 7:30
    That would be good.
    I also want you to
  • 7:30 - 7:33
    tie findings together and
    exploit common themes.
  • 7:33 - 7:35
    Across all of the five papers
  • 7:35 - 7:36
    that you're reading, of course,
  • 7:36 - 7:38
    they will be distinct,
    but they will share
  • 7:38 - 7:39
    a similar theme of women
  • 7:39 - 7:41
    experiencing sexual
    harassment in the workplace.
  • 7:41 - 7:46
    I want you to find similarities
    and differences between
  • 7:46 - 7:48
    them and also to cite
  • 7:48 - 7:52
    your paper or your
    articles in APA format.
  • 7:52 - 7:54
    This is why the citing
    paper comes into play.
  • 7:54 - 7:57
    I'll be grading that today.
  • 7:57 - 7:59
    Great. The problems would
  • 7:59 - 8:01
    be an insufficient
    literature review.
  • 8:01 - 8:05
    Maybe you just talk
    about two studies.
  • 8:05 - 8:08
    This happened before you give
    me the title of the paper,
  • 8:08 - 8:10
    but you don't really tell me
    what the paper was about.
  • 8:10 - 8:13
    That really doesn't
    give me an idea
  • 8:13 - 8:20
    of what research you've read,
  • 8:20 - 8:22
    that leads you to
    ask this question.
  • 8:22 - 8:25
    I don't want all the details
    about the study methods.
  • 8:25 - 8:26
    I don't want you to tell me they
  • 8:26 - 8:28
    recruited males from Portland,
  • 8:28 - 8:29
    Oregon on Tuesday and
  • 8:29 - 8:33
    Thursday because those are
    the days that males go out.
  • 8:33 - 8:35
    I don't want to know
    all that information.
  • 8:35 - 8:37
    I just want to know general.
  • 8:38 - 8:40
    You don't discuss the
    importance of each study,
  • 8:40 - 8:43
    so, of course,
    describe the study.
  • 8:44 - 8:46
    Main claims and supporting
  • 8:46 - 8:47
    evidence not clear
    from citations.
  • 8:47 - 8:50
    Again, don't just put
    the title of the study.
  • 8:50 - 8:54
    Give me some summary.
  • 8:54 - 8:56
    Summarizes article after article
  • 8:56 - 8:58
    instead of synthesizing
    research findings.
  • 8:58 - 9:00
    I do want you to
    include a summary,
  • 9:00 - 9:02
    but you should also have
  • 9:02 - 9:05
    a common thread
    throughout and talk
  • 9:05 - 9:07
    about how the
    articles inform one
  • 9:07 - 9:08
    another and how they
  • 9:08 - 9:11
    help you form your
    research question.
  • 9:12 - 9:15
    Improper APA
    citations and other.
  • 9:15 - 9:18
    Of course, APA is going to
    be important throughout.
  • 9:19 - 9:21
    After you write the bulk of
  • 9:21 - 9:23
    your research or your
    literature review,
  • 9:23 - 9:25
    you'll have a
    paragraph identifying
  • 9:25 - 9:27
    gaps to motivate your study.
  • 9:27 - 9:30
    For instance, for Hailey,
    it would be something like,
  • 9:30 - 9:35
    although research has examined
    how male allies can help
  • 9:35 - 9:38
    women experiencing
    sexual harassment
  • 9:38 - 9:40
    in the workplace,
    limited research.
  • 9:40 - 9:42
    This might not be true,
    but I'm just spitballing.
  • 9:42 - 9:45
    Limited research
    has investigated
  • 9:45 - 9:49
    how sexual harassment affects
    women's persistence in
  • 9:49 - 9:53
    the workplace and what
    strategies women can
  • 9:53 - 9:55
    engage in for themselves
  • 9:55 - 9:59
    to deal with sexual
    harassment in the workplace.
  • 10:00 - 10:04
    You'd compare and contrast
    findings across studies.
  • 10:04 - 10:06
    This is where you can
    include a sentence or
  • 10:06 - 10:06
    two for each of
  • 10:06 - 10:08
    the studies that you've
    already discussed.
  • 10:08 - 10:10
    Talk about the limitations of
  • 10:10 - 10:13
    previous work and gaps
    in the literature.
  • 10:15 - 10:18
    Sometimes allies are people
  • 10:18 - 10:22
    who exhibit sexual harassment
    towards other people,
  • 10:22 - 10:27
    so maybe talk about how
    allies are important,
  • 10:27 - 10:29
    but they can also be
  • 10:29 - 10:32
    perpetrators of sexual
    harassment, etc.
  • 10:32 - 10:34
    Reiterate your main claims and
  • 10:34 - 10:36
    outline evidence to set
  • 10:36 - 10:37
    the context for
    your current study,
  • 10:37 - 10:40
    and have APA
    citations throughout.
  • 10:40 - 10:43
    If you don't compare and
    contrast, that's not great,
  • 10:43 - 10:45
    if you don't critically evaluate
  • 10:45 - 10:48
    the limitations of the
    work, that's not great,
  • 10:48 - 10:51
    if you don't summarize
    everything that
  • 10:51 - 10:52
    you've talked about so far
  • 10:52 - 10:55
    before jumping into
    the current study,
  • 10:55 - 10:56
    that's also not great.
  • 10:56 - 10:59
    Then your final paragraph will
  • 10:59 - 11:03
    be talking about your
    study more specifically.
  • 11:03 - 11:06
    Hailey, the purpose
    of my study is to
  • 11:06 - 11:10
    see how women experience,
  • 11:10 - 11:12
    sexual harassment
    in the workplace,
  • 11:12 - 11:15
    and how that might
    affect their persistence
  • 11:15 - 11:19
    in mostly male dominated
    job or something.
  • 11:19 - 11:21
    Describe how your
    experimental variables
  • 11:21 - 11:23
    will be manipulated
    and measured.
  • 11:23 - 11:27
    Participants will be assigned
    to one of two conditions.
  • 11:27 - 11:30
    In condition 1, participants
    will be asked to either
  • 11:30 - 11:32
    recall or imagine a scenario
  • 11:32 - 11:34
    where they experience
    sexual harassment,
  • 11:34 - 11:36
    or in the second scenario,
  • 11:36 - 11:37
    participants will be asked to
  • 11:37 - 11:41
    recall one of their job
    experiences just generally.
  • 11:41 - 11:44
    Then all participants
    will complete
  • 11:44 - 11:48
    measures of persistence
    in the workplace,
  • 11:48 - 11:52
    and other things, whatever
    your dependent variables are.
  • 11:52 - 11:55
    Then your testable hypotheses.
  • 11:55 - 11:58
    I predict that people in
  • 11:58 - 12:00
    the sexual harassment condition
  • 12:00 - 12:02
    will exhibit lower levels of
  • 12:02 - 12:05
    persistence in the
    workplace compared to women
  • 12:05 - 12:09
    who describe your
    typical job scenario.
  • 12:10 - 12:14
    The problems might be
    lacking theoretical support,
  • 12:14 - 12:16
    but that's where
    your intro comes in.
  • 12:16 - 12:17
    That's where you're
    talking about
  • 12:17 - 12:18
    the articles that actually
  • 12:18 - 12:21
    helped you come up
    with this theory.
  • 12:21 - 12:24
    I do want you to talk about
    your IVs and your DVs,
  • 12:24 - 12:26
    so maybe you'll talk
    about persistence.
  • 12:26 - 12:28
    Persistence will be measured by
  • 12:28 - 12:31
    the everyday persistence
    scale created
  • 12:31 - 12:34
    by Chatterley and
    colleagues in 2020.
  • 12:35 - 12:38
    If you don't tell me
    what your hypothesis is,
  • 12:38 - 12:40
    then that's not great.
  • 12:40 - 12:42
    If I don't know what
    your hypothesis is,
  • 12:42 - 12:45
    or it seems unclear because
  • 12:45 - 12:47
    of the lack of information
    from prior studies,
  • 12:47 - 12:49
    it's also not great, and if it's
  • 12:49 - 12:50
    unclear how your study will test
  • 12:50 - 12:52
    hypotheses, it's also not great.
  • 12:52 - 12:54
    Let's say Hailey
    said something like,
  • 12:54 - 12:55
    "I'm interested in
    seeing persistent rates
  • 12:55 - 12:58
    among women who experienced
    sexual harassment.
  • 12:58 - 13:01
    I'm going to recruit
    10 male participants,
  • 13:01 - 13:03
    and I'm going to ask
    them about how many
  • 13:03 - 13:06
    times they've sexually
    harassed a woman."
  • 13:06 - 13:09
    That's not answering the
    question that she posed.
  • 13:09 - 13:11
    That's completely different.
  • 13:12 - 13:14
    Now your references.
  • 13:14 - 13:17
    Again, references are
    pretty important APA style.
  • 13:17 - 13:20
    Having a title that
    says references,
  • 13:20 - 13:23
    using APA style,
  • 13:23 - 13:24
    everything you cited in
  • 13:24 - 13:26
    your paper appears in
    the references and
  • 13:26 - 13:30
    having at least four
    papers that you can cite.
  • 13:30 - 13:32
    Of course, you should
    already have five with
  • 13:32 - 13:34
    assignment 1B, so
    that should be great.
  • 13:34 - 13:39
    The problems could be it's
    not formatted correctly,
  • 13:39 - 13:42
    APA style isn't followed
    or used inconsistently.
  • 13:42 - 13:43
    That's why I'm going
    to try to get you
  • 13:43 - 13:45
    feedback by the end of today,
  • 13:45 - 13:46
    if not the end of
    tomorrow on your site me,
  • 13:46 - 13:48
    so that you know if
  • 13:48 - 13:51
    you did anything wrong,
    what you can do to improve.
  • 13:51 - 13:53
    They have to be in
    alphabetical order.
  • 13:53 - 13:56
    If you use less
    than four sources,
  • 13:56 - 13:58
    you will be docked points,
  • 13:58 - 14:00
    does not match the text.
  • 14:00 - 14:04
    Hailey, if you cited Chatterley
    and colleagues in 2020,
  • 14:04 - 14:08
    but then you use Tingle
    and colleagues 2020,
  • 14:08 - 14:09
    then I would be
    like, "Well, what
  • 14:09 - 14:11
    happened to the other citation?"
  • 14:11 - 14:14
    Too many secondary
    sources cited.
  • 14:14 - 14:17
    So this would be using
    data from CNN to see
  • 14:17 - 14:19
    how many women experience
  • 14:19 - 14:22
    sexual harassment and not
    enough empirical articles.
  • 14:22 - 14:24
    You can use secondary sources,
  • 14:24 - 14:26
    but try not to use
    more than two.
  • 14:26 - 14:28
    Then just overall format,
  • 14:28 - 14:32
    your organization, your
    grammar, your spelling,
  • 14:32 - 14:33
    the length is sufficient for
  • 14:33 - 14:35
    the full picture of the problem,
  • 14:35 - 14:37
    your margins are good,
  • 14:37 - 14:38
    your double spacing is good,
  • 14:38 - 14:40
    your indentation's good,
  • 14:40 - 14:41
    you're using 12-point font,
  • 14:41 - 14:43
    and you have page numbers.
  • 14:43 - 14:46
    You don't start out
    broadly and narrowly.
  • 14:46 - 14:48
    Of course, Hailey
    with your paper,
  • 14:48 - 14:49
    you're going to be like, "Women
  • 14:49 - 14:51
    used to experience
    sexual harassment."
  • 14:51 - 14:53
    That'll be really broad,
  • 14:53 - 14:55
    and then you'll go very
    detailed because you'll talk
  • 14:55 - 14:58
    about your experiment
    specifically.
  • 15:02 - 15:05
    It is a lot easier
    to paraphrase.
  • 15:05 - 15:06
    If you're having trouble
    paraphrasing something,
  • 15:06 - 15:08
    let me know and I can help
  • 15:08 - 15:11
    using active rather
    than passive voice.
  • 15:11 - 15:13
    Don't give personal opinions.
  • 15:13 - 15:14
    If your margins
    are inconsistent,
  • 15:14 - 15:16
    your double spacing
    is inconsistent,
  • 15:16 - 15:18
    consistent font is not used,
  • 15:18 - 15:20
    page number is inconsistent
    in the wrong place,
  • 15:20 - 15:21
    you spell check isn't used,
  • 15:21 - 15:23
    they're choppy transitions,
  • 15:23 - 15:24
    they're fragmented sentences,
  • 15:24 - 15:26
    length is insufficient to
  • 15:26 - 15:28
    capture the breath of a problem.
  • 15:28 - 15:31
    After all that, you
    can get 50 points,
  • 15:31 - 15:35
    you divide it by 20, and
    that'll be worth 25 points.
  • 15:35 - 15:37
    Just to let you know,
  • 15:37 - 15:43
    I do have a way of grading
    all of these papers.
  • 15:43 - 15:45
    The first draft is worth less,
  • 15:45 - 15:48
    and then the second
    draft is worth more than
  • 15:48 - 15:50
    the final draft is
    worth the most.
  • 15:50 - 15:51
    I do that because I know you're
  • 15:51 - 15:53
    still learning how
    to write a paper,
  • 15:53 - 15:55
    so if you do get docked
    points on the intro,
  • 15:55 - 15:56
    I want you to be able to gain
  • 15:56 - 16:00
    those points by doing
    better on the second draft.
  • 16:00 - 16:03
    The points that you
    might lose here aren't
  • 16:03 - 16:05
    as meaningful as they would be
  • 16:05 - 16:07
    for the second or
    the third draft.
  • 16:07 - 16:08
    Once you write the second draft,
  • 16:08 - 16:10
    it will include a revised intro,
  • 16:10 - 16:13
    and then your first draft
    of your method and results.
  • 16:13 - 16:17
    Then your final draft will
    include your revised intro,
  • 16:17 - 16:20
    revised method, and results
    and a discussion section.
  • 16:20 - 16:22
    You're always going
    to get feedback,
  • 16:22 - 16:24
    and my goal is
  • 16:24 - 16:26
    not for you to get it
    perfect the first time,
  • 16:26 - 16:28
    because I remember this
    was really hard for
  • 16:28 - 16:30
    me when I learned how
    to write a paper,
  • 16:30 - 16:33
    so I want to see improvement.
  • 16:33 - 16:35
    I don't expect everyone
    to get a perfect score,
  • 16:35 - 16:38
    but I would like to
    see improvement.
  • 16:38 - 16:42
    Are there any questions here?
  • 16:43 - 16:46
    I will be uploading a
    sample paper as well,
  • 16:46 - 16:51
    but I did want to just give
    you a brief introduction.
  • 16:51 - 16:53
    But, how do I write
    an introduction?
  • 16:53 - 16:56
    I feel the rubric is
    good for telling you,
  • 16:56 - 16:59
    like, "These are
    the things that you
  • 16:59 - 17:02
    should and shouldn't
    do to get the points.
  • 17:02 - 17:06
    But I wrote this template
  • 17:06 - 17:11
    that you can use when you're
    writing your introduction."
  • 17:11 - 17:14
    Let me open it up
    in Word document
  • 17:14 - 17:15
    because this looks weird.
  • 17:15 - 17:18
    I'm going to stop the share,
  • 17:20 - 17:25
    and let me get my
    Word document open.
  • 17:25 - 17:28
    That's a lot of information
    I've thrown at you-all.
  • 17:28 - 17:30
    Any questions so far?
  • 17:31 - 17:34
    I really want to read
    Hailey's paper now.
  • 17:35 - 17:39
    Let me share the screen again.
  • 17:41 - 17:44
    Great, and I'm going
    to make it bigger.
  • 17:50 - 17:52
    I created this document to
  • 17:52 - 17:54
    help you on the
    upcoming assignment.
  • 17:54 - 17:58
    You can read all of this.
  • 17:58 - 18:00
    But the author of your textbook
  • 18:00 - 18:02
    recommends you write
    the main points of
  • 18:02 - 18:04
    an argument and individual
    research findings
  • 18:04 - 18:06
    on index card,
  • 18:06 - 18:08
    so then you can
    rearrange them to
  • 18:08 - 18:10
    figure out what makes
    the most sense.
  • 18:10 - 18:11
    Let me get the sun out of
  • 18:11 - 18:15
    my face so to figure out
    what makes the most sense.
  • 18:15 - 18:18
    You can also do this by
    writing the topic sentence
  • 18:18 - 18:20
    of each paragraph in this
    Word document and then
  • 18:20 - 18:21
    finding support for
    your arguments from
  • 18:21 - 18:23
    the five or more articles you've
  • 18:23 - 18:27
    reviewed for Assignment 1B.
  • 18:27 - 18:31
    Things to keep in mind.
    Your intro should be
  • 18:31 - 18:35
    between five to maybe
    seven paragraphs.
  • 18:35 - 18:38
    That might be anywhere
    between 3-5 pages,
  • 18:38 - 18:42
    so it shouldn't be that intense.
  • 18:42 - 18:45
    The most difficult part
    is finding how to weave
  • 18:45 - 18:47
    the entire narrative through
  • 18:47 - 18:49
    the article summaries
    that you have.
  • 18:49 - 18:51
    The first sentence
    of each paragraph
  • 18:51 - 18:53
    should be a topic sentence.
  • 18:53 - 18:55
    For instance, if Hailey is
  • 18:55 - 18:57
    going to be talking
    about male allies,
  • 18:57 - 19:03
    so she could say
    something like, men,
  • 19:03 - 19:08
    not only women can provide
    support for those who are
  • 19:08 - 19:11
    experiencing sexual
    harassment and
  • 19:11 - 19:14
    also to prevent sexual
    harassment from re-occurring.
  • 19:14 - 19:17
    Then you'll go into that study.
  • 19:17 - 19:19
    The topic sentence makes a claim
  • 19:19 - 19:20
    that the following sentences
  • 19:20 - 19:23
    support using research
    findings as evidence.
  • 19:23 - 19:26
    Chatterly, is it Chatterly?
  • 19:26 - 19:29
    Chatterley and
    colleagues conducted
  • 19:29 - 19:33
    a study where men were recruited
    and so on and so forth,
  • 19:33 - 19:40
    and then you go on to the
    summary of the study.
  • 19:41 - 19:44
    Respected and
    experienced researchers,
  • 19:44 - 19:46
    researchers do not
    use many, if any,
  • 19:46 - 19:49
    quotes when writing,
    so try to paraphrase.
  • 19:49 - 19:51
    You can always paraphrase.
  • 19:51 - 19:53
    When discussing the
    findings of past research,
  • 19:53 - 19:55
    only provide as
    much detail about
  • 19:55 - 19:57
    the researcher's method as
  • 19:57 - 20:00
    is necessary for your reader
    to understand the results.
  • 20:00 - 20:02
    I don't want how many
    people they collected,
  • 20:02 - 20:04
    I don't want when
    they collected it.
  • 20:04 - 20:08
    I just want maybe to know that
    all the participants were
  • 20:08 - 20:10
    self-identified as male and
  • 20:10 - 20:14
    what questions they were
    asked about allied behaviors.
  • 20:14 - 20:18
    If I'm curious about the
    methodological details,
  • 20:18 - 20:19
    I think it's really interesting,
  • 20:19 - 20:21
    I can always look up
    the information myself
  • 20:21 - 20:24
    by using the references
    that you've cited.
  • 20:25 - 20:28
    Sure. Go ahead.
  • 20:29 - 20:34
    Well, this is all for
    the intro, so okay.
  • 20:34 - 20:38
    Yeah. Do you have a question
    about it, or is it?
  • 20:38 - 20:42
    It just caught me right there.
  • 20:42 - 20:43
    I just go, okay,
  • 20:43 - 20:46
    that's a lot, but I'm
    following it. Sure.
  • 20:46 - 20:51
    It is a lot. I feel
  • 20:51 - 20:52
    like I would rather give you
  • 20:52 - 20:55
    more information
    than not enough.
  • 20:55 - 20:58
    This is helpful. A
    guideline is what I need.
  • 20:58 - 21:02
    Yeah. This is two pages long,
  • 21:02 - 21:04
    so I'll show you what I created.
  • 21:04 - 21:07
    There's just like things
    to keep in mind generally,
  • 21:07 - 21:12
    and then I have paragraph
    outlines that you can use.
  • 21:12 - 21:15
    They can end each paragraph
    with a sentence communicating
  • 21:15 - 21:16
    the implications of a finding
  • 21:16 - 21:18
    you discussed in the paragraph.
  • 21:18 - 21:19
    I want you to answer
    the question,
  • 21:19 - 21:21
    so what about the
    research you summarized?
  • 21:21 - 21:23
    This tells the reader what
  • 21:23 - 21:26
    they should have taken
    away from that paragraph.
  • 21:26 - 21:29
    The research from
    Chatterley and colleagues
  • 21:29 - 21:31
    suggests that males are
  • 21:31 - 21:33
    important sources of preventing
  • 21:33 - 21:36
    future instances of
    sexual harassment.
  • 21:38 - 21:43
    These are outlines
    for your paper.
  • 21:43 - 21:47
    You could use this. Begin your
    introduction with a hook,
  • 21:47 - 21:49
    something that is general.
  • 21:50 - 21:56
    What did I say? Imagine
    you walk into work and
  • 21:56 - 22:05
    your male colleague
    slaps your behind.
  • 22:05 - 22:09
    Though this might not represent
  • 22:09 - 22:17
    the sexual harassment that
    women experience in the work.
  • 22:19 - 22:25
    Women do still experience
    sexual harassment.
  • 22:28 - 22:32
    The present study will
  • 22:32 - 22:36
    explore the ways in
    which women experience
  • 22:36 - 22:41
    sexual harassment
    and the consequences
  • 22:41 - 22:48
    of sexual harassment for
    persistence in the workplace.
  • 22:48 - 22:52
    First, I will outline.
  • 22:52 - 22:56
    Next, we will discuss
  • 22:56 - 23:01
    and then following,
    blah. Then finally,
  • 23:10 - 23:14
    you talk about your
    actual experiment.
  • 23:14 - 23:16
    First, I said,
  • 23:16 - 23:19
    then your first
    paragraph follows.
  • 23:19 - 23:23
    That could be the
    first paragraph in
  • 23:23 - 23:28
    your intro. Does
    that make sense?
  • 23:29 - 23:31
    It's something really broad.
  • 23:31 - 23:34
    You talk about your
    present study,
  • 23:34 - 23:36
    and then you give an outline
  • 23:36 - 23:37
    of what you'll be talking about.
  • 23:37 - 23:41
    You're telling me what
    main points I should get.
  • 23:41 - 23:43
    The next sentence
    should introduce how
  • 23:43 - 23:45
    your hook relates to
    your research question.
  • 23:45 - 23:47
    Though this might not represent
  • 23:47 - 23:48
    the sexual harassment that women
  • 23:48 - 23:49
    experience in the workplace.
  • 23:49 - 23:53
    Today, women do still
    experience sexual harassment.
  • 23:53 - 23:56
    The next few sentences should
    discuss existing research
  • 23:56 - 23:57
    broadly and also outline
  • 23:57 - 23:59
    the limits of the
    current research.
  • 23:59 - 24:02
    How your research question
    will address the existing gap.
  • 24:02 - 24:05
    These sentences should
    foreshadow what you will be
  • 24:05 - 24:08
    discussing in the
    following paragraphs.
  • 24:08 - 24:10
    The present study will
    explore the ways in
  • 24:10 - 24:12
    which women experience
    sexual harassment and
  • 24:12 - 24:13
    the consequences of
    sexual harassment
  • 24:13 - 24:15
    for persistence
    in the workplace.
  • 24:15 - 24:17
    First, you're going
    to outline law.
  • 24:17 - 24:19
    Next, we'll discuss
    that following,
  • 24:19 - 24:21
    blah and then finally,
  • 24:21 - 24:29
    I will explore how these
    studies tie together.
  • 24:29 - 24:37
    But I'd be more specific to
    inform the current study.
  • 24:37 - 24:39
    The final sentence should
    be about your study
  • 24:39 - 24:41
    that outline your IV
    and your levels of it,
  • 24:41 - 24:43
    your dependent variable and
  • 24:43 - 24:46
    your predictions, and
    so on and so forth.
  • 24:46 - 24:47
    I have a question.
  • 24:47 - 24:48
    Yes, go for it.
  • 24:48 - 24:51
    About the existing gap.
  • 24:52 - 24:56
    Could you give an example
    or explain a little bit?
  • 24:56 - 24:57
    I missed really.
  • 24:57 - 24:59
    Sure.
  • 25:02 - 25:06
    How your research question
    will address that?
  • 25:08 - 25:17
    Limited research to date
    has directly examined
  • 25:17 - 25:23
    the consequences of
    sexual harassment
  • 25:23 - 25:28
    for persistence
    in the workplace.
  • 25:32 - 25:38
    You want to show work that
    needs to be done, but hasn't.
  • 25:39 - 25:42
    You can say that about
    mostly any study
  • 25:42 - 25:47
    because there are specific
    studies that are out there,
  • 25:47 - 25:50
    but there are always ways
    to make studies unique.
  • 25:50 - 25:53
    Right there at that point,
  • 25:53 - 25:59
    you want to state how years
    is going to be unique?
  • 25:59 - 26:02
    How this study is
    going to fill that.
  • 26:02 - 26:06
    Yeah. Rick, your study is
    about COVID-19, right?
  • 26:06 - 26:11
    Well, yeah, depression due to
    isolation from COVID, yeah.
  • 26:11 - 26:15
    Right. Your intro could
    be something like,
  • 26:15 - 26:17
    in 2019 and before,
  • 26:17 - 26:20
    most people would say that
  • 26:20 - 26:22
    they'd love to stay home
    and watch Netflix all day.
  • 26:22 - 26:26
    In 2020, that became a reality.
  • 26:26 - 26:28
    But what people didn't
    anticipate is that they would
  • 26:28 - 26:31
    experience increasing
    levels of loneliness.
  • 26:31 - 26:34
    The COVID-19 pandemic
    was unprecedented,
  • 26:34 - 26:38
    and people experience
    increased people
  • 26:38 - 26:40
    may have experienced
    increased sense
  • 26:40 - 26:43
    of loneliness and depression.
  • 26:43 - 26:45
    Almost no research to date
  • 26:45 - 26:49
    exists to examine
    how pandemics and
  • 26:49 - 26:51
    quarantining has effects on
  • 26:51 - 26:54
    people's emotional and
    psychological state.
  • 26:54 - 26:58
    The current research
    will explore law.
  • 26:58 - 27:00
    Because that's, like,
    brand new research.
  • 27:00 - 27:03
    Well, I did found
    some good ones.
  • 27:03 - 27:05
    Yeah, so there have been papers
  • 27:05 - 27:07
    that have been published
    really quickly.
  • 27:07 - 27:10
    But prior to those papers,
  • 27:10 - 27:12
    there's been really
    nothing because we weren't
  • 27:12 - 27:16
    doing this research
    during the Spanish flu.
  • 27:16 - 27:21
    But those statements can be
    made almost about any paper.
  • 27:21 - 27:23
    Thus, the present
    study will explore how
  • 27:23 - 27:25
    these studies tie together
    to inform the current study.
  • 27:25 - 27:28
    This would be a lot better.
  • 27:28 - 27:31
    This is just like
    me spit-balling.
  • 27:31 - 27:35
    Also, you should know,
    writing is hard.
  • 27:36 - 27:38
    This is not something
    I would submit,
  • 27:38 - 27:40
    but this would be
    something that I'm like,
  • 27:40 - 27:42
    Okay, that's a good start,
  • 27:42 - 27:44
    and then I would
    go back and edit.
  • 27:44 - 27:46
    I wouldn't anticipate writing
  • 27:46 - 27:48
    this paper the day before
    and submitting it.
  • 27:48 - 27:51
    I would maybe set a timer
    for 30 minutes and say,
  • 27:51 - 27:53
    All right, I'm just going
    to outline this paper.
  • 27:53 - 27:55
    No matter how crap it is,
  • 27:55 - 27:57
    it's still better than nothing,
  • 27:57 - 28:00
    and you can always work
    with it to make it better.
  • 28:00 - 28:03
    Okay, so that's the
    intro paragraph.
  • 28:03 - 28:06
    Then I want you to
    support your argument.
  • 28:06 - 28:09
    Write the support for
    your argument statement
  • 28:09 - 28:11
    as the beginning
    of the paragraph.
  • 28:11 - 28:13
    Based on the literature
    review you did,
  • 28:13 - 28:15
    what articles are finding,
    support your argument?
  • 28:15 - 28:18
    Write a few sentences based
    on these articles here,
  • 28:18 - 28:20
    reiterate your point, and create
  • 28:20 - 28:22
    a bridge between this
    paragraph and the next.
  • 28:22 - 28:25
    Repeat until your argument
    is clearly fleshed out.
  • 28:25 - 28:27
    Not to put you on
    the spot, Hailey,
  • 28:27 - 28:29
    but I am putting you on spot.
  • 28:30 - 28:34
    Give me some titles of the
    papers that you've read.
  • 28:34 - 28:40
    Okay. One is a
    confirmatory study
  • 28:40 - 28:42
    of the relations between
    workplace sexism,
  • 28:42 - 28:44
    sense of belonging,
    mental health,
  • 28:44 - 28:45
    and job satisfaction among
  • 28:45 - 28:48
    women in male-dominated
    industries.
  • 28:52 - 28:53
    Give me a sec.
  • 28:53 - 28:56
    Workplace harassment,
    belonging, and what else?
  • 28:58 - 29:02
    Mental health and
    job satisfaction.
  • 29:03 - 29:06
    Okay, and what's another one?
  • 29:07 - 29:11
    Hold on, women and male.
  • 29:13 - 29:17
    Another one is harmful
    workplace experiences
  • 29:17 - 29:22
    and women's occupational
    well-being, a meta-analysis.
  • 29:23 - 29:26
    You know what a
    meta-analysis is?
  • 29:26 - 29:29
    Well, from what I've read,
  • 29:29 - 29:32
    isn't it just like the author
  • 29:32 - 29:34
    of that article is
  • 29:34 - 29:37
    taking findings from other
    studies and kind of,
  • 29:37 - 29:38
    like, compiling it into one?
  • 29:38 - 29:41
    It's not his personal research.
  • 29:41 - 29:44
    It's him comparing a
    bunch of other research.
  • 29:44 - 29:46
    Yeah, which might be helpful
  • 29:46 - 29:47
    because you'll be able to read
  • 29:47 - 29:51
    like 20 research articles
    in that one article.
  • 29:51 - 29:54
    Yes, that's good. Any other?
  • 29:54 - 29:58
    Yeah, women and women of
    color and leadership,
  • 29:58 - 30:02
    complexity, identity,
    and intersectionality.
  • 30:03 - 30:07
    Okay, so let's just
    go with these three.
  • 30:11 - 30:14
    You could say something one of
  • 30:14 - 30:18
    your paragraphs following
    the intro could be
  • 30:28 - 30:32
    sexual harassment may lead to
  • 30:32 - 30:36
    decreased levels of belonging,
  • 30:36 - 30:41
    job satisfaction, and
    occupational well-being.
  • 30:41 - 30:45
    Then I talk about
    these two articles.
  • 30:45 - 30:49
    This could be two paragraphs.
  • 30:49 - 30:52
    These two articles I
    would talk about here.
  • 30:52 - 30:55
    Maybe what I would
    do is I would make
  • 30:55 - 30:58
    that a subheading and
    make that a subheading.
  • 30:58 - 31:02
    So that will bolster
    your argument there.
  • 31:09 - 31:14
    This may be particularly true
  • 31:14 - 31:19
    for women in
    male-dominated fields.
  • 31:19 - 31:23
    Maybe that could be
    the second paragraph.
  • 31:23 - 31:34
    Wow.
  • 31:34 - 31:39
    Does that make sense? Yeah.
  • 31:39 - 31:40
    Yeah.
  • 31:40 - 31:45
    Okay. The first sentence
  • 31:45 - 31:47
    of the second paragraph
  • 31:47 - 31:49
    might be something more
    specific than that,
  • 31:49 - 31:51
    but that could be
  • 31:51 - 31:54
    your first sentence for
    the first paragraph,
  • 31:54 - 31:56
    something like this for
    the second paragraph.
  • 31:56 - 31:58
    These are the articles
    that you will
  • 31:58 - 32:00
    use to support those statements
  • 32:00 - 32:04
    , and so on and so forth.
  • 32:04 - 32:09
    Then the third paragraph
    could be something like women
  • 32:09 - 32:15
    with multiple stigmatized
    identities may experience
  • 32:15 - 32:25
    the negative effects of
    workplace harassment.
  • 32:34 - 32:41
    Seeing multiple may compound
  • 32:41 - 32:45
    the negative effects of
    workplace harassment
  • 32:45 - 32:48
    for women or something.
  • 32:48 - 32:50
    Then you'll talk about this:
  • 32:50 - 32:52
    women and women of
    color in leadership.
  • 32:52 - 32:55
    Then your fourth paragraph might
  • 32:55 - 32:58
    be something about allies
  • 32:58 - 33:05
    and preventing future
    sexual harassment.
  • 33:05 - 33:08
    Then your final
    paragraph, there you go.
  • 33:08 - 33:11
    I outlined your paper, Hailey.
    It doesn't have to be that.
  • 33:11 - 33:12
    You can rearrange it,
  • 33:12 - 33:15
    but does that make sense Alicia?
  • 33:15 - 33:17
    I can do this for
    anyone else's paper.
  • 33:17 - 33:19
    I'm just picking on Hailey.
  • 33:19 - 33:23
    If you want me to help, I can
    do this for your paper too.
  • 33:23 - 33:25
    Then your final paragraph,
  • 33:25 - 33:26
    you'll summarize the arguments,
  • 33:26 - 33:29
    points listed in
    Paragraphs 2 and 5.
  • 33:32 - 33:36
    As stated earlier,
    sexual harassment
  • 33:36 - 33:42
    has negative
    implications for women.
  • 33:42 - 33:49
    Women who experience sexual
    harassment experience
  • 33:49 - 34:00
    lower levels of belonging
    and job satisfaction;
  • 34:00 - 34:05
    citation, lower levels of
  • 34:05 - 34:10
    occupational
    well-being; citation.
  • 34:10 - 34:14
    Further, those who possess
  • 34:14 - 34:20
    multiple stigmatized identities,
  • 34:20 - 34:22
    so on and so forth.
  • 34:22 - 34:25
    Then allies may help
  • 34:25 - 34:32
    prevent future sexual harassment
    and so on and so forth.
  • 34:32 - 34:42
    However, limited work has
    examined how sexual harassment
  • 34:42 - 34:48
    can affect not only
    job satisfaction,
  • 34:48 - 34:52
    but also persistence.
  • 34:52 - 34:55
    In the current study,
  • 34:55 - 34:59
    I will examine how
    sexual harassment
  • 34:59 - 35:10
    affects persistence among
    college-aged women,
  • 35:10 - 35:18
    women of different ethnicities,
    whatever you want.
  • 35:18 - 35:21
    You can talk about your sample.
  • 35:23 - 35:31
    I will manipulate sexual
    harassment by blah, blah, blah.
  • 35:31 - 35:37
    I will measure persistence
  • 35:37 - 35:40
    by using the everyday workplace.
  • 35:40 - 35:44
    This doesn't exist by
    the way. I don't know.
  • 35:44 - 35:50
    Persistent scale;
    Chatterley and Tingle,
  • 35:50 - 35:56
    2020 which includes items like,
  • 35:56 - 36:01
    "I want to burn my
    office down," on
  • 36:01 - 36:05
    a scale from one
    strongly disagree
  • 36:05 - 36:09
    to seven, strongly agree.
  • 36:09 - 36:13
    Just to give me a sense of
    what the scale measures.
  • 36:17 - 36:29
    "I often daydream about
    burning my office down."
  • 36:30 - 36:34
    Then that's the end.
    That's your intro.
  • 36:34 - 36:35
    There it is.
  • 36:35 - 36:37
    Well, does that make sense?
  • 36:37 - 36:42
    Alicia? Does that make sense?
  • 36:46 - 36:50
    I'm looking at it, but I just
    want to say it out loud.
  • 36:50 - 36:52
    On the final sentence,
  • 36:52 - 36:56
    you're going to bring in
  • 36:56 - 36:59
    your references
    and say what they
  • 36:59 - 37:03
    offered like a summary thing.
  • 37:03 - 37:05
    Mm-hmm. Then finally,
    you're going to say,
  • 37:05 - 37:09
    "I predict that women who are
  • 37:09 - 37:14
    in Condition 1 will want to burn
  • 37:14 - 37:18
    their office down more
    than those who are in
  • 37:18 - 37:20
    Condition 2," even though that's
  • 37:20 - 37:23
    not what you were
    originally measuring.
  • 37:23 - 37:25
    I messed up. That
    should be persistence.
  • 37:25 - 37:27
    I'm not burning
    your office down.
  • 37:27 - 37:29
    But you get what I'm saying.
  • 37:29 - 37:32
    The last part, this is
    the reaction I am going.
  • 37:32 - 37:34
    Here's the questions
    that haven't
  • 37:34 - 37:37
    been asked or answered
    or something.
  • 37:37 - 37:40
    Yeah. Hailey, I
    can send you this.
  • 37:40 - 37:43
    I'm not sure how
    helpful it'll be.
  • 37:43 - 37:45
    But anyone else who
    comes to these meetings,
  • 37:45 - 37:48
    I'm more than happy
    to use your study.
  • 37:48 - 37:55
    Perfect. Does that make it
    less intimidating I guess?
  • 37:57 - 38:00
    I created this
    just for your sake
  • 38:00 - 38:03
    and also for my sake because
    I want better papers.
  • 38:03 - 38:06
    Then your support
    for your argument.
  • 38:06 - 38:09
    This will be Paragraphs 2-5
  • 38:09 - 38:11
    and then your final paragraph;
  • 38:11 - 38:14
    Paragraph 6, the end. That's it.
  • 38:14 - 38:17
    Also, remember I do offer
    extra credit for visiting
  • 38:17 - 38:19
    the writing center
    and you can get
  • 38:19 - 38:22
    extra credit for every time
    that you submit a paper.
  • 38:22 - 38:23
    For the intro; for
    the method and
  • 38:23 - 38:25
    results and for the discussion,
  • 38:25 - 38:27
    so schedule an appointment
    today if you want to get
  • 38:27 - 38:29
    that extra credit and
  • 38:29 - 38:32
    they'll also help you
    write your paper.
  • 38:33 - 38:37
    On that should we state,
  • 38:37 - 38:40
    is it the timing of
    when our thing is
  • 38:40 - 38:43
    before the paper
    or should we state
  • 38:43 - 38:45
    in our thing with
  • 38:45 - 38:49
    the writing center what we
    want to go over them about?
  • 38:49 - 38:51
    Do you mean, should you schedule
  • 38:51 - 38:53
    your appointment before
    the assignments due?
  • 38:53 - 38:57
    No, just to get credit for
    what we're going over.
  • 38:57 - 39:01
    It's just the timing of
    the the tutor assigned.
  • 39:01 - 39:03
    Yeah, you can go over anything.
  • 39:03 - 39:05
    You can go over APA formatting,
  • 39:05 - 39:09
    you can go over making
    your logical argument.
  • 39:09 - 39:10
    You can go over
    anything you want.
  • 39:10 - 39:12
    I just want you to visit
    the writing center
  • 39:12 - 39:17
    and make use of the
    resources we have available.
  • 39:18 - 39:23
    I wasn't able to get in touch
    with Emily for some reason.
  • 39:23 - 39:26
    Was that who you were
    about to say we also have?
  • 39:26 - 39:30
    Yes, Emily. I'll reach out
    to her. Did you email her?
  • 39:32 - 39:34
    Do you have that information?
  • 39:34 - 39:36
    I will get that information
  • 39:36 - 39:38
    and send it out to
    everyone because that's
  • 39:38 - 39:40
    a tutor that we have
    that's separate from
  • 39:40 - 39:42
    the writing center and
    she's experienced in 301,
  • 39:42 - 39:44
    so she can help with that.
  • 39:44 - 39:46
    I need to talk with her;
  • 39:46 - 39:50
    it's getting deep
    on the stuff here,
  • 39:50 - 39:52
    so just to go over stuff
  • 39:52 - 39:54
    and I feel more
    comfortable in it.
  • 39:54 - 39:57
    A few things. Appreciate
    that. Thank you.
  • 39:57 - 39:58
    Of course, yeah. I'll
    send it out to the group.
  • 39:58 - 40:03
    I wrote it down. Alicia,
    how are you feeling?
  • 40:05 - 40:08
    I'm feeling okay. I've been
    having a little bit of
  • 40:08 - 40:11
    trouble with finding studies.
  • 40:11 - 40:12
    I think it's because I'm not
  • 40:12 - 40:15
    sure specific keywords to
  • 40:15 - 40:18
    use or what to
    actually look for.
  • 40:18 - 40:21
    Has anyone else been
    having trouble with that?
  • 40:21 - 40:25
    Hailey, it seems like you've
    got it all down. Rick?
  • 40:25 - 40:31
    My keywords, I think I had
    them right off the bat.
  • 40:31 - 40:37
    Well, depression due
    to COVID isolation.
  • 40:37 - 40:42
    It got me some real
    pinpoint stuff.
  • 40:42 - 40:48
    They're using different
    methods so I can cross.
  • 40:48 - 40:52
    It's nice. There's
    one self survey and
  • 40:52 - 40:57
    then there's statistical
    and physiological also.
  • 40:57 - 40:58
    It's all great.
  • 40:58 - 41:01
    Alicia, you scheduled an
    appointment with me. Right?
  • 41:01 - 41:03
    I have not.
  • 41:03 - 41:08
    Okay. Do you have
    time after class?
  • 41:08 - 41:11
    We might not go the full
    hour so I can help you.
  • 41:11 - 41:12
    Yeah.
  • 41:12 - 41:14
    Okay. Anyone else?
    If you want to help,
  • 41:14 - 41:15
    schedule an appointment
    or stay after class,
  • 41:15 - 41:18
    and I will go over those things.
  • 41:18 - 41:19
    I do want to finish the lecture
  • 41:19 - 41:22
    today. Everyone
    take a deep breath.
  • 41:22 - 41:24
    The intro will be fine.
  • 41:24 - 41:26
    I need to take a deep breath
    too because I feel like
  • 41:26 - 41:29
    I talk so much that
    I forget to breathe.
  • 41:31 - 41:34
    The intro will be fine.
  • 41:34 - 41:35
    I'm more than happy to meet with
  • 41:35 - 41:36
    you during my office hours.
  • 41:36 - 41:38
    I still have office
    hours this week.
  • 41:38 - 41:41
    I have advising hours
    that no one is coming to.
  • 41:41 - 41:43
    You can come to those
    advising hours too.
  • 41:43 - 41:46
    I can make time
    to meet with you.
  • 41:46 - 41:47
    I'm more than happy to help you.
  • 41:47 - 41:50
    I love research especially
    when it's not mine.
  • 41:50 - 41:52
    When it's my research,
    I'm just like,
  • 41:52 - 41:55
    "I don't want to do it,' but
    I love helping other people.
  • 41:55 - 42:00
    Let me know. Alright, so
    let me get my PowerPoint.
  • 42:00 - 42:04
    We're almost done
    with the PowerPoint.
  • 42:07 - 42:10
    Where were we?
  • 42:14 - 42:17
    Share screen.
  • 42:20 - 42:24
    Yes. I believe yes,
    that is what I want.
  • 42:24 - 42:26
    Because I just got
    my external monitor
  • 42:26 - 42:29
    back. Thank goodness.
  • 42:30 - 42:34
    We're going to continue
    with Chapter five.
  • 42:34 - 42:36
    If you have a question,
  • 42:36 - 42:37
    just holla because I can't see
  • 42:37 - 42:40
    your faces now for some reason.
  • 42:40 - 42:43
    Identifying good measurement.
  • 42:43 - 42:45
    I do want to see
    your faces, though.
  • 42:45 - 42:48
    What do I do? I'm
    going to put here.
  • 42:50 - 42:54
    I can't see. Oh, man. I
    can't see your faces.
  • 42:54 - 42:56
    Just speak out if you know
  • 42:56 - 42:59
    the answers to the
    questions I'm going to ask.
  • 42:59 - 43:02
    Can you see the screen
  • 43:02 - 43:07
    without the notes or
    can you see the notes?
  • 43:08 - 43:10
    I just see you.
  • 43:10 - 43:13
    Yeah, right now, it's basics.
  • 43:13 - 43:14
    I see the school.
  • 43:14 - 43:27
    Oh, my bad. Oh, man. Let's see.
  • 43:28 - 43:30
    There they are.
  • 43:30 - 43:34
    Now.
  • 43:34 - 43:35
    Perfect.
  • 43:35 - 43:37
    Now I can see you.
  • 43:37 - 43:41
    We talked about three
    types of reliability.
  • 43:41 - 43:43
    What are they?
  • 43:43 - 43:44
    I didn't ask
    questions last time,
  • 43:44 - 43:47
    but I'll do it this time.
  • 43:48 - 43:50
    The three types.
  • 43:50 - 43:58
    Alicia. Anyone else?
  • 43:59 - 44:03
    I don't know where
    I'm at. Oh, my God.
  • 44:05 - 44:07
    Yes, Hailey.
  • 44:08 - 44:12
    Test reliability, inter-rater
  • 44:12 - 44:15
    reliability, and
    internal reliability.
  • 44:15 - 44:17
    Perfect. I won't make you
    answer what they are.
  • 44:17 - 44:20
    But Rick or Alicia
    or anyone else,
  • 44:20 - 44:23
    do you remember what
    those mean? Test, retest.
  • 44:25 - 44:34
    When you test or do, I
    want to call it a study.
  • 44:34 - 44:35
    I don't know if that's the
    right word, but basically,
  • 44:35 - 44:37
    do it at one point.
  • 44:37 - 44:40
    We're looking at test-retest
    reliability for IQ.
  • 44:40 - 44:46
    For IQ? Say, today
    you test someone's IQ
  • 44:46 - 44:48
    and they get a specific answer,
  • 44:48 - 44:51
    and then say next
    week you test them
  • 44:51 - 44:56
    again and compare the results.
  • 44:56 - 44:59
    Good test for test
    reliability would tell us
  • 44:59 - 45:02
    what about the IQ scores?
  • 45:02 - 45:08
    That it's an accurate
    representation of
  • 45:08 - 45:14
    their IQ and it's
    reliable data to use.
  • 45:14 - 45:18
    Close. Rick or Hailey?
  • 45:20 - 45:23
    I think Meghan
    raised their hand.
  • 45:23 - 45:26
    Oh, Meghan, go for it.
    I didn't see your hand.
  • 45:27 - 45:30
    It would be consistent,
  • 45:30 - 45:34
    so it would be around
    the same quantitatively?
  • 45:34 - 45:38
    Yes, perfect. Let's
    say at Time 1,
  • 45:38 - 45:41
    you get an IQ score
    of 125 and Time 2,
  • 45:41 - 45:45
    you get an IQ score of 128,
    that's relatively consistent.
  • 45:45 - 45:47
    I think that's what you meant
  • 45:47 - 45:49
    Alicia when you said accurate,
  • 45:49 - 45:51
    but it's about consistency,
  • 45:51 - 45:53
    so having almost the same score.
  • 45:53 - 45:55
    I would have low
    test-retest reliability if
  • 45:55 - 45:58
    one day I score a
    40 on an IQ test,
  • 45:58 - 46:00
    and then the next
    time I score 140,
  • 46:00 - 46:03
    so that doesn't have good
    test-retest reliability.
  • 46:03 - 46:06
    Perfect. We talked about
    inter-rater reliability.
  • 46:06 - 46:08
    Does anyone want to
    take a stab at that?
  • 46:08 - 46:17
    Maybe Meghan? Or Rick.
  • 46:17 - 46:20
    Well, do the same test,
  • 46:20 - 46:22
    but it's not you doing it again.
  • 46:22 - 46:24
    It's two different people
  • 46:24 - 46:27
    doing the same test on the same.
  • 46:31 - 46:37
    That's what it is. You're
    looking for consistent scores,
  • 46:37 - 46:40
    but using two different
    people doing this film.
  • 46:40 - 46:43
    Yes, can you give us an example?
  • 46:49 - 46:52
    I came up with really
    silly examples,
  • 46:52 - 46:54
    so just go for it.
  • 46:56 - 47:01
    I test to see if,
  • 47:01 - 47:05
    I run around the block
    and I get sweaty,
  • 47:05 - 47:07
    and then someone else
  • 47:07 - 47:09
    test to see if running
    around the block.
  • 47:09 - 47:13
    No, that's wrong. I don't
    know. I can't think.
  • 47:13 - 47:15
    No, that's okay. I think
  • 47:15 - 47:18
    the key here is that
    there are two observers.
  • 47:18 - 47:20
    There's two observers.
  • 47:20 - 47:21
    Yes.
  • 47:21 - 47:24
    But for the same testing.
  • 47:24 - 47:29
    Yes. In your example,
  • 47:29 - 47:33
    Alicia would be my
    research assistant,
  • 47:33 - 47:35
    and she would be coding for how
  • 47:35 - 47:38
    sweaty you get
    versus someone else.
  • 47:38 - 47:40
    I would also have Hailey.
  • 47:41 - 47:45
    Inter-rater reliability
    means that Alicia
  • 47:45 - 47:49
    and Hailey score you similarly
    on how sweaty you get,
  • 47:49 - 47:52
    and Hailey and Alicia score
  • 47:52 - 47:55
    the other person similarly
    in how sweaty they get.
  • 47:55 - 47:58
    It's two observers looking at
  • 47:58 - 48:02
    behavior in someone else.
    Does that make sense?
  • 48:02 - 48:04
    Yeah, thank you.
  • 48:04 - 48:06
    Yeah, I think you
    were almost there.
  • 48:06 - 48:09
    You just had the
    two people down,
  • 48:09 - 48:12
    but not sure where it
    went, but that's good.
  • 48:12 - 48:16
    That's still good. What
    about internal reliability?
  • 48:18 - 48:22
    Isn't this where if R
    is positive and strong,
  • 48:22 - 48:24
    you have a good reliability?
  • 48:24 - 48:28
    Yes, but what does that mean?
  • 48:31 - 48:34
    I'm not quite positive.
  • 48:34 - 48:35
    Who was that?
  • 48:35 - 48:36
    Grace.
  • 48:36 - 48:39
    Grace. Yeah. Good
    job, Grace. Hailey.
  • 48:39 - 48:45
    Isn't it where, when you ask
    questions to the people,
  • 48:45 - 48:48
    so it's like two
    different questions,
  • 48:48 - 48:51
    but they both are trying
    to get at the same answer.
  • 48:51 - 48:52
    They're just worded differently,
  • 48:52 - 48:55
    and the people answer
    them consistently.
  • 48:55 - 48:58
    Yes. Rick.
  • 48:58 - 49:01
    Same question, word
    it differently.
  • 49:01 - 49:07
    Yes. In Hailey's
    questionnaire that I created,
  • 49:07 - 49:09
    it would be if you ask people,
  • 49:09 - 49:11
    how likely are you or how
  • 49:11 - 49:13
    often do you daydream about
    burning your office down?
  • 49:13 - 49:16
    Then also, if your
    office was burning down,
  • 49:16 - 49:21
    would you call the
    fire department?
  • 49:21 - 49:23
    Those are questions that
    are framed differently,
  • 49:23 - 49:26
    but also measuring the same,
  • 49:26 - 49:27
    like, how much do you
  • 49:27 - 49:30
    appreciate or want
    to work at your job?
  • 49:30 - 49:31
    Oh, okay.
  • 49:31 - 49:34
    Yeah. They would
    answer consistently.
  • 49:34 - 49:36
    Good. I'm going to keep
  • 49:36 - 49:38
    asking these questions
    just to make sure.
  • 49:38 - 49:42
    It's okay if you don't
    get the entire measure
  • 49:42 - 49:46
    or the type of
    reliability, 100% there.
  • 49:46 - 49:52
    As you can tell, most of you
    were 80% there. It's okay.
  • 49:52 - 49:54
    And during the exam,
    it will be open book,
  • 49:54 - 49:59
    open note at the
    end of the term,
  • 49:59 - 50:01
    which is also, I guess,
    the end of the year.
  • 50:01 - 50:07
    Still good job. We
    talked about this.
  • 50:07 - 50:12
    We looked at the measurement
    of head circumference,
  • 50:12 - 50:15
    at Time 1 and Time 2.
  • 50:16 - 50:20
    Which reliability would this be?
  • 50:20 - 50:23
    You measure the head at Time 1,
  • 50:23 - 50:24
    you measure the head at Time 2,
  • 50:24 - 50:29
    and the measures are pretty
    related to one another.
  • 50:29 - 50:32
    Which of the reliabilities
    would this be?
  • 50:32 - 50:33
    Test-retest.
  • 50:33 - 50:39
    Yeah. Perfect. This is
    inter-rater reliability.
  • 50:40 - 50:42
    Does anyone want to
    explain the difference
  • 50:42 - 50:44
    between the left and
    the right to me?
  • 50:48 - 50:54
    The left one is showing
    that both observers,
  • 50:54 - 50:59
    their ratings are very
    close to one another,
  • 50:59 - 51:02
    whereas the right
    side is showing
  • 51:02 - 51:04
    they're all over the place and
  • 51:04 - 51:07
    not quite matching
    up with one another.
  • 51:07 - 51:09
    Perfect. You would
    say that this one has
  • 51:09 - 51:11
    high interrater reliability and
  • 51:11 - 51:14
    this one has low
    interrater reliability
  • 51:14 - 51:16
    because we have two observers,
  • 51:16 - 51:18
    so Mark's ratings
    and Matt's ratings,
  • 51:18 - 51:21
    and they both seem to
    track on the same line.
  • 51:21 - 51:26
    But if we have Mark's
    ratings and Peter's ratings,
  • 51:26 - 51:27
    they don't seem to track on
  • 51:27 - 51:30
    the same line;
    they're scattered.
  • 51:30 - 51:33
    This would mean that this has
    low interrater reliability,
  • 51:33 - 51:36
    so I might not trust
    Peter's ratings.
  • 51:36 - 51:39
    Maybe he's using a
    different assessment
  • 51:39 - 51:41
    for sweatiness if we're testing
  • 51:41 - 51:43
    Rick and other people's
    sweatiness when they run,
  • 51:43 - 51:44
    but Mark and Matt seem to
  • 51:44 - 51:48
    be on point with what
    they're measuring.
  • 51:49 - 51:51
    I believe this is where we were,
  • 51:51 - 51:53
    and this is what Grace
    was talking about.
  • 51:53 - 51:55
    Using the correlation
    coefficient
  • 51:55 - 51:59
    r to evaluate reliability.
  • 51:59 - 52:03
    The correlation
    coefficient finds
  • 52:03 - 52:07
    the relationship between
    two different variables.
  • 52:08 - 52:11
    Someone give me two
    variables that could be
  • 52:11 - 52:14
    related or maybe not related.
  • 52:14 - 52:16
    Foot size and height.
  • 52:16 - 52:19
    Foot size and height.
    We have foot size
  • 52:19 - 52:22
    being X and height being Y.
  • 52:22 - 52:24
    We could use all
    of these different
  • 52:24 - 52:26
    scatter plots to represent that.
  • 52:26 - 52:30
    Another thing to note for
    the correlation coefficient,
  • 52:30 - 52:33
    the values go from
    negative one to one,
  • 52:33 - 52:35
    so it includes zero.
  • 52:36 - 52:39
    The sign in front of the number,
  • 52:39 - 52:41
    whether it's negative
    or positive,
  • 52:41 - 52:44
    tells you the slope direction.
  • 52:44 - 52:47
    If the slope seems to be
    increasing like this one,
  • 52:47 - 52:49
    all the data seem
    to be increasing,
  • 52:49 - 52:52
    as X increases, Y increases;
  • 52:52 - 52:55
    you would say that this
    is a positive slope.
  • 52:55 - 53:02
    As X increases as shoe size
    increases, height increases.
  • 53:02 - 53:05
    This would also be
    a positive slope.
  • 53:05 - 53:07
    This one, on the other hand,
  • 53:07 - 53:09
    would be a negative
    slope because as
  • 53:09 - 53:12
    shoe size increases,
    height decreases.
  • 53:12 - 53:14
    If you're a size, let's
    just say this is 11,
  • 53:14 - 53:19
    then you are five foot or
    four foot two or something.
  • 53:19 - 53:23
    as shoe size increases,
    height decreases.
  • 53:23 - 53:26
    But this one doesn't really
    have a slope because there's
  • 53:26 - 53:29
    no way to predict as X
    increases what Y would be,
  • 53:29 - 53:31
    since it's all scattered around.
  • 53:31 - 53:34
    You can see what the
    slope direction is or
  • 53:34 - 53:36
    whether there's a positive
    value or negative value
  • 53:36 - 53:41
    by looking at the slope of
    the pattern of the dots.
  • 53:41 - 53:45
    Does that make
    sense? Then you can
  • 53:45 - 53:50
    tell the strength by how
    closely together the dots are.
  • 53:50 - 53:55
    I would imagine a line going
    straight diagonally here,
  • 53:55 - 53:59
    and how well do the dots
    match up to that line?
  • 53:59 - 54:02
    They don't match up
    super well here.
  • 54:02 - 54:04
    This is a 0.56 relationship.
  • 54:04 - 54:08
    Again, from negative
    one to one, this one,
  • 54:08 - 54:10
    if you had a line,
  • 54:10 - 54:13
    they all seem to
    track really well.
  • 54:13 - 54:17
    This is an r of 0.93.
  • 54:17 - 54:19
    You can tell that it's
    really strong by how well
  • 54:19 - 54:22
    the dots adhere to one another.
  • 54:22 - 54:26
    This one is -0.59,
  • 54:26 - 54:29
    so it's a little better
    than this one or a
  • 54:29 - 54:33
    little stronger
    than this example.
  • 54:33 - 54:36
    This r of 0.01,
  • 54:36 - 54:40
    there's no strength, it's
    not really predictive.
  • 54:42 - 54:44
    Perfect. Let me see
  • 54:44 - 54:47
    my notes if I need to
    tell you anything else.
  • 54:51 - 54:54
    Any questions here?
  • 54:54 - 55:02
    Which is the strongest r
    in these scatter plots?
  • 55:02 - 55:04
    R 0.93.
  • 55:04 - 55:06
    Perfect. If it was a -0.93,
  • 55:06 - 55:08
    would it still be the strongest?
  • 55:08 - 55:12
    Or actually, negative 0.94.
  • 55:13 - 55:15
    I think so because
  • 55:15 - 55:18
    negative correlations
    can also be strong.
  • 55:18 - 55:19
    It just depends on
    what you're measuring.
  • 55:19 - 55:21
    Perfect. It doesn't really
  • 55:21 - 55:23
    matter whether it has a
    positive or negative.
  • 55:23 - 55:27
    It's more about the absolute
    value of the r. Of these,
  • 55:27 - 55:29
    the r 0.93 is the strongest,
  • 55:29 - 55:31
    and which one is the weakest,
  • 55:31 - 55:34
    Rick or Elysia, or Grace?
  • 55:36 - 55:38
    I can't see the numbers,
  • 55:38 - 55:39
    but the scattering ones.
  • 55:39 - 55:40
    This one.
  • 55:40 - 55:42
    That one. 0.01, yeah.
  • 55:42 - 55:46
    Perfect. How does
    the correlation
  • 55:46 - 55:48
    coefficient evaluate reliability
  • 55:48 - 55:51
    for the test-retest reliability?
  • 55:51 - 55:55
    You would correlate
    their first IQ score to
  • 55:55 - 55:59
    their second IQ score and
    see how strong it is.
  • 55:59 - 56:00
    If your first IQ score was
  • 56:00 - 56:03
    125 and your second
    IQ score was 126,
  • 56:03 - 56:05
    they would correlate
    pretty highly.
  • 56:05 - 56:08
    Your r would be very positive.
  • 56:09 - 56:12
    If r is strong and positive,
  • 56:12 - 56:17
    at least 0.05 or above,
    you have good test.
  • 56:17 - 56:19
    I think it should
    be 0.50 or above.
  • 56:19 - 56:21
    Then you have good
    test-retest reliability.
  • 56:21 - 56:23
    If r is positive at weak,
  • 56:23 - 56:27
    this is a sign of low
    test-retest reliability.
  • 56:27 - 56:29
    This reliability is
    only relevant for
  • 56:29 - 56:31
    variables that don't
    change over time.
  • 56:31 - 56:35
    It wouldn't be relevant
    for something like weight.
  • 56:35 - 56:39
    Weight tends to fluctuate
    more than shoe size, maybe.
  • 56:39 - 56:42
    What about for
    interrater reliability?
  • 56:42 - 56:44
    Two observers rate the same
  • 56:44 - 56:46
    participants at the same time,
  • 56:46 - 56:47
    and then r is computed.
  • 56:47 - 56:51
    You want to see how Hailey and
  • 56:51 - 56:55
    Elysia's evaluation of
    sweating after running,
  • 56:55 - 56:57
    how related they
    are to one another.
  • 56:57 - 57:00
    Here, if r is
    positive and strong,
  • 57:00 - 57:03
    the value has to
    be 0.7 or higher;
  • 57:03 - 57:05
    you have good
    interrater reliability.
  • 57:05 - 57:08
    Here, you want it to be
    positive, not negative.
  • 57:08 - 57:10
    If r is positive but weak,
  • 57:10 - 57:13
    you don't have good
    interrater reliability.
  • 57:13 - 57:17
    Negative r in this situation
    would be meaningful,
  • 57:17 - 57:20
    and it would mean terrible
    interrater reliability.
  • 57:20 - 57:23
    If you had a -0.7,
  • 57:23 - 57:26
    that would mean that when
    Hailey rates the person
  • 57:26 - 57:30
    as 5 on sweatiness,
  • 57:30 - 57:33
    Elysia would rate them
    as -5 on sweatiness.
  • 57:33 - 57:35
    As Hailey's scores go up,
  • 57:35 - 57:37
    Elysia's scores go down,
  • 57:37 - 57:39
    or the other way around.
  • 57:42 - 57:46
    Internal reliability, this is
  • 57:46 - 57:48
    when you have more than one item
  • 57:48 - 57:49
    to tap into the construct.
  • 57:49 - 57:52
    This is like the example
    that I gave with
  • 57:52 - 57:54
    Hailey's burning
    the building down.
  • 57:54 - 57:57
    I would correlate the two values
  • 57:57 - 58:00
    to see how related they
    are to one another.
  • 58:00 - 58:01
    Another important
    thing to note is,
  • 58:01 - 58:06
    I asked one question
    where positive means more
  • 58:06 - 58:11
    likely to burn the
    employment place down.
  • 58:11 - 58:12
    The other question, I said,
  • 58:12 - 58:13
    how likely are you to
  • 58:13 - 58:15
    prevent the burning
    of the place down?
  • 58:15 - 58:18
    I need to reverse
    code that item,
  • 58:18 - 58:19
    and we'll talk about
    what that means,
  • 58:19 - 58:21
    and then correlate the items to
  • 58:21 - 58:23
    see how well that taps
    into the construct
  • 58:23 - 58:29
    of workplace satisfaction.
    Does that make sense?
  • 58:29 - 58:34
    I must hear more about
    reverse code. I like that.
  • 58:34 - 58:39
    We'll talk about that, and
    I'll show you how to do that,
  • 58:39 - 58:42
    but maybe not in this lecture.
  • 58:42 - 58:43
    Essentially, what
    you want to do with
  • 58:43 - 58:45
    reverse coding is you want to
  • 58:45 - 58:48
    make sure that all of your
    items mean the same thing.
  • 58:48 - 58:50
    I want to tap into
    the construct of
  • 58:50 - 58:54
    workplace satisfaction
    by assessing
  • 58:54 - 58:57
    desire to burn your
    workplace down.
  • 58:57 - 59:00
    The question, how
    likely are you to
  • 59:00 - 59:02
    call the fire department
  • 59:02 - 59:04
    if your workplace
    was burning down,
  • 59:04 - 59:09
    is not necessarily measuring
    your workplace satisfaction,
  • 59:09 - 59:13
    but it might be
    measuring how likely you
  • 59:13 - 59:17
    are to not want it to burn down.
  • 59:17 - 59:18
    But if I want to measure
  • 59:18 - 59:20
    how much I want the
    building to burn down,
  • 59:20 - 59:22
    then I need to
    reverse code that.
  • 59:22 - 59:26
    If you said, strongly disagree,
  • 59:26 - 59:28
    I would not want to
    call a fire department;
  • 59:28 - 59:31
    that would mean now
    with reverse coding,
  • 59:31 - 59:35
    it would be strongly agree.
  • 59:36 - 59:39
    I would word the
    question instead
  • 59:39 - 59:43
    as I would not be likely to
    call the fire department,
  • 59:43 - 59:45
    and then strongly agree.
  • 59:45 - 59:47
    But it'll make more sense once
  • 59:47 - 59:49
    we get to that part
    of the chapter.
  • 59:49 - 59:51
    Does it make sense now?
  • 59:51 - 59:55
    Yeah.
  • 59:55 - 60:00
    That is almost word for word
    what I said. Yes, Hailey.
  • 60:00 - 60:03
    Just like what you were talking
    about the reverse coding,
  • 60:03 - 60:07
    I've taken personality
    tests for previous classes.
  • 60:07 - 60:13
    Is that why some questions
    are just the same words,
  • 60:13 - 60:17
    but worded backwards on the
    big five personality tests?
  • 60:17 - 60:19
    Exactly. That's why they're
  • 60:19 - 60:21
    worded backwards because
    we want to make sure
  • 60:21 - 60:24
    that you answer consistently
    irrespective of
  • 60:24 - 60:28
    how we word the questions
    because if I asked you,
  • 60:28 - 60:34
    on a scale of 1-7, how
    happy are you from one,
  • 60:34 - 60:38
    no emotions, to seven,
    extremely ecstatic.
  • 60:38 - 60:40
    I might also ask you,
  • 60:40 - 60:45
    how unhappy are you from one,
  • 60:45 - 60:46
    to not at all,
  • 60:46 - 60:49
    to seven, no emotions.
  • 60:49 - 60:50
    Then I'd reverse code one of
  • 60:50 - 60:53
    the items to actually
    assess happiness.
  • 60:54 - 60:58
    I've always seen those
    questions and I'm like,
  • 60:58 - 61:00
    didn't I just answer
    questions exactly like
  • 61:00 - 61:03
    this, but that makes sense.
  • 61:03 - 61:05
    Rick. Sorry.
  • 61:05 - 61:08
    Hey, [inaudible] exactly
    what you're saying.
  • 61:08 - 61:11
    That's exactly what I'm
    thinking when I'm reading this.
  • 61:11 - 61:13
    It just clarified something.
  • 61:13 - 61:15
    It was really good
    because all the
  • 61:15 - 61:17
    time in all these surveys,
  • 61:17 - 61:22
    I'm going, I've just read
    that question, so this helps.
  • 61:22 - 61:23
    That's the reason why we do it.
  • 61:23 - 61:26
    We want to make sure that
    there's reliability in
  • 61:26 - 61:28
    the scale because if you
  • 61:28 - 61:30
    answer the questions
    differently,
  • 61:30 - 61:33
    then that means that
    we're not appropriately
  • 61:33 - 61:35
    measuring the construct or we're
  • 61:35 - 61:38
    not measuring it as well
    as we thought we would.
  • 61:38 - 61:42
    I said those in words so
    you can have that later.
  • 61:42 - 61:44
    What about in journal articles?
  • 61:44 - 61:46
    Rick, you'll probably
    read about this.
  • 61:46 - 61:48
    All of you will probably
    read about this, but Rick,
  • 61:48 - 61:50
    you were talking
    about physiology,
  • 61:50 - 61:52
    and self-report, and
    things like that.
  • 61:52 - 61:57
    You'll see the reliability
    presented like this.
  • 61:57 - 61:59
    I'm trying to get
    my mouse over here.
  • 61:59 - 62:02
    Test-retest reliability,
    for instance, 0.83,
  • 62:02 - 62:06
    0.84, 0.6, 0.82, 0.5, 0.54.
  • 62:07 - 62:11
    Hailey, are you looking
    at a meta analysis?
  • 62:11 - 62:14
    You might find this too.
  • 62:14 - 62:16
    I have, I think,
  • 62:16 - 62:19
    two articles that
    are meta analysis.
  • 62:19 - 62:23
    You might find some values
    like this for test-retest.
  • 62:23 - 62:27
    This is telling you how
    long between the tests.
  • 62:27 - 62:29
    This is a satisfaction with
  • 62:29 - 62:31
    life scale, but
    they're different.
  • 62:31 - 62:33
    There's Alfonso and Allison,
  • 62:33 - 62:35
    Pavot et al, Blais et al,
  • 62:35 - 62:37
    Diener et al, and
    so on and so forth.
  • 62:37 - 62:40
    If you take the tests
    that Alfonso and Allison
  • 62:40 - 62:45
    gave at Time 1 and
    two weeks later,
  • 62:45 - 62:49
    the test-retest reliability
    is 0.83 with one month.
  • 62:49 - 62:52
    With a Pavot et al, 0.84.
  • 62:52 - 62:55
    Two months, you see
    it going down, 0.64.
  • 62:55 - 62:59
    The Diener et al
    test-retest reliability
  • 62:59 - 63:01
    is actually higher
    at two months.
  • 63:01 - 63:03
    At 10 weeks, of course,
  • 63:03 - 63:05
    it goes down to 0.5,
  • 63:05 - 63:07
    and in four years,
  • 63:07 - 63:09
    it goes down to 0.54.
  • 63:09 - 63:11
    That means that the
    scores that you have at
  • 63:11 - 63:13
    Time 1 aren't necessarily as
  • 63:13 - 63:18
    related at Time 2.
    Does that make sense?
  • 63:18 - 63:23
    The coefficient Alpha is for
    the internal reliability.
  • 63:23 - 63:27
    How good do all of the questions
  • 63:27 - 63:29
    that are in that scale measure
  • 63:29 - 63:31
    the construct that
    you're interested in?
  • 63:31 - 63:33
    Here you want a good value.
  • 63:33 - 63:38
    How well does, I'm very happy
  • 63:38 - 63:45
    and I feel really sad,
    reverse-coded measure happiness?
  • 63:45 - 63:48
    Here, the coefficient
    Alpha is 0.89.
  • 63:48 - 63:50
    That's good because
    it's close to one.
  • 63:50 - 63:54
    0.85, still good,
    0.79-0.84 still good.
  • 63:54 - 63:56
    All of these are
    greater than 0.70,
  • 63:56 - 63:58
    which means that all the items
  • 63:58 - 64:00
    tend to hang together nicely.
  • 64:00 - 64:03
    Does that make sense?
  • 64:04 - 64:07
    That's what you'll see in
  • 64:07 - 64:10
    the text or any
    articles that you read.
  • 64:12 - 64:16
    Now we're going to talk about
    validity of measurement.
  • 64:16 - 64:18
    We're going to talk about
    measurement validity
  • 64:18 - 64:20
    of abstract constructs.
  • 64:20 - 64:21
    We've talked about reliability.
  • 64:21 - 64:23
    Now we're moving on to validity.
  • 64:23 - 64:25
    What is the difference between
  • 64:25 - 64:27
    face validity and
    content validity?
  • 64:27 - 64:29
    Does it look like
    a good measure?
  • 64:29 - 64:31
    Criterion validity.
  • 64:31 - 64:33
    Does it correlate
    with key behaviors.
  • 64:33 - 64:36
    Convergent validity and
    discriminate validity.
  • 64:36 - 64:37
    Does a pattern make sense?
  • 64:37 - 64:40
    Relationship between
    reliability and validity.
  • 64:40 - 64:43
    What are the three
    reliabilities again?
  • 64:46 - 64:52
    Rick. Alesia or whoever
    was saying that.
  • 64:52 - 65:00
    Test-retest. I keep
    wanting to say relator.
  • 65:00 - 65:01
    Interrater.
  • 65:01 - 65:09
    Interrater. My gosh. I'm
    blanking on the third one.
  • 65:11 - 65:16
    Internal. How do we
    measure the strength of
  • 65:16 - 65:25
    those reliabilities?
    It's a letter.
  • 65:28 - 65:30
    The coefficient R.
  • 65:30 - 65:34
    Coefficient R. The
    coefficient R,
  • 65:34 - 65:39
    what are the endpoints?
    Or what's the range?
  • 65:39 - 65:41
    Negative one to one
    including [inaudible]
  • 65:41 - 65:44
    Perfect. What do
    we want the value
  • 65:44 - 65:48
    to be to be good reliability?
  • 65:51 - 65:57
    We want it to be positive and
    closer to one as possible.
  • 65:57 - 66:01
    That's what I've been lecturing
    about for the last hour.
  • 66:01 - 66:05
    [LAUGHTER] Also last
    lecture and this lecture.
  • 66:05 - 66:08
    I just want to make sure we
    are all on the same page.
  • 66:08 - 66:10
    Now we're moving to validity.
  • 66:12 - 66:15
    Let's move on to validity.
  • 66:16 - 66:19
    We talked about
    reliability here,
  • 66:19 - 66:23
    so test-retest, internal,
    and interrater reliability.
  • 66:23 - 66:25
    Now, we're going to move on to
  • 66:25 - 66:27
    here with construct validity,
  • 66:27 - 66:30
    and we'll talk about all of
    these different validities,
  • 66:30 - 66:31
    but we're mostly
    going to be talking
  • 66:31 - 66:34
    about construct validity here.
  • 66:34 - 66:37
    We're going to jump into
  • 66:37 - 66:39
    two subjective ways
    to assess validity,
  • 66:39 - 66:43
    so face validity and
    content validity.
  • 66:43 - 66:47
    Face validity is exactly
    what it sounds like.
  • 66:47 - 66:51
    Does your measure look like
    what you want to measure?
  • 66:51 - 66:54
    Then content validity
    is the measure
  • 66:54 - 66:56
    contains all the parts that
  • 66:56 - 66:59
    your theory says
    it should contain.
  • 66:59 - 67:01
    We have different standards for
  • 67:01 - 67:03
    measurements as
    physical scientists,
  • 67:03 - 67:05
    so we can use rulers, scales,
  • 67:05 - 67:07
    thermometers to ensure that
  • 67:07 - 67:08
    we're measuring
    things like length,
  • 67:08 - 67:11
    weight, and
    temperature reliably.
  • 67:11 - 67:13
    However, psychological
    scientists
  • 67:13 - 67:14
    are interested in measuring
  • 67:14 - 67:16
    abstract concepts
    like self-esteem,
  • 67:16 - 67:20
    or how much you hate
    your job, or depression.
  • 67:20 - 67:21
    That's more abstract.
  • 67:21 - 67:24
    Because of that, our
    construct validity is a lot
  • 67:24 - 67:30
    more important to
    psychological scientists.
  • 67:30 - 67:32
    For people who measure
    physical things like
  • 67:32 - 67:35
    weight, well,
    construct validity.
  • 67:35 - 67:36
    How would you measure weight?
  • 67:36 - 67:38
    The scale.
  • 67:38 - 67:42
    That's pretty easy, but for
    more abstract concepts,
  • 67:42 - 67:46
    construct validity
    is super important.
  • 67:46 - 67:51
    Let's jump into face validity
    and content validity.
  • 67:51 - 67:54
    Like I said, face
    validity is it looks
  • 67:54 - 67:56
    like what you want to measure
    and content validity,
  • 67:56 - 67:58
    the measure contains all of
  • 67:58 - 68:01
    the parts your theory
    says it should contain.
  • 68:06 - 68:09
    Alesia, what is
    your study about?
  • 68:10 - 68:13
    How introversion and
  • 68:13 - 68:19
    extroversion affect
    anxiety management.
  • 68:19 - 68:21
    Having good validity would
  • 68:21 - 68:25
    be asking questions
    related to introversion.
  • 68:25 - 68:29
    Do you enjoy spending
    time with others?
  • 68:29 - 68:30
    Do you get energized by spending
  • 68:30 - 68:32
    time with others?
    Things like that.
  • 68:32 - 68:37
    It seems to have good face
    validity and content validity.
  • 68:37 - 68:38
    Contains all the parts that
  • 68:38 - 68:40
    your theory says
    it should contain.
  • 68:40 - 68:41
    With introversion, we're talking
  • 68:41 - 68:44
    about how much time you
    like spending with others,
  • 68:44 - 68:45
    so we're asking those questions,
  • 68:45 - 68:49
    but if I ask
    questions related to,
  • 68:50 - 68:53
    how much time you spend online,
  • 68:53 - 68:56
    that might not be good content
    validity because it's not
  • 68:56 - 68:58
    assessing how much time you
    enjoy spending with others.
  • 68:58 - 69:01
    [inaudible]
  • 69:01 - 69:03
    It could if you did how
    much time you spent on
  • 69:03 - 69:07
    Facebook talking to people
    or whatever the case may be.
  • 69:07 - 69:09
    Does that make sense, face
  • 69:09 - 69:11
    validity and content validity?
  • 69:11 - 69:13
    If I was measuring happiness,
  • 69:13 - 69:15
    it might be asking questions
    about how happy you are,
  • 69:15 - 69:17
    how sad you are.
  • 69:17 - 69:19
    It might not have as
  • 69:19 - 69:23
    good content validity if
    I ask how angry you are,
  • 69:23 - 69:25
    how violent you are.
  • 69:25 - 69:28
    That might not be a great
    measure of happiness.
  • 69:28 - 69:30
    It might be a great
    measure of likelihood to
  • 69:30 - 69:34
    engage in violence,
    but not happiness.
  • 69:36 - 69:39
    Then we have criterion validity,
  • 69:39 - 69:44
    and this is when you
    want to see whether
  • 69:44 - 69:49
    your variable of interest
    correlates with key behaviors.
  • 69:50 - 69:53
    Most psychological
    scientists prefer to rely on
  • 69:53 - 69:54
    empirical assessments of
  • 69:54 - 69:57
    validity over
    subjective judgments.
  • 69:57 - 70:00
    One way to empirically
    assess validity is
  • 70:00 - 70:03
    by examining criterion
    validity, which is here.
  • 70:03 - 70:05
    Whether the measure
    is related to
  • 70:05 - 70:08
    a concrete outcome that
    it should be related to.
  • 70:08 - 70:10
    Let's say you work
    for a company that
  • 70:10 - 70:11
    wants to predict how well
  • 70:11 - 70:16
    job applicants do a salespeople.
  • 70:16 - 70:19
    Initially, the company used
  • 70:19 - 70:25
    IQ scores to predict sales
    aptitude. Here we go.
  • 70:25 - 70:28
    The criterion
    validity. Right here,
  • 70:28 - 70:31
    we're checking how
    well aptitude test
  • 70:31 - 70:37
    A relates to sale figures
    in thousands of dollars.
  • 70:37 - 70:39
    You wanted your IQ test to
  • 70:39 - 70:43
    predict or relate to a
    behavior, sales figures.
  • 70:43 - 70:44
    As you can tell,
  • 70:44 - 70:47
    as aptitude test scores go up,
  • 70:47 - 70:51
    so do sales figures in
    dollars. They're, great.
  • 70:51 - 70:56
    We can use IQ to predict how
    well our employees will do.
  • 70:56 - 71:01
    Let's say that then they
    also have aptitude test B,
  • 71:01 - 71:04
    which is a different IQ test.
  • 71:04 - 71:05
    They're looking at
    how that relates to
  • 71:05 - 71:07
    sales figures and what they
  • 71:07 - 71:09
    find because these dots are a
  • 71:09 - 71:11
    lot more scattered
    from that line,
  • 71:11 - 71:15
    that aptitude test B
    doesn't have as great of
  • 71:15 - 71:18
    a criterion validity
    as aptitude test A.
  • 71:18 - 71:21
    That means that aptitude
    test B is less likely to
  • 71:21 - 71:26
    predict sales figures
    relative to aptitude test A.
  • 71:26 - 71:31
    Is that clear? Just
    criterion validity is you're
  • 71:31 - 71:34
    using one measure to
  • 71:34 - 71:38
    predict behaviors or
    something another scale.
  • 71:38 - 71:42
    You're comparing the measures
    that you're going to
  • 71:42 - 71:45
    use to see which one
    is best serious.
  • 71:45 - 71:50
    No, that's good.
  • 71:50 - 71:53
    For the burning of the building,
  • 71:53 - 71:58
    you might want to use burning
    of the building scale
  • 71:58 - 72:04
    to what people actually do
    when the building is on fire,
  • 72:04 - 72:06
    how likely you are
    to put the fire
  • 72:06 - 72:07
    out if there is an actual fire.
  • 72:07 - 72:09
    What you find is maybe
  • 72:09 - 72:12
    people burning saying
    that they would burn
  • 72:12 - 72:15
    a building is not the same
    as what they actually do in
  • 72:15 - 72:19
    a fire so might not have
    great criterion validity.
  • 72:20 - 72:23
    Another way to gather evidence
    for criterion validity
  • 72:23 - 72:27
    is to use a known
    groups paradigm.
  • 72:27 - 72:30
    What is a knowns group paradigm?
  • 72:30 - 72:31
    This is when you have
  • 72:31 - 72:33
    a group that you know
    that scores high
  • 72:33 - 72:37
    on this measure or on
    a related measure,
  • 72:37 - 72:39
    and you create a new measure.
  • 72:39 - 72:43
    For instance, the Beck
    Depression Inventory
  • 72:43 - 72:46
    is one of the most
    popularly used inventory
  • 72:46 - 72:47
    to measure depression.
  • 72:47 - 72:48
    But when it first came out,
  • 72:48 - 72:52
    there were already existing
    measures of depression.
  • 72:52 - 72:53
    If you want to create
    a new measure,
  • 72:53 - 72:57
    you have to have a good reason
    to include that measure.
  • 72:57 - 72:58
    The BEC depression inventory is
  • 72:58 - 73:01
    a 21 item, self report scales.
  • 73:01 - 73:04
    In order to test the
    criterion validity
  • 73:04 - 73:06
    of this depression scale,
  • 73:06 - 73:08
    you could administer the BDI
  • 73:08 - 73:10
    to a group of people
    with depression,
  • 73:10 - 73:11
    so are already in treatment for
  • 73:11 - 73:16
    depression and a group of
    people who aren't depressed.
  • 73:16 - 73:18
    If you find here,
  • 73:18 - 73:19
    this is the known group.
  • 73:19 - 73:22
    You have a group of
    people who are depressed.
  • 73:22 - 73:25
    You have an inventory that
    should measure depression.
  • 73:25 - 73:29
    If you find that depressed
    people score higher on
  • 73:29 - 73:30
    your depression
    inventory relative
  • 73:30 - 73:32
    to those that are not depressed,
  • 73:32 - 73:36
    then that tells us that there's
    good criterion validity.
  • 73:36 - 73:38
    The depression
    inventory actually
  • 73:38 - 73:42
    predicts who is depressed
    versus who is not.
  • 73:42 - 73:45
    This is the known groups method.
  • 73:45 - 73:48
    Another way is to separate
  • 73:48 - 73:53
    the depression that people
    are experiencing from mild,
  • 73:53 - 73:54
    moderate, and severe.
  • 73:54 - 73:57
    This could be the
    psychiatrists rating of how
  • 73:57 - 74:01
    much their patients are
    experiencing depression,
  • 74:01 - 74:02
    and the BDI score,
  • 74:02 - 74:05
    the BEC depression
    inventory should be able
  • 74:05 - 74:07
    to distinguish between
    those who have mild,
  • 74:07 - 74:10
    moderate and severe depression,
    which happens here.
  • 74:10 - 74:13
    Severe people are experiencing
    the most amount of
  • 74:13 - 74:15
    BDI relative to those who are
  • 74:15 - 74:19
    moderate and mild depression.
  • 74:19 - 74:25
    Is that clear? Do you guys
  • 74:25 - 74:28
    like me to use the
    weird examples.
  • 74:28 - 74:31
    Example. We're good.
  • 74:31 - 74:33
    I was fine. The burning
  • 74:33 - 74:36
    the house down or burning
    your place of employment,
  • 74:36 - 74:38
    you might have pyromaniacs and
  • 74:38 - 74:41
    measure whether
    pyromaniacs would
  • 74:41 - 74:44
    score higher on the
    likelihood to burn
  • 74:44 - 74:46
    the employment down relative to
  • 74:46 - 74:48
    those who are not pyromaniacs,
  • 74:48 - 74:50
    and you would want
    people who are
  • 74:50 - 74:52
    pyromaniacs to score higher on
  • 74:52 - 74:55
    likelihood of burning place
    of employment down relative
  • 74:55 - 74:58
    to those that
    aren't pyromaniacs.
  • 74:58 - 75:02
    That would be a good
    measure of fire.
  • 75:04 - 75:06
    I actually like my job.
  • 75:06 - 75:09
    I don't know why I came
    up with that. [LAUGHTER]
  • 75:09 - 75:11
    It's very dark.
  • 75:11 - 75:13
    It is very dark. I don't
  • 75:13 - 75:15
    know I came up
    with that example.
  • 75:16 - 75:19
    Perfect. Does that make sense?
  • 75:19 - 75:21
    Your measure should be able to
  • 75:21 - 75:23
    predict likelihood of starting
  • 75:23 - 75:26
    a fire and the degree
  • 75:26 - 75:28
    to which you would be
    likely to start a fire.
  • 75:28 - 75:33
    Pyromaniac should
    be high here and
  • 75:33 - 75:36
    then mild pyromaniacs versus
  • 75:36 - 75:39
    moderate pyromaniacs
    versus severe pyromaniacs,
  • 75:39 - 75:42
    it should also be
    able to predict
  • 75:42 - 75:44
    the extent to which
    people are pyromaniacs.
  • 75:44 - 75:46
    That's one way to establish
  • 75:46 - 75:48
    whether your scale
    is good or not.
  • 75:50 - 75:53
    We can also use known
    groups evidence
  • 75:53 - 75:55
    for criterion validity.
  • 75:55 - 75:58
    This is Diener's subjective
    well being scale.
  • 75:58 - 76:02
    Remember, Diener created
    this to measure happiness.
  • 76:02 - 76:06
    We're using it for different
    groups of individuals.
  • 76:06 - 76:12
    What we are using is
    American college students,
  • 76:12 - 76:14
    French Canadian
    college students,
  • 76:14 - 76:17
    Korean university students,
    printing trade workers,
  • 76:17 - 76:19
    veterans Affairs
    hospital patients,
  • 76:19 - 76:23
    abused women, and
    male prison innates.
  • 76:23 - 76:30
    These are the references where
    they got the scores from.
  • 76:30 - 76:35
    What the Diener's subjective
    well being scale would want
  • 76:35 - 76:37
    is scores that map on to
  • 76:37 - 76:40
    these different
    measures of happiness.
  • 76:40 - 76:43
    Does that make sense? When
    you measure happiness
  • 76:43 - 76:46
    with Pavot and Diener measure,
  • 76:46 - 76:48
    you have a 6.4 of
  • 76:48 - 76:49
    happiness for American
    college students.
  • 76:49 - 76:52
    With Blais et al, you have 6.1,
  • 76:52 - 76:56
    for Suh study, it's 5.8,
  • 76:56 - 76:58
    for George, it's 6.0,
  • 76:58 - 77:00
    for veterans Affairs
    hospital inpatients
  • 77:00 - 77:02
    with Frisch, it is 5.6,
  • 77:02 - 77:04
    for Fisher at 7.4,
  • 77:04 - 77:08
    and that's the
    standard deviation.
  • 77:08 - 77:10
    Abused women is 20.7,
  • 77:10 - 77:14
    and male prison inmates
    with Joy is 12.3.
  • 77:14 - 77:15
    Just by looking at this,
  • 77:15 - 77:17
    do you think that
  • 77:17 - 77:20
    these measures are okay
    measures of happiness?
  • 77:21 - 77:26
    For instance, who is the
    happiest based on these values?
  • 77:30 - 77:32
    Printing trade workers.
  • 77:32 - 77:34
    Printing trade workers,
  • 77:35 - 77:38
    their jobs are
    probably pretty chill.
  • 77:38 - 77:40
    American college students or
  • 77:40 - 77:42
    these college students might
    still be super stressed out,
  • 77:42 - 77:44
    but they're still
    relatively happy.
  • 77:44 - 77:49
    What about male prison inmates
  • 77:49 - 77:52
    and Veterans Affair
    hospital inpatients?
  • 77:52 - 77:54
    They're pretty low on happiness.
  • 77:54 - 77:55
    It tracks so it does seem
  • 77:55 - 78:00
    to accurately measure happiness.
  • 78:01 - 78:06
    Because the actual values
  • 78:06 - 78:08
    of happiness are tracking
    what we would expect,
  • 78:08 - 78:12
    then we can say that the
    subjective well-being scale
  • 78:12 - 78:16
    would have high
    criterion validity.
  • 78:16 - 78:18
    It actually measures
    what it's intended to
  • 78:18 - 78:21
    measure. Does that make sense?
  • 78:21 - 78:25
    Yeah, I can read that one.
  • 78:25 - 78:29
    This one's very clear. Now we
  • 78:29 - 78:34
    have convergent validity
    and discriminant validity.
  • 78:35 - 78:38
    This is concerned
    with whether there is
  • 78:38 - 78:41
    a meaningful pattern of
    similarities and differences.
  • 78:41 - 78:43
    Convergent, as you might guess,
  • 78:43 - 78:46
    is how well does the BDI,
  • 78:46 - 78:49
    Beck Depression
    Inventory relate to
  • 78:49 - 78:53
    an already existing
    inventory for depression,
  • 78:53 - 78:57
    which is the Center
    for Epidemiologic
  • 78:57 - 78:59
    Studies Depression Scale.
  • 78:59 - 79:03
    It's not hard work.
  • 79:03 - 79:06
    The Center for Epidemiologic,
  • 79:06 - 79:09
    no I can't say it.
  • 79:09 - 79:11
    You know what I'm trying to say.
  • 79:11 - 79:13
    Studies Depression Scale,
  • 79:13 - 79:17
    and these have a correlation
    coefficient of 0.68.
  • 79:17 - 79:20
    I'm just going to
    call it the CES-D.
  • 79:20 - 79:23
    When the Beck Depression
    Inventory came on the scene,
  • 79:23 - 79:27
    this was the most popularly
    used measure of depression.
  • 79:27 - 79:29
    But the BDI needs to
  • 79:29 - 79:34
    be similar or converge
  • 79:34 - 79:38
    on an already existing
    measure of depression,
  • 79:38 - 79:39
    but it also needs to be slightly
  • 79:39 - 79:41
    unique because if it's
    exactly the same,
  • 79:41 - 79:43
    then why are we
    creating a new scale?
  • 79:43 - 79:45
    Right here, let's say
  • 79:45 - 79:47
    we have people that we
    know are depressed,
  • 79:47 - 79:51
    and they take both
    CES-D and the BDI.
  • 79:51 - 79:55
    What we see is that the
    scores do tend to converge.
  • 79:55 - 79:57
    As people increase in BDI,
  • 79:57 - 80:01
    they also tend to
    increase in CES-D.
  • 80:01 - 80:04
    But it's not a perfect
    relationship, and that's fine.
  • 80:04 - 80:07
    We don't actually want
    it to be perfect.
  • 80:07 - 80:10
    Now, discriminant
    validity is how
  • 80:10 - 80:16
    well and how different is this
    scale from other measures.
  • 80:17 - 80:19
    A measure should
    correlate less strongly
  • 80:19 - 80:22
    with measures of
    different constructs.
  • 80:22 - 80:24
    In other words, there
    must be differences.
  • 80:24 - 80:27
    The Beck Depression Inventory
    is supposed to measure
  • 80:27 - 80:32
    psychological well being or
  • 80:32 - 80:34
    non-well being and not
  • 80:34 - 80:36
    necessarily physical
    health problems.
  • 80:36 - 80:39
    If this has good
    discriminate validity,
  • 80:39 - 80:42
    which it does, that
    means that your BDI,
  • 80:42 - 80:44
    Beck Depression
    Inventory should be not
  • 80:44 - 80:46
    related to physical
    health problems
  • 80:46 - 80:48
    and that's what we find.
  • 80:48 - 80:51
    As BDI increases, it doesn't
  • 80:51 - 80:55
    necessarily predict how many
    health problems you'll have.
  • 80:55 - 80:58
    Convergent validity
    is how well does
  • 80:58 - 81:01
    your measure map onto
    already existing measures,
  • 81:01 - 81:05
    and discriminant
    validity is about how
  • 81:05 - 81:14
    much your measure correlates
  • 81:14 - 81:17
    with measures of
    different constructs.
  • 81:20 - 81:23
    Think of convergent as same and
  • 81:23 - 81:26
    discriminant as different
    with different scales.
  • 81:32 - 81:38
    Any questions here?
    Then the relationship
  • 81:38 - 81:41
    between reliability
    and validity.
  • 81:41 - 81:45
    We talked a lot about
    reliability and validity.
  • 81:45 - 81:47
    Remember that just
    because a measure is
  • 81:47 - 81:51
    reliable doesn't mean
    it's a valid measure.
  • 81:51 - 81:57
    For instance, I could measure
    my cat's head every day,
  • 81:57 - 82:00
    and I'll have a really reliable
    measure of my cat's head,
  • 82:00 - 82:05
    but that might not be
    a valid measure of IQ.
  • 82:06 - 82:11
    Head size might not be
    a valid measure of IQ,
  • 82:11 - 82:15
    meaning that it doesn't
    actually measure IQ.
  • 82:15 - 82:18
    It's reliable, but
    it's not valid.
  • 82:18 - 82:21
    Reliability is more of how well
  • 82:21 - 82:24
    a measure correlates
    with itself.
  • 82:24 - 82:26
    Invalidity is how
    well a measure is
  • 82:26 - 82:30
    associated with something else.
  • 82:33 - 82:37
    A measure can be less
    valid than it is reliable,
  • 82:37 - 82:41
    but it cannot be more
    valid than it is reliable.
  • 82:44 - 82:47
    For instance, a measure can
    be less valid than it is
  • 82:47 - 82:51
    reliable so measuring
    my cat's head
  • 82:51 - 82:53
    might not be related to its IQ,
  • 82:53 - 82:55
    so it's less valid
    than it's reliable,
  • 82:55 - 82:59
    but it can't be more
    valid than it's reliable.
  • 83:00 - 83:05
    IQ scores cannot be a
    more valid measure of
  • 83:05 - 83:09
    head circumference relative to
  • 83:09 - 83:13
    how reliable the head
    circumference and IQ is.
  • 83:13 - 83:19
    Reliability is necessary but
    not sufficient for validity.
  • 83:19 - 83:21
    These are just things
    to consider as you're
  • 83:21 - 83:25
    thinking about your projects.
  • 83:25 - 83:28
    There will be some checks and
  • 83:28 - 83:30
    balances that you'll have
    to go through when you're
  • 83:30 - 83:32
    thinking about validity
    and reliability.
  • 83:32 - 83:36
    You'll want your scales to be
  • 83:36 - 83:39
    reliable and you'll also
    want them to be valid.
  • 83:39 - 83:41
    Reliable means that the scales
  • 83:41 - 83:43
    actually measure what you
    want them to measure,
  • 83:43 - 83:45
    and validity is how well
  • 83:45 - 83:47
    that measure is associated
    with something else.
  • 83:47 - 83:50
    For instance, Hailey, if you're
  • 83:50 - 83:53
    interested in job persistence,
  • 83:53 - 83:56
    you want the measure of job
    persistence to actually
  • 83:56 - 83:58
    predict the people being
  • 83:58 - 84:01
    in their jobs maybe
    five years later.
  • 84:01 - 84:04
    That would be the validity.
  • 84:05 - 84:08
    That is it for today.
  • 84:08 - 84:11
    Any questions?
  • 84:13 - 84:16
    We're going through
    a lot of chapters,
  • 84:16 - 84:17
    and I think it's good
    that we're actually
  • 84:17 - 84:20
    keeping on track.
  • 84:20 - 84:24
    But if you don't have any more
    questions, then that's it.
  • 84:24 - 84:29
    Enjoy your weekend.
    Alasia, if you
  • 84:29 - 84:33
    want to stay. Thank you.
  • 84:33 - 84:34
    Thank you.
  • 84:34 - 84:34
    Thanks.
  • 84:34 - 84:35
    Thank you.
  • 84:35 - 84:37
    Thank you.
  • 84:41 - 84:46
    Let's see. I stopped
    recording. I think I did.
Title:
10.12 - Intro Review & Good Measurement Pt 2
Video Language:
English
Duration:
01:24:45

English subtitles

Revisions Compare revisions