100% tevredenheidsgarantie Direct beschikbaar na betaling Zowel online als in PDF Je zit nergens aan vast
logo-home
Summary Research Methods and Statistics - Exam 2 €4,49   In winkelwagen

Samenvatting

Summary Research Methods and Statistics - Exam 2

5 beoordelingen
 148 keer bekeken  23 keer verkocht

Smmary of Morling and Agresti (with lectures added in) needed for the second exam of Research Methods and Statistics! Exam 2 information: Morling Chapter 3, chapter 6, chapter 7 and chapter 10 Agresti Chapter 6, chapter 7 and chapter 8 (except paragraph 8.5)

Voorbeeld 3 van de 20  pagina's

  • 22 oktober 2018
  • 20
  • 2018/2019
  • Samenvatting
Alle documenten voor dit vak (4)

5  beoordelingen

review-writer-avatar

Door: Maartjepapen • 4 jaar geleden

review-writer-avatar

Door: justinapps15 • 5 jaar geleden

review-writer-avatar

Door: alecbaxendale • 5 jaar geleden

helped a bunch but could be made better for understanding, and maybe some examples should be given for practical understanding in the statistics part.

reply-writer-avatar

Door: QueenNope • 5 jaar geleden

Thank you for your feedback, really helpful!

review-writer-avatar

Door: znlwm00 • 5 jaar geleden

review-writer-avatar

Door: notesbymau • 5 jaar geleden

avatar-seller
QueenNope
Exam 2 information:
Morling
Chapter 3, chapter 6, chapter 7 and chapter 10

Agresti
Chapter 6, chapter 7 and chapter 8 (except paragraph 8.5)

Chapter 3 - Three Claims, Four Validities: Interrogation Tools for Consumers of
Research (Morling)

Variables
A ​variable​​ is a thing that can take multiple “values”. More specifically, it’s described
in the book that a variable is any characteristic observed in a study, as something that varies.
A ​measured variable​​ (or dependent variable) is a variable whose levels are simply observed
and recorded. A ​manipulated variable​​ (or independent variable) is a variable a researcher
controls. ​Conceptual variables​​ are abstract concepts, this is sometimes called a ​construct​​.
Conceptual variables must be carefully defined at the theoretical level, and these definitions
are called ​conceptual definitions​​. They turn these concepts of interest into a measured or
manipulated variable, known as ​operationalizing​​. They create ​operational definitions​​ of the
variables, also known as ​operational variables​​.

Three Claims
A ​claim​​ is an argument someone is trying to make. ​Frequency claims​​ describe a
particular rate or degree of a single variable (e.g. “2 out of 5”, “25% says…”). Frequency
claims focus on one variable. In studies that support frequency claims, the variables are
always measured, not manipulated. An ​association claim​​ argues that one level of a variable
is likely to be associated with a particular level of another variable. They are said to correlate.
An association claim states a relationship between at least two variables. In a study that
measures variables and makes a claim about whether or not two variables correlate, is called
a ​correlation study​​. A ​causal claim​​ argues that one of the variables is responsible for
changing the other variable. Causal claims are a step above association claims. To move from
association to causality, a study has to satisfy three criteria.
1) It must establish that the two variables are correlated, the relationship
cannot be zero. -> Covariance / Statistical validity
2) It must show that the causal variable came first an the outcome variable
came later. -> Temporal precedence.
3) It must establish that no other explanation exists for the relationship.
Only an experiment will support a causal claim. -> Internal validity.

,Interrogating the Three Claims Using the Four Big Validities
Validity​​ refers to the appropriateness of a conclusion or decision, and in general, is a
valid​ claim is reasonable, accurate, and justifiable. The four big validities are:
1) Construct validity - How well is the conceptual variable operationalized?
2) Statistical validity - The extent to which a study’s statistical conclusions are
accurate and reasonable.
3) Internal validity - ​refers to how well an experiment is done, especially
whether it avoids confounding (more than one possible independent variable [cause] acting at
the same time).
4) External validity - How well do the results of a study generalize to, or
represent, people or contexts beside those in the original study?




Interrogating Frequency Claims
When evaluating the construct validity of a frequency claim, the question is how well
the researchers measured their variables. When asking how well a study measured or
manipulated a variable, you are interrogating the construct validity. To ensure construct
validity, the researchers must prove the variables to be measured reliably, so in other words
that several testings showed similar results. ​Generalizability​​ is whether or not the results of
the study are transferable from the specific group used to the general population. This is
important for the external validity. For the statistical validity, we ask ourselves how well the
numbers support the claim. The percentage reported in a frequency claim is usually
accompanied by a margin of error of the estimate, which should give you a solid idea.

Interrogating Association Claims
To support an association claim, a researcher measures two variable, so you have to
assess the construct validity of each variable. If you conclude one of the variables was
measures poorly, you would not be able to trust the conclusions related to that variable. You
might also interrogate the external validity of an association claim b asking whether it can
generalize to other populations, as well as to other contexts, times, or places. When applied to
an association claim, statistical validity is the extent to which the statistical conclusions are

, accurate and reasonable. One aspect of statistical validity is strength, so how strong the the
association is. Another question worth interrogating is the statistical significance of a
particular association, because some associations might be due to chance. Now comes the
hard part. There is a Type I error an a Type II error. In the book, a ​type I error​​ is described
as a “false positive”. In the book they give the example of finding an association between two
variables in a sample, but no association in the population. It is also known as rejecting a true
null hypothesis. A ​null hypothesis​​ is a hypothesis that states that there is no association or
relationship between two variables. Rejecting a null hypothesis is assuming there is a
relationship. So a Type I error is rejecting the null hypothesis (there is no relationship),
assuming there is an association, even though there is none. A ​Type II error​​ is described in
the book is assuming there is no relationship or association between two variables, because it
didn’t show in the sample, but seeing an association in the population. This is known as a
“miss”. This is when we fail to reject a false null hypothesis, so there is no evidence in the
sample that there is an association, but we see one in the population.

Interrogating Causal Claims
We already discussed the three criteria for causation, but just because the books and
Dylan can’t seem to stress it enough, let’s have a reminder:
1) ​Covariance - ​The extent to which two variables are observed to go
together. One variable usually cannot be said to cause another variable unless the two are
related. The relationship cannot be zero.
2) ​Temporal precedence - ​One variable comes first in time, before the other
variable. It must show that the causal variable came first and the outcome variable came later.
3) ​Internal Validity​​, or ​third-variable criterion​, - There is no other
explanation for the relationship. It eliminates alternative explanations for the association.
To support a causal claim, researchers must conduct an experiment. The manipulated
variable is the cause and the outcome is the measures variable. The manipulated variable is
called the independent variable, and the measures variable is called the dependent variable.
Random assignment​​ is the, perhaps you guessed it, randomly assignment of participants into
groups, so the groups are similar. This is a way to control potential alternative explanations.

Types of Research and their claims
1) Surveys and/or interview research
2) Observational research
3) Correlational research
Studies association between two variables.
4) Experimental research
Manipulate the cause and study the effect.

Voordelen van het kopen van samenvattingen bij Stuvia op een rij:

Verzekerd van kwaliteit door reviews

Verzekerd van kwaliteit door reviews

Stuvia-klanten hebben meer dan 700.000 samenvattingen beoordeeld. Zo weet je zeker dat je de beste documenten koopt!

Snel en makkelijk kopen

Snel en makkelijk kopen

Je betaalt supersnel en eenmalig met iDeal, creditcard of Stuvia-tegoed voor de samenvatting. Zonder lidmaatschap.

Focus op de essentie

Focus op de essentie

Samenvattingen worden geschreven voor en door anderen. Daarom zijn de samenvattingen altijd betrouwbaar en actueel. Zo kom je snel tot de kern!

Veelgestelde vragen

Wat krijg ik als ik dit document koop?

Je krijgt een PDF, die direct beschikbaar is na je aankoop. Het gekochte document is altijd, overal en oneindig toegankelijk via je profiel.

Tevredenheidsgarantie: hoe werkt dat?

Onze tevredenheidsgarantie zorgt ervoor dat je altijd een studiedocument vindt dat goed bij je past. Je vult een formulier in en onze klantenservice regelt de rest.

Van wie koop ik deze samenvatting?

Stuvia is een marktplaats, je koop dit document dus niet van ons, maar van verkoper QueenNope. Stuvia faciliteert de betaling aan de verkoper.

Zit ik meteen vast aan een abonnement?

Nee, je koopt alleen deze samenvatting voor €4,49. Je zit daarna nergens aan vast.

Is Stuvia te vertrouwen?

4,6 sterren op Google & Trustpilot (+1000 reviews)

Afgelopen 30 dagen zijn er 62890 samenvattingen verkocht

Opgericht in 2010, al 14 jaar dé plek om samenvattingen te kopen

Start met verkopen
€4,49  23x  verkocht
  • (5)
  Kopen