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    which one of the following is not the condition for causality? curvilinear variation temporal precedence concomitant variation no plausible alternative explanations

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    Establishing Cause & Effect

    How do we establish a cause-effect (causal) relationship? What criteria do we have to meet?

    Establishing a Cause-Effect Relationship

    How do we establish a cause-effect (causal) relationship? What criteria do we have to meet? Generally, there are three criteria that you must meet before you can say that you have evidence for a causal relationship:

    Temporal Precedence

    First, you have to be able to show that your cause happened before your effect. Sounds easy, huh? Of course my cause has to happen before the effect. Did you ever hear of an effect happening before its cause? Before we get lost in the logic here, consider a classic example from economics: does inflation cause unemployment? It certainly seems plausible that as inflation increases, more employers find that in order to meet costs they have to lay off employees. So it seems that inflation could, at least partially, be a cause for unemployment. But both inflation and employment rates are occurring together on an ongoing basis. Is it possible that fluctuations in employment can affect inflation? If we have an increase in the work force (i.e., lower unemployment) we may have more demand for goods, which would tend to drive up the prices (i.e., inflate them) at least until supply can catch up. So which is the cause and which the effect, inflation or unemployment? It turns out that in this kind of cyclical situation involving ongoing processes that interact that both may cause and, in turn, be affected by the other. This makes it very hard to establish a causal relationship in this situation.

    Covariation of the Cause and Effect

    What does this mean? Before you can show that you have a causal relationship you have to show that you have some type of relationship. For instance, consider the syllogism:

    if X then Y if not X then not Y

    If you observe that whenever X is present, Y is also present, and whenever X is absent, Y is too, then you have demonstrated that there is a relationship between X and Y. I don’t know about you, but sometimes I find it’s not easy to think about X’s and Y’s. Let’s put this same syllogism in program evaluation terms:

    if program then outcome

    if not program then not outcome

    Or, in colloquial terms: if you give a program you observe the outcome but if you don’t give the program you don’t observe the outcome. This provides evidence that the program and outcome are related. Notice, however, that this syllogism doesn’t not provide evidence that the program caused the outcome — perhaps there was some other factor present with the program that caused the outcome, rather than the program. The relationships described so far are rather simple binary relationships. Sometimes we want to know whether different amounts of the program lead to different amounts of the outcome — a continuous relationship:

    if more of the program then more of the outcome

    if less of the program then less of the outcome

    No Plausible Alternative Explanations

    Just because you show there’s a relationship doesn’t mean it’s a causal one. It’s possible that there is some other variable or factor that is causing the outcome. This is sometimes referred to as the “third variable” or “missing variable” problem and it’s at the heart of the issue of internal validity. What are some of the possible plausible alternative explanations? Just go look at the threats to internal validity (see single group threats, multiple group threats or social threats) — each one describes a type of alternative explanation.

    In order for you to argue that you have demonstrated internal validity — that you have shown there’s a causal relationship — you have to “rule out” the plausible alternative explanations. How do you do that? One of the major ways is with your research design. Let’s consider a simple single group threat to internal validity, a history threat. Let’s assume you measure your program group before they start the program (to establish a baseline), you give them the program, and then you measure their performance afterwards in a posttest. You see a marked improvement in their performance which you would like to infer is caused by your program. One of the plausible alternative explanations is that you have a history threat — it’s not your program that caused the gain but some other specific historical event. For instance, it’s not your anti-smoking campaign that caused the reduction in smoking but rather the Surgeon General’s latest report that happened to be issued between the time you gave your pretest and posttest. How do you rule this out with your research design? One of the simplest ways would be to incorporate the use of a control group — a group that is comparable to your program group with the only difference being that they didn’t receive the program. But they did experience the Surgeon General’s latest report. If you find that they didn’t show a reduction in smoking even though they did experience the same Surgeon General report you have effectively “ruled out” the Surgeon General’s report as a plausible alternative explanation for why you observed the smoking reduction.

    In most applied social research that involves evaluating programs, temporal precedence is not a difficult criterion to meet because you administer the program before you measure effects. And, establishing covariation is relatively simple because you have some control over the program and can set things up so that you have some people who get it and some who don’t (if X and if not X). Typically the most difficult criterion to meet is the third —ruling out alternative explanations for the observed effect. That is why research design is such an important issue and why it is intimately linked to the idea of internal validity.

    स्रोत : conjointly.com

    Research Methods 3 Flashcards

    Study with Quizlet and memorize flashcards containing terms like independent variable, dependent variable, covariance and more.

    Research Methods 3

    Term 1 / 66

    independent variable

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    Definition 1 / 66

    The experimental factor that is manipulated; the variable whose effect is being studied.

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    Created by myamercer2446

    Terms in this set (66)

    independent variable

    The experimental factor that is manipulated; the variable whose effect is being studied.

    dependent variable

    The measurable effect, outcome, or response in which the research is interested. how the people act on the measured variable depends on the level of independent variable.

    covariance

    that the causal variable must vary systematically with changes in the proposed outcome variable. 2 variables go together.

    temporal precedence

    the proposed casual claim comes 1st in time, before the proposed outcome variable.

    internal validity

    a study's ability to rule out alternative explanations for a casual relationship between two variables.

    confounds

    a potential alternative explanation for a research finding (a threat to internal validity)

    independent groups design: posttest only

    participants are randomly assigned to independent variable groups/ tested on dv once.

    satisfy casual claim (covariance, temporal precedence, internal validity)

    independent groups design: pretest/ posttest

    randomly assigned to atleast two different groups and are tested on the key dependent variable twice- once before and after exposure to the independent variable.

    - enables to track ppl's change in performance

    - use to study improvement over time

    within groups design: repeated measures

    participants are measured on a dependent variable more than once, after exposure to each level of the independent variable.

    within groups design: concurrent measures

    participants are exposed to all the levels of an independent variable at roughly the same time, and a single behavioral preferences is the dependent variable.

    order effects

    a threat to internal validity in which exposure to one condition changes participants' responses to a later condition

    full counterbalancing versus partial counterbalancing/ Latin square design

    Full - a method of counterbalancing in which all possible condition orders are represented (2 or 3 levels and as the number of conditions increases, the number of possible orders need for full c increase)

    Partial - some of the possible condition orders are represented (one way is to randomized order for every subject)

    Latin Square design- system to ensure that every condition appears in each position atleast once.

    demand characteristics

    a cue that can lead participants to guess an experiment's hypothesis. It creates an alternative explanation for the study's results.

    Cohen's D ...

    Threats to internal validity of any design: observer/ experimenter bias, demand characteristics, and placebo effects

    observer bias - when researcher's expectations influence their interpretation of results.

    Its a threat to internal validity because another explanation and construct validity of the dependent variable.

    demand characteristics- problem when people guess what the study is supposed to be about and change their behavior in the expected direction.

    prevention: double blind study where nobody knows the groups or masked study where participants know, and observer dont.

    placebo effect- people experience change only because they believe they are receiving a valid treatment.

    ceiling effects

    all scores fall at the high end of their distribution aka independent groups score almost the same on a dependent variable

    floor effects

    all scores fall at the low end of their distribution

    measurement error

    a human or instrument factor that can inflate or deflate a person's true score on the dependent variable

    ex: a an whose 160 cm gets measured at 159 cute he slouches.

    use larger sample size to cancel out

    individual differences

    (a problem in ind. groups design)

    prevention: change the design to use a within groups design, add people (the more people you measure, the less impact any single person will have on the group's average. It reduces the influence of individual differneces within groups, thereby enhancing the study's ability to detect differences between groups.

    situation noise

    can cause variability within groups and obscure true group difference.

    quasi experiment

    researchers don't have full experimental control.

    nonequivalent control group design

    one treatment and one comparison group. participants are not randomly assigned to the 2 groups (ind group designs).

    nonequivalent control group pretest-posttest design

    Because they were not randomly assigned and tested both before and after (ind group design - diff ppl at each level of iV)

    nonequivalent control group interrupted time series design

    2 or more groups in which participants have not been randomly assigned. measured repeatedly on the DV before, during, and after the "interruption" caused by some event, and the precince or timing of the interrupting event differs among the groups.

    interrupted time series design

    measures people repeatedly on a dependent variable before, during, and after the "interruption" caused by some event (repeated measure design)

    स्रोत : quizlet.com

    Concept of Causality and Conditions for Causality

    The concept of causality has been debated over the centuries but remains one of the most valuable types of knowledge because it tells what can or should be done to obtain a desired consequence or to ...

    Wiley International Encyclopedia of Marketing

    Concept of Causality and Conditions for Causality

    Part 2. Marketing Research

    Harmen Oppewal

    First published: 15 December 2010

    https://doi.org/10.1002/9781444316568.wiem02059

    Citations: 4

    Abstract

    The concept of causality has been debated over the centuries but remains one of the most valuable types of knowledge because it tells what can or should be done to obtain a desired consequence or to avoid an undesirable outcome. Causality concerns relationships where a change in one variable necessarily results in a change in another variable. There are three conditions for causality: covariation, temporal precedence, and control for “third variables.” The latter comprise alternative explanations for the observed causal relationship. Spurious relationships occur when covariation between variables suggests a causal effect but where this covariation is, in fact, the result of an underlying shared cause.

    Citing Literature

    स्रोत : onlinelibrary.wiley.com

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    Mohammed 16 day ago
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