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Topic: Spurious relationship

	Encyclopedia: Spurious relationship
		In statistics, a spurious relationship (or, sometimes, spurious correlation) is a mathematical relationship in which two occurrences have no logical connection, yet it may be implied that they do, due to a certain third, unseen factor (referred to as a "confounding factor" or "lurking variable").
		An example of a spurious relationship can be delineated by a city's ice cream sales.
		Because of this, experimentally identified correlations do not represent causal relationships unless spurious relationships can be ruled out.
	www.nationmaster.com /encyclopedia/Spurious-relationship (189 words)

	Elaborating Program Impacts Through Data Analysis
		When an antecedent contextual variable, education, is used to stratify the association, the relationship between participation and adoption disappears for each level of education (Table 1b).
		The initial relationship between program participation and adoption of practice Y is due solely to the "marginal" relationship of education with both of the variables.
		Spurious relationships are perhaps the greatest threat to the validity of evaluation studies.
	edis.ifas.ufl.edu /PD003 (1561 words)

	Encyclopedia topic: Spurious relationship (Site not responding. Last check: 2007-11-07)
		An example of a spurious relationship can be illuminated examining a city's ice cream (Frozen dessert containing cream and sugar and flavoring) sales.
		The term is commonly used in statistics (A branch of applied mathematics concerned with the collection and interpretation of quantitative data and the use of probability theory to estimate population parameters) and in particular in experimental research (additional info and facts about experimental research) techniques.
		Because of this, experimentally identified correlations (A statistical relation between two or more variables such that systematic changes in the value of one variable are accompanied by systematic changes in the other) do not represent causal relationships (additional info and facts about causal relationships) unless spurious relationships can be ruled out.
	www.absoluteastronomy.com /encyclopedia/s/sp/spurious_relationship.htm (422 words)

	Spurious relationship (Site not responding. Last check: 2007-11-07)
		In statistics, a spurious relationship (or, sometimes,spurious correlation) is a mathematical relationship in which two occurrences have no logical connection, yet it may beimplied that they do, due to a certain third, unseen factor (referred to as a "lurking variable").
		The spurious relationshipgives an impression of a worthy link between two groups that is invalid when objectively examined.
		Because ofthis, it is safest to present the conclusions of experimental research in terms of correlation instead of causation.
	www.therfcc.org /spurious-relationship-22030.html (217 words)

	C (Site not responding. Last check: 2007-11-07)
		Now we have two causal relationships, one between A and B, and a second one between B and C. In a sense, there is also a third one between A and C, but it is mediated by variable B. In this case, variable B is called an intervening variable.
		This is when a relationship only exists for some values of a variable so that the relationship holds or changes for different combinations of the two variables.
		Therefore, a spurious relationship is an untrue relationship.
	www.usca.edu /polisci/apls301/relationships.htm (2476 words)

	Lab 7 - Elaboration
		This means that the original relationship between gender and income is actually a manifestation of a relationship between education and income.
		In the case of a spurious relationship we must also determine whether the control variable is antecedent to the two variables or intervening between them.
		Determine whether the elaborated relationship is an example of replication, specification, or a spurious relationship.
	www.chass.utoronto.ca /~josephf/pol242/LM-Elaboration.htm (1560 words)

	Untitled
		Spurious: Often two variables will show a relationship, but the relationship is not between the two variables.
		A spurious relationship is indicated if the original strength of the measured relationship between the two variables is substantially reduced when controlling for the third.
		Suppression: As in spurious relationships, a third variable is affecting the strength of relationship.
	www.geosoc.org /schools/adult/stats/8.html (323 words)

	Concept: Spurious Relationship (Site not responding. Last check: 2007-11-07)
		spurious relaltionship - a relationship that appears to exist at face value, but that disappears when you control for another variable.
		The old joke to help you remember: Once a group of students decided to study empirically the causes of drunkenness.
		The conclusion that water makes you drunk is spurious.
	www.csudh.edu /dearhabermas/cnspurious.htm (111 words)

	[No title]
		Replication occurs whenever the partial relationships are essentially the same as the original relationship (regardless of whether the test factor is antecedent or intervening).
		Explanation is the term used to describe a spurious relationship: an original relationship that is explained through the introduction of a test variable.
		For example, one partial relationship is the same as or stronger than the original 2- variable relationship and the second partial relationship is less than the original and may be reduced to zero.
	darkwing.uoregon.edu /~mtusler/lecture11.html (612 words)

	socanalysishealey16
		spurious relationship A multivariate relationship in which a bivariate relationship becomes substantially weaker after a third variable is controlled for.
		Direct relationships - relationships between X and Y is the same in all partial tables and in the bivariate table.
		Spurious relationships and intervening relationships - relationship between X and Y is the same in all partial tables but much weaker than in the bivariate table.
	www-personal.ksu.edu /~lswilli/socanalysishealey16.html (313 words)

	Spurious relationship (Site not responding. Last check: 2007-11-07)
		In statistics, a spurious relationship (or, sometimes, spurious correlation) is a mathematical relationship in which two occurrences have no logical connection, yet it may be implied that they do, due to a certain third, unseen factor (referred to as a "lurking variable").
		To allege that ice cream sales cause crime would be to imply a spurious relationship between the two.
		Because of this, it is safest to present the conclusions of experimental research in terms of correlation instead of causation.
	www.sciencedaily.com /encyclopedia/spurious_relationship (274 words)

	FEEDBACK FOR ASSIGNMENT 5 (EXERCISE 5a) (Site not responding. Last check: 2007-11-07)
		Note that, in order to assert that the relationship is spurious, it is necessary to show that the relationship disappears for all levels (all values) of the control variable – so, in this case, it was necessary to show that there was no relationship between gun ownership and religious attendance for both males and females.
		If the relationship between gun ownership and religious attendance was significant for either males or females (or both), then we would not be able to conclude that the relationship was spurious.
		This shows that the relationship still exists (at least for some levels of education) even when we control for level of education, so we cannot conclude that the relationship is spurious.
	www.soc.ucsb.edu /faculty/straits/courses/soc108/s2005/feed5.html (566 words)

	definitions
		For example, in a leisure services marketing study, the effects of age, income, and education could be analyzed simultaneously to determine which of the variables would better predict (or explain the largest amount of variation in) nonuse of a program.
		A widely used statistic for measuring the relationship between two sets of scores that are assumed to be continuously distributed.
		spurious relationship: The condition where a third variable precedes, and is seen as a cause of, the independent and the dependent variables.
	www.prm.nau.edu /prm447/definitions.htm (3094 words)

	[No title]
		Ans: it was spurious Ans: it was suppressed.
		This is evidence of Ans: a spurious relationship.
		Ans: the variable that does not destroy the zero-order relationship Ans: the variable that is the most theoretically meaningful in terms of its connection with X and Y Ans: the variable that essentially replicates the zero-order relationship since we already know such a relationship exists.
	instruct.uwo.ca /sociology/501/tut3004.htm (773 words)

	POWERMUTT: Control Variables
		The fact that two variables in a table are related does not necessarily mean that one is a cause of the other, even if the relationship is statistically significant and we are willing to reject the notion that the relationship is due to chance.
		spurious, that is, the independent and dependent variables may be related, not because either one affects the other, but because both are dependent on some third variable.
		Though the relationship is a little weaker in the Northeast, the overall pattern is basically “replicated” within each region of the county.
	www.csupomona.edu /~jlkorey/POWERMUTT/Topics/control_variables.html (1786 words)

	Mixer 2x2 Spurious Response and IP2 Relationship - Maxim/Dallas
		Ideally, the mixer output signal amplitude and phase are proportional to the input signal's amplitude and phase and independent of the LO signal characteristics.
		(Note this is in contrast to a multiplier where the input amplitude and phase relationship is not preserved at the device output.) Using this assumption, the amplitude response of the mixer is linear for the RF input and is independent of the LO input.
		Spurious responses that appear within the IF band will not be attenuated by the IF filter.
	www.maxim-ic.com /appnotes.cfm/appnote_number/1838 (1617 words)

	Controls (Site not responding. Last check: 2007-11-07)
		A spurious relationship is an apparent yet false or misleading relationship that is caused by a third variable that is related to both the independent and dependent variable.
		In that case we conclude that the original relationship was spurious, that it was only the result of the effect of the control variables.
		If you had to do a test other than crosstabs for the bivariate relationship because the independent and dependent variables were ordinal with many categories or interval variables, then you simple redo whatever tests you did (scatterplot with regression or analysis of variance in most cases) for each value of the control variable.
	www.usca.edu /polisci/apls301/Controls.htm (1527 words)

	Talk:Correlation - Wikipedia, the free encyclopedia
		I have now partially addressed the concerns above by putting in a link to spurious relationship, which treats the "correlation does not imply causation" cliche.
		By definition a random variable has no correlation with anything else (if it does have a correlation the variable is either 1) not random, or 2) the correlation is a coincidence likely due to a small sample size).
		It is more accurate to think of these not as random variables, but simply as variables that have an undetermined relationship.
	www.wikipedia.org /wiki/Talk:Correlation (805 words)

	HKUST Institutional Repository: Item 1783.1/579
		The results support the view that the relationship between insider ownership and corporate performance should be a firm-specific one.
		Using linear mixed effects model, the paper finds that the quadratic relationship is spurious if we allow for heterogeneity in the coefficient on insider ownership.
		The findings are consistent with the views that firms adjust to their target insider ownership (corporate payouts) gradually over time due to the presence of adjustment costs and that to ignore such lagged effects may produce a spurious relationship between the two variables and these firm characteristics.
	hdl.handle.net /1783.1/579 (386 words)

	CAUSALITY & ind & dep vars
		An empirically existing relationship between the variables X and Y is said to be spurious if a third variable Z can be found such that after controlling for Z (i.e., holding Z constant), the relationship between X and Y disappears, changes sign, or otherwise changes its character.
		Hypothesis that is spurious: "Black people vote less often than whites." This is spurious because it implies that there is something inherent in "being fl" that simply causes people not to vote--it does not take into factors such as how SES affects voting habits.
		The initial, positive relationship between "# of firefighters" and "amount of fire damage" was shown to be spurious, because when we controlled for the size of the fire (that is, holding the size of the fire constant), then "# of firefighters" became negatively correlated with "amount of fire damage".
	www.d.umn.edu /~schilton/2700/2700.Causality.html (5485 words)

	Spurious Correlations
		The purpose of this paper is to illustrate the widespread occurrence of spurious correlations.
		"(b) A spurious correlation, as defined in definition a, is sometimes called an "illusory correlation." In that case, "spurious" is then reserved for the special case in which a correlation is not present in the original observations but is produced by the way the data are handled.
		As an instance of the nonsense or spurious correlation that is a real statistical fact, someone has gleefully pointed to this: There is a close relationship between the salaries of Presbyterian ministers in Massachusetts and the price of rum in Havana.
	www.burns.com /wcbspurcorl.htm (2910 words)

	Relationship between Christian Religion and anti-Semitism
		As to the 'whether' question, the relationship between Christian religion and religious and secular anti-Semitism appears to be both inherent to Christian religion and spurious, but not suppressed.
		It turned out that 39 per cent of the relationship between Christian beliefs and religious anti-Semitism is caused by religious factors, and 52 per cent by the breadth of the perspective that people have on social reality.
		However, as to the relationship between Christian beliefs and secular anti-Semitism, only 13 per cent is caused by religious factors, which is little, compared to the 70 per cent that is caused by people's breadth of perspective.
	baserv.uci.kun.nl /~rkonig/christian_religion_antisemitism (8008 words)

	Deltoid » 1995 » March (Site not responding. Last check: 2007-11-07)
		So Bourda found what he considered a spurious relationship and you trust his work enough to believe that the relationship existed, but you don’t believe the relationship was spurious.
		Since only 11.5% of gun murderers were female, it is unreasonable to expect female ownership to cause gun murders, so Bordua concludes that gun murders cause female gun ownership, and that the relationship between male ownership and gun murders was spurious, engendered by the correlation between male and female ownership.
		However, it might actually be the case that only, say, 5% of the control households had a significantly violent (or criminal, or whatever) person in the household, whereas say 45% of the households in which a murder took place might have had such a person.
	timlambert.org /1995/03 (1913 words)

	M/C Ch. 11
		She decides that a potentially extraneous variable in the relationship is IQ.
		In developing her groups for her study, she pairs each child who was an early reader with a child of the same IQ level who was not an early reader.
		Observing a relationship between two variables is NOT sufficient grounds for concluding that the relationship is a causal relationship.
	www.southalabama.edu /coe/bset/johnson/dr_johnson/mcq/mc11.htm (810 words)

	Spurious & Suppressor Relationships (Site not responding. Last check: 2007-11-07)
		Mistakes in theory induction and deduction can be summarized as two types of false relationships between variables: spurious relationships and suppressor relationships.
		Sapp did not realize from his limited data was that a third variable--size of fire--caused variation in both of the events he recorded: number of fire trucks dispatched and amount of fire damage.
		The false indication of causality is the relationship between number of fire trucks dispatched and amount of fire damage.
	www.soc.iastate.edu /soc130Sec1/SSRelationships.html (583 words)

	Relational Research Handout
		For example, to address the research question "Is there a relationship between the amount of violent behavior children show and the amount of violent television they watch?" you may conduct a relational study in which you measure each of these variables and then use statistics to see if they are related.
		In this example, we might want assume that there is a causal relationship between behavior and TV, but (1) we don't know the direction of the relationship, and (2) we don't know if the relationship is spurious.
		A spurious relationship between variables occurs when they are related only because both are related to a third variable.
	otel.uis.edu /yoder/rel_h.htm (753 words)

	Guide 4: Bivariate Basics
		One possibility is that the variables are locked in a symmetric relationship and we cannot tease out which variable is the cause and which variable is the effect.
		If there really were NO relationship in the population, then the observed sample results would occur by chance in less than 5 in 100 independent samples of the same size and the same type (taken at about the same time).
		Relationship strength is the second question we must answer about the relationship between two variables.
	edf5400-01.sp02.fsu.edu /Guide4.html (6887 words)

	Talk:Spurious relationship - Wikipedia, the free encyclopedia
		Guys, don't you know in America the word "relationship" means something else?
		Doubtless, a relationship based only on ice cream is spurious, and hence the article was a tautology.
		What is the difference between spurious variables and antecedent variables?
	en.wikipedia.org /wiki/Talk:Spurious_relationship (234 words)

	Untitled Document
		This is called a direct relationship between education level and parenting style.
		Notice that for both spurious and intervening relationships, controlling for a third variable makes the original relationship disappear (or at least get weaker).
		Where the relationship disappears or even reverses direction among one category of the control variable, we have an interactive relationship.
	www.d.umn.edu /~bmork/2001/Outline/Elaboration3151.htm (775 words)

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