ct.

(やや古い)(人・事・物)を(間違って)(～と)混同する(confuse)(with,and)
(物・事が)(人)を当惑させる(puzzle)。(be ～ed)(物・事に/～ということに)まごつく、うろたえる、泡を食う(at,by,that節)
～を打ち負かす。～を妨げる
～に反駁する、反論させる
～に恥をかかせる、赤面させる
～を悪化させる
(予想などに)反する

関: confuse、confusion、derange、derangement、disarray、disorganized、disorient、disrupt、disruption、embarrass、embarrassment、perplexity、perturbation、upset

WordNet

be uncertain about; think about without fully understanding or being able to decide; "We puzzled over her sudden departure"
a game that tests your ingenuity
a particularly baffling problem that is said to have a correct solution; "he loved to solve chessmate puzzles"; "thats a real puzzler" (同)puzzler, mystifier, teaser
used of an unexpected defeat of a team favored to win; "the Bills upset victory over the Houston Oilers"
the act of disturbing the mind or body; "his carelessness could have caused an ecological upset"; "she was unprepared for this sudden overthrow of their normal way of living" (同)derangement, overthrow
the act of upsetting something; "he was badly bruised by the upset of his sled at a high speed" (同)overturn, turnover
a tool used to thicken or spread metal (the end of a bar or a rivet etc.) by forging or hammering or swaging (同)swage
cause to lose ones composure (同)discompose, untune, disconcert, discomfit
defeat suddenly and unexpectedly; "The foreign team upset the local team"
disturb the balance or stability of; "The hostile talks upset the peaceful relations between the two countries"
mildly physically distressed; "an upset stomach"
be confusing or perplexing to; cause to be unable to think clearly; "These questions confuse even the experts"; "This question completely threw me"; "This question befuddled even the teacher" (同)throw, fox, befuddle, fuddle, bedevil, confound, discombobulate
make unclear, indistinct, or blurred; "Her remarks confused the debate"; "Their words obnubilate their intentions" (同)blur, obscure, obnubilate
mistake one thing for another; "you are confusing me with the other candidate"; "I mistook her for the secretary" (同)confound
cause to feel embarrassment; "The constant attention of the young man confused her" (同)flurry, disconcert, put off
(physics) a secondary influence on a system that causes it to deviate slightly
a disposition that is confused or nervous and upset (同)fluster
activity that is a malfunction, intrusion, or interruption; "the term `distress connotes some degree of perturbation and emotional upset"; "he looked around for the source of the disturbance"; "there was a disturbance of neural function" (同)disturbance
cause to be embarrassed; cause to feel self-conscious (同)abash
throw into disorder; "This event disrupted the orderly process"
that confounds or contradicts or confuses (同)contradictory

PrepTutorEJDIC

〈情況など〉'を'『混乱させる』,〈人〉'を'困惑させる,面くらわせる / (…と)…'を'『混同する』《+『名』+『with』+『名』》 / 〈計画・希望・敵など〉'を'破る,くじく(defeat) / 《遠回しに腹立たしさなどを表して》〈神〉が…'を'地獄に落とす
《単数形で》(頭を)『悩ませる物』(『事』,『人』),難問,難物 / 《しばしば複合語で用いて》(遊び用の)『パズル』,判じ物,なぞ / 〈難問が〉〈人〉‘の'頭を悩ませる;(…について)〈頭〉をひねって考える《+『名』+『about』(『over』)+『名』》 / (…に)頭を悩ませる,(…を)頭をひねって考える《+『about』(『as to, over』)+『名』》
『…‘を'ひっくり返す』,転覆させる / 〈計画など〉‘を'『だめにする』,だいなしにする,狂わす / 〈人〉‘の'『心お乱す』,'を'ろうばいさせる / 〈体など〉‘の'調子お狂わせる / 《米》(競技などで)〈相手〉'を'思いがけなく負かす / ひっくり返った / (計画などが)だいなしの,狂った / 《捕語にのみ用いて》(精神的に)混乱した,ろうばいした / (体などの)調子がおかしくなった / 〈U〉〈C〉『転倒』,転落 / 〈C〉(計画.心などの)『混乱』,乱れ;(体の)不調 / 〈C〉《話》けんか / 〈C〉《米》(競技などでの)番狂わせ
(…と)'を'『混同する』,取り違える《+『名』+『with』+『名』》 / 〈人〉'を'『とまどわせる』,当惑させる / 〈論点・情況・順序など〉'を'混乱させる
〈U〉動揺,不安;混乱 / 〈C〉不安(混乱)のもと
〈人〉‘の'『まごつかせる』,困惑させる,きまり悪がらせる / …‘を'『じゃまする』,妨げる / 〈人・会社など〉‘を'『財政困難にする』
…‘を'混乱させる / (一時的に)…‘を'中断させる
混乱した,まごついた / 《名詞の前にのみ用いて》《遠回しに》ひどい,いまいましい

Wikipedia preview

出典(authority):フリー百科事典『ウィキペディア（Wikipedia）』「2013/04/05 18:47:25」(JST)

wiki en

[Wiki en表示]

"Confounding factor" redirects here. For other uses, see Confounding factor (disambiguation).

In statistics, a confounding variable (also confounding factor, hidden variable, lurking variable, a confound, or confounder) is an extraneous variable in a statistical model that correlates (positively or negatively) with both the dependent variable and the independent variable. A perceived relationship between an independent variable and a dependent variable that has been misestimated due to the failure to account for a confounding factor is termed a spurious relationship, and the presence of misestimation for this reason is termed omitted-variable bias. In the case of risk assessments evaluating the magnitude and nature of risk to human health, it is important to control for confounding to isolate the effect of a particular hazard such as a food additive, pesticide, or new drug. For prospective studies, it is difficult to recruit and screen for volunteers with the same background (age, diet, education, geography, etc.), and in historical studies, there can be similar variability. Due to the inability to control for variability of volunteers and human studies, confounding is a particular challenge. For these reasons, experiments offer a way to avoid most forms of confounding.

As an example, suppose that there is a statistical relationship between ice cream consumption and number of drowning deaths for a given period. These two variables have a positive correlation with each other. An evaluator might attempt to explain this correlation by inferring a causal relationship between the two variables (either that ice cream causes drowning, or that drowning causes ice cream consumption). However, a more likely explanation is that the relationship between ice cream consumption and drowning is spurious and that a third, confounding, variable (the season) influences both variables: during the summer, warmer temperatures lead to increased ice cream consumption as well as more people swimming and thus more drowning deaths.

Types of confounding

In some disciplines, confounding is categorized into different types. In epidemiology, one type is "confounding by indication,"^[1] which relates to confounding from observational studies. Because prognostic factors may influence treatment decisions (and bias estimates of treatment effects) Controlling for known prognostic factors may reduce this problem, but it is always possible that a forgotten or unknown factor was not included or that factors interact complexly. Confounding by indication has been described as the most important limitation of observational studies. Randomized trials are not affected by confounding by indication due to random assignment.

Confounding variables may also be categorised according to their source. The choice of measurement instrument (operational confound), situational characteristics (procedural confound), or inter-individual differences (person confound).

An operational confound can occur in both experimental and nonexperimental research designs. This type of confound occurs when a measure designed to assess a particular construct inadvertently measures something else as well.^[2]
A procedural confound can occur in a laboratory experiment or a quasi-experiment. This type of confound occurs when the researcher mistakenly allows another variable to change along with the manipulated independent variable.^[2]
A person confound occurs when two or more groups of units are analyzed together (e.g., workers from different occupations), despite varying according to one or more other (observed or unobserved) characteristics (e.g., gender).^[3]

Examples

In risk assessments, factors such as age, gender, and educational levels often have impact on health status and so should be controlled. Beyond these factors, researchers may not consider or have access to data on other causal factors. An example is on the study of smoking tobacco on human health. Smoking, drinking alcohol, and diet are lifestyle activities that are related. A risk assessment that looks at the effects of smoking but does not control for alcohol consumption or diet may overestimate the risk of smoking.^[4] Smoking and confounding are reviewed in occupational risk assessments such as the safety of coal mining.^[5] When there is not a large sample population of non-smokers or non-drinkers in a particular occupation, the risk assessment may be biased towards finding a negative effect on health.

Decreasing the potential for confounding to occur

A reduction in the potential for the occurrence and effect of confounding factors can be obtained by increasing the types and numbers of comparisons performed in an analysis.^{[citation needed]} If a relationship holds among different subgroups of analyzed units, confounding may be less likely. That said, if measures or manipulations of core constructs are confounded (i.e., operational or procedural confounds exist), subgroup analysis may not reveal problems in the analysis. Additionally, increasing the number of comparisons can create other problems (see multiple comparisons).

Peer review is a process that can assist in reducing instances of confounding, either before study implementation or after analysis has occurred. Peer review relies on collective expertise within a discipline to identify potential weaknesses in study design and analysis, including ways in which results may depend on confounding. Similarly, replication can test for the robustness of findings from one study under alternative study conditions or alternative analyses (e.g., controlling for potential confounds not identified in the initial study).

Confounding effects may be less likely to occur and act similarly at multiple times and locations.^{[citation needed]} In selecting study sites, the environment can be characterized in detail at the study sites to ensure sites are ecologically similar and therefore less likely to have confounding variables. Lastly, the relationship between the environmental variables that possibly confound the analysis and the measured parameters can be studied. The information pertaining to environmental variables can then be used in site-specific models to identify residual variance that may be due to real effects.^[6]

Depending on the type of study design in place, there are various ways to modify that design to actively exclude or control confounding variables:^[7]

Case-control studies assign confounders to both groups, cases and controls, equally. For example if somebody wanted to study the cause of myocardial infarct and thinks that the age is a probable confounding variable, each 67 years old infarct patient will be matched with a healthy 67 year old "control" person. In case-control studies, matched variables most often are the age and sex. Drawback: Case-control studies are feasible only when it is easy to find controls, i.e., persons whose status vis-à-vis all known potential confounding factors is the same as that of the case's patient: Suppose a case-control study attempts to find the cause of a given disease in a person who is 1) 45 years old, 2) African-American, 3) from Alaska, 4) an avid football player, 5) vegetarian, and 6) working in education. A theoretically perfect control would be a person who, in addition to not having the disease being investigated, matches all these characteristics and has no diseases that the patient does not also have — but finding such a control would be an enormous task.
Cohort studies: A degree of matching is also possible and it is often done by only admitting certain age groups or a certain sex into the study population, creating a cohort of people who share similar characteristics and thus all cohorts are comparable in regard to the possible confounding variable. For example, if age and sex are thought to be confounders, only 40 to 50 years old males would be involved in a cohort study that would assess the myocardial infarct risk in cohorts that either are physically active or inactive. Drawback: In cohort studies, the overexclusion of input data may lead researchers to define too narrowly the set of similarly situated persons for whom they claim the study to be useful, such that other persons to whom the causal relationship does in fact apply may lose the opportunity to benefit from the study's recommendations. Similarly, "over-stratification" of input data within a study may reduce the sample size in a given stratum to the point where generalizations drawn by observing the members of that stratum alone are not statistically significant.
Double blinding: conceals from the trial population and the observers the experiment group membership of the participants. By preventing the participants from knowing if they are receiving treatment or not, the placebo effect should be the same for the control and treatment groups. By preventing the observers from knowing of their membership, there should be no bias from researchers treating the groups differently or from interpreting the outcomes differently.
Randomized controlled trial: A method where the study population is divided randomly in order to mitigate the chances of self-selection by participants or bias by the study designers. Before the experiment begins, the testers will assign the members of the participant pool to their groups (control, intervention, parallel), using a randomization process such as the use of a random number generator. For example, in a study on the effects of exercise, the conclusions would be less valid if participants were given a choice if they wanted to belong to the control group which would not exercise or the intervention group which would be willing to take part in an exercise program. The study would then capture other variables besides exercise, such as pre-experiment health levels and motivation to adopt healthy activities. From the observer’s side, the experimenter may choose candidates who are more likely to show the results the study wants to see or may interpret subjective results (more energetic, positive attitude) in a way favorable to their desires.
Stratification: As in the example above, physical activity is thought to be a behaviour that protects from myocardial infarct; and age is assumed to be a possible confounder. The data sampled is then stratified by age group – this means, the association between activity and infarct would be analyzed per each age group. If the different age groups (or age strata) yield much different risk ratios, age must be viewed as a confounding variable. There exist statistical tools, among them Mantel–Haenszel methods, that account for stratification of data sets.

Controlling for confounding by measuring the known confounders and including them as covariates in multivariate analyses such as regression analysis; however, multivariate analyses reveal much less information about the strength of the confounding variable than do stratification methods.^{[dubious – discuss]}

All these methods have their drawbacks:

The best available defense against the possibility of spurious results due to confounding is often to dispense with efforts at stratification and instead conduct a randomized study of a sufficiently large sample taken as a whole, such that all potential confounding variables (known and unknown) will be distributed by chance across all study groups and hence will be uncorrelated with the binary variable for inclusion/exclusion in any group.
Ethical considerations: In double blind and randomized controlled trials, participants are not aware that they are recipients of sham treatments and may be denied effective treatments.^[8] There is resistance to randomized controlled trials in surgery because patients would agree to invasive surgery which carry risks under the understanding that they were receiving treatment.

References

^ Johnston SC. Identifying Confounding by Indication through Blinded Prospective Review. Am J Epidemiol 2001;154:276–84
^ ^a ^b Pelham, Brett (2006). Conducting Research in Psychology. Belmont: Wadsworth Publishing. ISBN 0-534-53294-2.
^ Steg, L., Buunk, A.P. & Rothengatter, T. (2008). Applied Social Psychology: Understanding and managing social problems. Cambridge, UK: Cambridge University Press, Ch. 4.
^ Tjønneland, Anne; Morten Grønbæk, Connie Stripp and Kim Overvad (January 1999). American Society for Nutrition American Journal of Clinical Nutrition 69 (1): 49–54.
^ Axelson, O (1989). "Confounding from smoking in occupational epidemiology". British Journal of Industrial Medicine 46: 505–07.
^ Calow, Peter P. (2009) Handbook of Environmental Risk Assessment and Management, Wiley
^ Mayrent, Sherry L (1987). Epidemiology in Medicine. Lippincott Williams & Wilkins. ISBN 0-316-35636-0.
^ Emanuel, Ezekiel J; Miller, Franklin G (Sep 20, 2001). "he ethics of placebo-controlled trials--a middle ground". The New England Journal of Medicine 345 (12): 915–9.

External links

These sites contain descriptions or examples of confounding variables:

Linear Regression (Yale University)
Scatterplots (Simon Fraser University)
Tutorial by University of New England

UpToDate Contents

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Related Pictures

★リンクテーブル★

リンク元	「embarrass」「disrupt」「perturbation」「upset」「derange」
拡張検索	「confounding factor」「confounder」「confounding variable」
関連記事	「confounding」

「embarrass」

　　[★]

em-(中に) + -barass(障害物、横木=bar) = 中に障害物を置く

vt.

(人)を恥ずかしがらせる/当惑させる。(人)を辱めて(～)させる(shame)(into doing)。(be ～ed)(人が)(人前で)恥ずかしい思いをする、(～で)まごつく(at, with, by, about, that節)
(正式)(be ～ed)(人が)財政困難になる、借金を負う
(問題など)を複雑にする、こじらせる。(政府・行動など)を妨害する
(消化・肺など)の機能を損なう、～に障害を来す。