This article discusses measures of association, causality relationship and different possibilities in a causal relationship. The Bradford Hill criteria comprising nine principles is also being discussed. The possibilities of a causal relationship include four principles. The article also explains reliability, validity, and related characteristics. The major threats to reliability discussed in the article include poor sampling, instability and divergence.

rope causality, validity, reliability

Image: “rope” by JerzyGorecki. License: CC0 1.0

Measures of Association

It refers to a wide variety of co-efficients which are required to measure the statistical strength of different variables. There are many statistical distinctions associated for understanding of association between statistical measures. The statistical measures are different from statistical significance. Measures of association assume categorical or continuous level of data.

The categorical data include nominal or cordial data level, whereas the casual direction followed by the measure of association is based on symmetrical or asymmetrical direction.


Causality refers to any reason which leads to a specific disease in order to diagnose a disease timely and to take preventative or curative measures. A cause can be sufficient or necessary or both in order to create an effect leading to a specific disease. There can be more than one causal mechanism leading to a single disease.

The casualty relationship refers to the association between cause and its effect on an individual. There are several factors in causality such as

  • predisposing factors,
  • enabling factors,
  • precipitating factors and
  • reinforcing factors.

Bradford has defined a criteria to define a cause and effect relationship.

Bradford Hill Criteria

Austin Bradford Hill in 1965 introduced a certain criteria to provide evidence for the causal relationship between a presumed cause and an observed effect. This criteria is used in public health research processes. It is helpful in epidemic research process by addressing different areas involved. There are nine principles of Bradford Hill criteria given as follows:

1. Temporality


“Temporality” by Lecturio

It refers the time relationship between cause and effect. The effect which occurs due to a cause by the subject is addressed in context of time under this principle. The delay in cause and effect relationship is addressed under this category. It states that if there is a delay in effect related due to effect, it should have occurred after the delayed period.

2. Strength and association


“Strength” by Lecturio

It refers to the effect size created due to the epidemic cause. The size of an association impacts the intensity of effect. The cause and effect relationship is usually seen in context of statistical correlation between repeated events. The full strength correlation is denoted by 1. In case an association is weak, the cause and effect relationship will show higher variations and vice versa.

3. Biological gradient (dose-response)


“Biological Gradient” by Lecturio

In case a patient is given dose there is a relationship between the dose of medicine and the reaction of the patient caused by the dose. It does not indicate a simple linear relationship due to minimum and maximum thresholds. The higher the exposure the greater the effect of the cause. In some circumstances the mere presence of biological gradient can trigger a large effect.

4. Consistency


“Consistency” by Lecturio

In order to find out reproducibility for a research process, the consistency principle is mandatory to keep it going at a wider context. In order to prove usefulness of a treatment, the consistency principle contributes for making up its productivity in wide range of circumstances.

5. Plausibility


“Plausibility” by Lecturio

The cause and effect relationship should be sensible and logical in context of all related theories, concepts and results. In case the causal relationship between cause and effect of a subject indicate the occurrence of factors outside the science of research, it may create hindrance in accurate analysis of the casual relationship. It investigates the plausible mechanism employed by causal relationship between cause and effect.

6. Specificity


“Specificity” by Lecturio

In case there is no other plausible explanation, it explains the specificity of a population. In case there is a specific population of patients suffering from Asthma, in a town in California, there will be a specific association between the disease factor and its effect.

The more specific this association will be, the bigger the probability of existence of causal relationship. It is not always possible in medical research that the symptoms of a disease are caused by a wide range of causing conditions.

7. Evidence


“Evidence” by Lecturio

The experimental evidence give a strong proof for cause and effect relationship between a disease and the factors causing it. Several significant variables are held stable in order to prevent them from interfering in the experimental results.

8. Analogy


“Analogy” by Lecturio

This principle considers the effect of similar factors in order to create a logical relationship between suspected cause and its effect. The other related factors should create a logical sense with the research subject otherwise, they should be removed from the investigation process.

9. Coherence


“Coherence” by Lecturio

The likelihood of effect of a cause and its effect increases when there is coherence between epidemiological and laboratory findings of an experiment. Despite of the Coherence principle, Hill noted that in case the laboratory evidence is not available or insufficient it can not completely nullify the epidemiological effect on associations.

Example: Smoker/Cancer

Bradford Hill Criteria applied to Smokers/Cancer Example

“Bradford Hill Criteria applied to Smokers/Cancer Example” by Lecturio

Possibilities in a Causal Relationship

There are four conditions of a causal relationship which should be fulfilled in order to create an association. These conditions include

  1. Necessary and sufficient
  2. Necessary but not sufficient
  3. Sufficient but not necessary
  4. Neither sufficient or necessary

1. Necessary and sufficient

In case a condition is necessary for occurrence of dependent condition, because without it, there is no possibility of an occurrence of another condition. This condition should also be sufficient enough to cause and effect. In such a situation, both necessary and sufficiency requirements should be fulfilled.

Example: Corona Virus causes SARS disease, here the necessary condition for SARS disease is Corona virus.
Necessary and Sufficient

“Necessary and Sufficient” by Lecturio

2. Necessary but not sufficient

This is the condition where the existence of a situation is enough to cause a problem. In this situation, it is not necessary to measure the sufficiency of the condition for occurrence of related effect.

Example: In case a gene is activated by an environmental trigger like pollution or other harmful factor it can produce a disease.

In this case, it is not necessary that the trigger is sufficient, only its existence can cause the problem.

Necessary but not sufficient

“Necessary but not Sufficient” by Lecturio

3. Sufficient but not necessary

In this situation the sufficiency of a factor is necessary to create an effect.

Example: Both radiation and benzene poisoning can lead to Leukemia. In this situation both Leukemia and Benzene alone are sufficient to cause Leukemia disease but none of them are necessary for the calamity.
Sufficient but not necessary

“Sufficient but not Necessary” by Lecturio

4. Neither sufficient or necessary

In case none of the factors is mandatory for occurrence of a condition.

Example: Being tall is neither necessary nor sufficient for a person to become educated in life.

In case of epidemics the effect which is caused by any damaging factor requires no specific sufficient or necessary condition for occurrence of disease.

Neither Sufficient nor necessary

“Neither Sufficient nor Necessary” by Lecturio

Validity and Reliability



“Reliability” by Lecturio

It refers to the degree to which a method or tool is used to generate stable and consistent results. There are several types of reliability, such as:

Test-retest reliability: a test is taken twice to measure reliable results.

Parallel forms reliability: different versions of assessment tools are used to generate desirable results.

Inter rater reliability: different raters are approached to find out the accurate results of a research.

Internal consistency reliability: measures the degree to which different test samples generate the same result.



“Validity” by Lecturio

It refers to ability of a test measure to estimate a result which is desired to be measured. Reliability alone is not sufficient to evaluate the required results. For ensured reliability, a test should be valid.

Example: Suppose a weight scale is off by 5 lbs. On daily weight measurement, the weight scale shows the weight in access of 5 lbs. Now the scale measures the weight reliably but still it does not give a valid result.

1. Internal validity
Internal validity refers to zero generalizability concern. It shows that the researcher has the evidence that the measures taken to an investigation or research purpose have caused what has been observed in a study.

The major requirements of internal validity include temporality, strength, and plausibility.

Major threats include confounding and selection bias.

2. External validity
It is the degree to which the results of a study can be generalized at a large extent or for general population. The findings of a research and experiment are measured to be sufficient for a large population to conclude a specific or required result. The requirements of external validity include minimized observer effects, parsimonious exclusion criteria.

There are several threats to be countered by external validity including overly specific study characteristics, Hawthorne effect, and Rosenthal effect. External validity further has two types, i.e., population validity and Ecological validity.

1. Population validity
It refers to the extent to which the results of an experiment can be generalized to whole population. In case the sample population well represents the reference population, it is known as the population validity.

2. Ecological validity
In this case, the environment of the study has complete resemblance with the real world. It is the extent to which the conclusions of a research study match with the generalized findings in context of whole population.

Reliability and Validity

“Reliability and Validity” by Lecturio

Validity and Reliability are the philosophical cornerstones of what is accepted as scientific prove.

Threats to Reliability

The major threats to reliability include

Poor Sampling

In case the sample selected does not represent the whole population, it can lead to inappropriate and unreliable results of a research or experiment.

Example: The mean age of a non-random sample represents an inappropriate population.


It refers to the inconsistent characteristics of a subject which is evaluated for required results.

Example: Suppose blood pressure is measured multiple times a day, the blood pressure here is the instable variable which changes every time it is measured. It can lead to unreliable results.


In case raters or evaluators have divergence in their moods, it can lead to untrusted results.

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