Related Experiment Video
Updated: Aug 10, 2026

10:46
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
Published on: December 9, 2015
Validity and reliability of trends in suicide statistics
Summary
Official suicide statistics, while subject to under-reporting, remain valuable for epidemiological research. Studies indicate reporting errors are random, allowing valid comparisons across groups and time.
Area of Science:
- Epidemiology
- Public Health
- Mortality Statistics
Background:
- Official suicide rates are questioned due to under-reporting and definitional variations.
- Inaccuracies in mortality data can affect epidemiological research reliability.
- Despite potential errors, suicide statistics are crucial for public health surveillance.
Purpose of the Study:
- To assess the reliability and heuristic value of official suicide statistics in epidemiological research.
- To investigate the extent and nature of under-reporting in suicide data.
- To compare suicide rates across different cultural, social, and demographic groups.
Main Methods:
- Analysis of official suicide statistics from various countries, including England and Wales, Scotland, and Ireland.
- Examination of coroner's inquest procedures and post-mortem investigations for suicide ascertainment.
- Review of studies investigating under-reporting through methods like clinical assessment of case records.
Main Results:
- Evidence suggests under-reporting of suicide is a significant issue, though errors are largely random.
- Official suicide statistics, despite inaccuracies, retain heuristic value for epidemiological comparisons.
- Adjusted rates in some studies, like Ireland, show considerable increases but remain low internationally.
Conclusions:
- Official suicide statistics are useful for epidemiological research due to random error patterns.
- Under-reporting is a key concern, but over-reporting is negligible.
- Comparisons of suicide rates between countries and demographic groups remain feasible and informative.
Related Concept Videos
Surveys
Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally. Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.
Longitudinal Research
Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
Reliability and Validity
Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways.
Regression Toward the Mean
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...
Actuarial Approach
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Assumptions of Survival Analysis
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
