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Related Concept Videos

Hypothesis Test for Test of Independence01:16

Hypothesis Test for Test of Independence

The test of independence is a chi-square-based test used to determine whether two variables or factors are independent or dependent. This hypothesis test is used to examine the independence of the variables. One can construct two qualitative survey questions or experiments based on the variables in a contingency table. The goal is to see if the two variables are unrelated (independent) or related (dependent). The null and alternative hypotheses for this test are:
H0: The two variables (factors)...
Introduction to Test of Independence01:21

Introduction to Test of Independence

In statistics, the term independence means that one can directly obtain the probability of any event involving both variables by multiplying their individual probabilities. Tests of independence are chi-square tests involving the use of a contingency table of observed (data) values.
The test statistic for a test of independence is similar to that of a goodness-of-fit test:
Determination of Expected Frequency01:08

Determination of Expected Frequency

Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
Impact of Groups on Individuals01:28

Impact of Groups on Individuals

Groups play a fundamental role in shaping individual behavior, as they establish norms that guide interactions and decision-making. Social psychology examines how individuals conform to group expectations, often adjusting their attitudes and actions to align with group norms. These norms can be formal, such as workplace policies, or informal, such as unspoken social expectations within a fraternity.Conformity and Social InfluenceConformity arises when individuals modify their behaviors or...
In- and Out-Groups01:31

In- and Out-Groups

People all belong to a gender, race, age, and social economic group. These groups provide a powerful source of our identity and self-esteem (Tajfel & Turner, 1979) and serve as our in-groups. An in-group is a group that we identify with or see ourselves as belonging to.
Surveys02:16

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.

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Related Experiment Video

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Using a Virtual Reality Walking Simulator to Investigate Pedestrian Behavior
06:38

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Inter-group differences in road-traffic crash involvement.

Roni Factor1, David Mahalel, Gad Yair

  • 1Faculty of Civil and Environmental Engineering, Transportation Research Institute, Technion-Israel Institute of Technology, Haifa 32000, Israel. ronif@tx.technion.ac.il

Accident; Analysis and Prevention
|December 11, 2008
PubMed
Summary

Road traffic accidents show significant group disparities. Males, non-Jewish drivers, and those from Africa/Asia face higher risks, while higher education/socioeconomic status correlates with lower accident probability.

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Area of Science:

  • Road safety research
  • Transportation safety
  • Public health

Background:

  • Road traffic accidents (RTAs) pose a significant public health challenge globally.
  • Understanding demographic and socioeconomic disparities in RTA involvement is crucial for targeted interventions.

Purpose of the Study:

  • To investigate socioeconomic and demographic group differences in severe and fatal road-traffic accidents.
  • To analyze the probability of accident involvement based on various driver characteristics.

Main Methods:

  • Utilized a unique database merging 9 years of Israeli road-traffic records with census data.
  • Included 20% of the Israeli driving population, controlling for daily distance traveled and license type.
  • Examined socioeconomic status, education, gender, ethnicity, and geographic origin.

Main Results:

  • Significant group differences in severe and fatal RTA probabilities were identified.
  • Males, non-Jewish drivers, and those of African/Asian origin showed higher accident involvement probabilities.
  • Higher education and socioeconomic status were associated with lower accident involvement probabilities.

Conclusions:

  • Socioeconomic and demographic factors significantly influence road-traffic accident risk.
  • Findings support the development of targeted road-safety programs addressing specific vulnerable groups.
  • Further research is recommended to refine understanding and prevention strategies.