how to make bio index
Answers
Answered by
0
The bio-index model, developed by PollyVote team members Scott Armstrong and Andreas Graefe, predicts U.S. presidential election winners based on information about candidates’ biographies. The model uses 58 biographical cues that are expected to influence the chances of a candidate on being elected. The bio-index is useful to political decision-makers, as it can provide advice on questions such as whether a candidate should run for office or which candidate a party should nominate.
Variables
A large stream of research, particularly in psychology, analyzes questions such as what makes people emerge as leaders? For example, meta-analyses found intelligence and height to have a positive impact on both leader performance and leader emergence. Such findings from prior research were used to identify and code the majority of variables in the bio-index. In addition, some variables are based on common sense. For example, it was assumed that voters are more attracted to candidates who are married but not divorced. As shown in Table 1 at the end of this page, the model distinguishes two types of variables:
Yes / No variables (n=47): For this type of variable, candidates are assigned a score of 1 if they possess a certain attribute and 0 otherwise.
Comparative variables(n=11): For this type of variable, the candidates of the two major parties are compared on the underlying attribute. The candidate who scores better than his opponent is assigned a score of 1 and 0 otherwise.
Forecast calculation
The model is based on the index method, which is useful in situations with many variables and good prior knowledge about the variables’ directional effect on the target criterion.
Predicting the election winner
After all variables have been coded, the total index scores for each candidate are calculated by summing up the scores using equal weights. Then, the candidate who achieves the higher overall score is predicted as the election winner.
Predicting vote-shares
Simple linear regression is then used to relate the incumbent party candidate’s relative bio-index score (bio) to the dependent variable, which is the actual two-party popular vote share received by the candidate of the incumbent party (V). Using data from the 30 elections from 1896 to 2012 leads to the following vote equation:
V = 19.9 + 60.5 * bio,
where bio = bio-score (incumbent) / [bio-score (incumbent) + bio-score (challenger)]
That is, the big-issue model predicts that an incumbent would start with 19.9% of the vote, plus a share depending on bio. If the incumbent’s relative bio score went up by 10 percentage points, the incumbent’s vote share would go up by 6 percentage points.
2016 forecast
The table above shows the coding for Hillary Clinton and Donald Trump. For some comparative variables, such as intelligence, data are not available. Other comparative variables, such as weight or height, are not coded due to the fact that the Clinton-Trump race is an inter-gender comparison.
Based on the coding, Clinton achieves a bio-index score of 18 points (compared to 11 points for Trump) and is thus predicted to win the election. Inserting these figures into the vote equation derived above leads to:
V = 19.9 + 60.5 * ( 19 / (19 + 11)) = 58.3
That is, the bio-index model predicts Clinton to achieve 58.3% of the vote, compared to 41.7% for Trump.
Compute your own forecast
You can also compute your own bio-index model forecasts. This feature allows you
See how the model forecast would change for different variable .
The following chart shows the model’s predicted and actual percentage point lead in the two-party vote for the winners of the 30 elections from 1896 to 2012. The vote-share predictions are calculated in-sample. If both the grey and the orange bars are on the right hand side of zero, the model correctly predicted the final election winner.
Limitations
As any model, the bio-index is subject to limitations.
The bio-index model ignores many factors that are also important for predicting election outcomes.some variables (e.g., height, weight) are not coded since their predictive validity is unclear.
the model’s major aim is not to produce the most accurate forecasts possible. Instead, the major goal of the bio-index was to provide decision-making implications by advising parties on who they should nominate. Given the predicted 18-point lead for Clinton in a hypothetical race against Trump, the model’s implications are clear: Donald Trump would be the worst possible choice of the remaining candidates.
Variables
A large stream of research, particularly in psychology, analyzes questions such as what makes people emerge as leaders? For example, meta-analyses found intelligence and height to have a positive impact on both leader performance and leader emergence. Such findings from prior research were used to identify and code the majority of variables in the bio-index. In addition, some variables are based on common sense. For example, it was assumed that voters are more attracted to candidates who are married but not divorced. As shown in Table 1 at the end of this page, the model distinguishes two types of variables:
Yes / No variables (n=47): For this type of variable, candidates are assigned a score of 1 if they possess a certain attribute and 0 otherwise.
Comparative variables(n=11): For this type of variable, the candidates of the two major parties are compared on the underlying attribute. The candidate who scores better than his opponent is assigned a score of 1 and 0 otherwise.
Forecast calculation
The model is based on the index method, which is useful in situations with many variables and good prior knowledge about the variables’ directional effect on the target criterion.
Predicting the election winner
After all variables have been coded, the total index scores for each candidate are calculated by summing up the scores using equal weights. Then, the candidate who achieves the higher overall score is predicted as the election winner.
Predicting vote-shares
Simple linear regression is then used to relate the incumbent party candidate’s relative bio-index score (bio) to the dependent variable, which is the actual two-party popular vote share received by the candidate of the incumbent party (V). Using data from the 30 elections from 1896 to 2012 leads to the following vote equation:
V = 19.9 + 60.5 * bio,
where bio = bio-score (incumbent) / [bio-score (incumbent) + bio-score (challenger)]
That is, the big-issue model predicts that an incumbent would start with 19.9% of the vote, plus a share depending on bio. If the incumbent’s relative bio score went up by 10 percentage points, the incumbent’s vote share would go up by 6 percentage points.
2016 forecast
The table above shows the coding for Hillary Clinton and Donald Trump. For some comparative variables, such as intelligence, data are not available. Other comparative variables, such as weight or height, are not coded due to the fact that the Clinton-Trump race is an inter-gender comparison.
Based on the coding, Clinton achieves a bio-index score of 18 points (compared to 11 points for Trump) and is thus predicted to win the election. Inserting these figures into the vote equation derived above leads to:
V = 19.9 + 60.5 * ( 19 / (19 + 11)) = 58.3
That is, the bio-index model predicts Clinton to achieve 58.3% of the vote, compared to 41.7% for Trump.
Compute your own forecast
You can also compute your own bio-index model forecasts. This feature allows you
See how the model forecast would change for different variable .
The following chart shows the model’s predicted and actual percentage point lead in the two-party vote for the winners of the 30 elections from 1896 to 2012. The vote-share predictions are calculated in-sample. If both the grey and the orange bars are on the right hand side of zero, the model correctly predicted the final election winner.
Limitations
As any model, the bio-index is subject to limitations.
The bio-index model ignores many factors that are also important for predicting election outcomes.some variables (e.g., height, weight) are not coded since their predictive validity is unclear.
the model’s major aim is not to produce the most accurate forecasts possible. Instead, the major goal of the bio-index was to provide decision-making implications by advising parties on who they should nominate. Given the predicted 18-point lead for Clinton in a hypothetical race against Trump, the model’s implications are clear: Donald Trump would be the worst possible choice of the remaining candidates.
Similar questions