Methodology

  • The City University of New York’s Institute for State and Local Governance (CUNY ISLG) developed the original methodology for creating and implementing Equality Indicator tools; this methodology was replicated and built upon here in Tulsa.

  • Development of Tulsa Equality Indicators

    In August 2017, Community Service Council (CSC) and the City of Tulsa, with guidance from the CUNY ISLG team, led seven community feedback sessions over three days in six different locations covering North, South, East, West, and downtown Tulsa. There was a combined total of 159 attendees. The community feedback sessions were designed to be an open-forum for discussion about equality issues in Tulsa. Participants were invited to share their opinions on topics relevant to the Tulsa area, and ideas were captured by CSC and CUNY ISLG staff and compiled after the events. CSC also released an online opinion survey to collect responses about issues of inequality in Tulsa. Invitations to complete the online survey went out to around 8,000 people through various digital avenues. The survey was open for six weeks and received 259 responses.

    The community feedback sessions and the online opinion survey provided a combined total of 396 unique suggestions for possible indicators in addition to broad feedback about themes, topics, and groups. All public feedback was collected and analyzed to shape the initial framework for the Tulsa Equality Indicators. Indicators were then carefully selected based on the quality and availability of data.

  • Populations Negatively Affected by Inequality

    Many groups of people experience inequality. Tulsa Equality Indicators measures disparities between comparable groups on different topics, which serve as proxies for measuring equality in the City of Tulsa. Most of the indicators reflect a comparison of the most and least advantaged groups.

    The Tulsa Equality Indicators compares outcomes for populations according to:

    1. Age
    2. Education Level
    3. English Proficiency
    4. Gender
    5. Geography / Location
    6. Income Level
    7. Mode of Transportation
    8. Presence of a Disability
    9. Race / Ethnicity
    10. Veteran Status

  • Structure of Tulsa Equality Indicators

    Four Levels of the Tulsa Equality Indicators Framework

    City Level Score: 1
    Theme Scores: 6
    Topic Scores: 18 (6 Themes x 3 Topics)
    Indicator Scores: 54 (6 Themes x 3 Topics x 3 Indicators)

  • Indicators Flow Chart 2018
  • Data Sources

    Much of the data used for the indicators are publicly available, however some data sets were provided by request. All data used in this report will be publicly available via the Tulsa Equality Indicators website.

    As in the original methodology, Tulsa Equality Indicators uses annually-collected data to score the indicators. Tracking these measures from year to year enables progress to be assessed at regular intervals. The most recently available data at the time of data collection are used; however, that year is not uniform across sources. For this year’s report, while much of the annual data represent 2016, in some cases the most recent data available were from 2015 or 2017. Additionally, education data for one school year takes place over portions of two separate calendar years (e.g., school year 2017 includes the fall semester of 2016 and the spring semester of 2017). Tulsa Equality Indicators uses the U.S. Census Bureau American Community Survey to calculate population rates where appropriate.

    How Information is Reported

    Equality Indicators tools score indicators in two ways, employing a score for each given year, termed a static score, and a score measuring change from the baseline, a change score. Where possible, additional data are reported alongside scores to provide a fuller picture of each measure. Since this is the first year, indicators will only have static scores. In following years, change scores will be reported alongside each static score.

    As noted by CUNY ISLG, there are two primary benefits to scoring. First, it allows for different types of data using different metrics to be reported in a standard way. Second, scoring allows for findings to be aggregated to produce findings at successively higher levels.

    Click Here for the full resource library used in the Tulsa Equality Indicators 2018 Annual Report.

  • Scoring

    Equality Indicators tools score indicators in two ways, employing a score for each given year, termed a static score, and a score measuring change from the baseline, a change score. Where possible, additional data are reported alongside scores to provide a fuller picture of each measure. Since this is the first year, indicators will only have static scores. In following years, change scores will be reported alongside each static score.

    As noted by CUNY ISLG, there are two primary benefits to scoring. First, it allows for different types of data using different metrics to be reported in a standard way. Second, scoring allows for findings to be aggregated to produce findings at successively higher levels.

    Static Scoring

    Each indicator, topic, and theme, as well as the city level score, is scored from 1 to 100, with 1 being the highest possible inequality and 100 being the highest possible equality.

    All 54 indicators are reported as ratios. The ratios are created by comparing two groups—generally the most and least disadvantaged for a specific indicator. Higher ratios correspond to more disparities and lower scores. For instance, a ratio of 1:1 indicates equality, while a ratio of 5:1 indicates that a group is five times more likely to experience a particular outcome.

    Static scores at higher levels are created by averaging the scores one level below them. This means that static topic scores are comprised of the average of their three indicators and static theme scores are comprised of the average of their three topics. The six themes are averaged to produce the static citywide score each year.

  • Rounded Values

    The report uses uniform rounding rules for decimal places. Indicator ratios and scores are calculated using raw values from the data sources. For the report language, the accompanying table indicates how the decimal place rules are uniformly applied. The purpose of this exception is to clarify any perceived discrepancy in data sources and the numerical values represented in this report.

  • Value Table
  • Scoring Change

    Change scores can reflect positive change (represented by a positive number), negative change (a negative number), or no change (score of 0).

    In all future annual reports, change scores at each level will be calculated by subtracting the baseline year’s score from the current year’s score. As laid out by CUNY ISLG, “change scores at each successive level [will only be] produced when all relevant lower-level scores have been produced. This means that a topic-level score will only be produced when all indicators within the topic are scored, a theme-level score will only be produced when all topics in the theme have been scored, and the citywide score will only be produced when all themes have been scored.”

    Methodology Exceptions

    North Tulsa is typically found to be the most disadvantaged group in geographic-based comparisons throughout this report. Accordingly, North Tulsa has been designated as the most disadvantaged group for all indicators in which it is one of the comparison groups. As a result, for those indicators in which North Tulsa is found to have a better outcome, an exception to the methodology is applied. The ascribed ratio of largest to smallest number is replaced with the inverted ratio of smallest to largest number, resulting in a ratio of less than 1 and a score of 100. Therefore, in the few cases where North Tulsa has performed the same or better than the geography to which it was compared, that indicator was assigned a perfect equality score of 100.

    Indicators 26 (Geography & housing choice vouchers) and 47 (Geography & public city parks with playgrounds) are the two instances in which this methodology exception is appropriate and applied.