Abstract: Methods and apparatus to model on/off states of media presentation devices based on return path data are disclosed. An example method includes generating a modelled tuning segment distribution indicative of modelled durations of modelled tuning segments. The modelled tuning segments is based on panel tuning segments during which panelists were exposed to first media. The example method further includes obtaining return path data (RPD) tuning information reported from an RPD device. The RPD tuning information is indicative of a reported RPD tuning segment during which the RPD device was accessing second media. The example method also includes estimating a set-on time for a media set associated with the RPD device based on the RPD tuning information and the modelled tuning segment distribution. The set-on time is indicative of a period of time when the media set is powered on.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to generate synthetic respondent level data. An example method includes generating a first and second synthetic panel corresponding to a first duration of time based on a seed panel and first duration constraints; generating synthetic respondent level data corresponding to a second duration of time by linking synthetic panelists from the first synthetic panel to the second synthetic panel; and generating an output file based on the synthetic respondent level data.

Abstract: Methods, apparatus, systems and articles of manufacture to assign viewers to media meter data are disclosed. An apparatus includes memory to store instructions, and a processor to execute the instructions to at least: determine first probabilities for first panelists in a media meter household during a first set of time periods, determine second probabilities for second panelists in a plurality of learning households during a second set of time periods, compare the first probabilities and the second probabilities to identify a candidate household from the plurality of learning households to associated with the media meter household, and impute ones of the first number of minutes to individual ones of the first panelists when second panelist behavior data associated with the candidate household indicates activity during one of the second set of time periods that matches activity in the media meter household during one of the first set of time periods.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to rectify false set top box tuning data. Disclosed examples methods include identifying in return path data a first group of set top boxes classified as likely to exhibit machine events in tuning data of the return path data more frequently than a second group of set top boxes represented in the return path data. Additionally, in some examples, the method includes determining whether the first group of set top boxes includes a machine event based on a pattern of the tuning data in the return path data for respective ones of the first group of set top boxes and improving an accuracy of return path data by rectifying the machine event.

Abstract: Methods, apparatus, systems and articles of manufacture to improve viewer assignment by adjusting for a localized event are disclosed. An example method includes identifying, by executing an instruction with a processor, heavy tuning data associated with panelists in a first area based on (1) a first number of households tuned to first media in the first area and (2) a first percentage of exposure minutes tuned to the first media in the first area. The example method also includes determining, by executing an instruction with the processor, if the heavy tuning data represents a local bias based on a second percentage of exposure minutes tuned to second media in a second area.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to assign viewers to media meter data.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to generate synthetic respondent level data. An example method includes generating a first and second synthetic panel corresponding to a first duration of time based on a seed panel and first duration constraints; generating synthetic respondent level data corresponding to a second duration of time by linking synthetic panelists from the first synthetic panel to the second synthetic panel; and generating an output file based on the synthetic respondent level data.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to generate synthetic respondent level data.

Abstract: Methods and apparatus to model on/off states of media presentation devices based on return path data are disclosed. An example method includes generating a modelled tuning segment distribution indicative of modelled durations of modelled tuning segments. The modelled tuning segments is based on panel tuning segments during which panelists were exposed to first media. The example method further includes obtaining return path data (RPD) tuning information reported from an RPD device. The RPD tuning information is indicative of a reported RPD tuning segment during which the RPD device was accessing second media. The example method also includes estimating a set-on time for a media set associated with the RPD device based on the RPD tuning information and the modelled tuning segment distribution. The set-on time is indicative of a period of time when the media set is powered on.

Abstract: Methods, apparatus, systems and articles of manufacture to improve viewer assignment by adjusting for a localized event are disclosed. An example method includes identifying, by executing an instruction with a processor, heavy tuning data associated with panelists in a first area based on (1) a first number of households tuned to first media in the first area and (2) a first percentage of exposure minutes tuned to the first media in the first area. The example method also includes determining, by executing an instruction with the processor, if the heavy tuning data represents a local bias based on a second percentage of exposure minutes tuned to second media in a second area.

Abstract: Methods, apparatus, systems and articles of manufacture to impute media consumption behavior are disclosed. An example method includes calculating, by executing an instruction with a processor, first viewing probabilities for tuning panelists in a tuning household. The example method also includes calculating, by executing an instruction with the processor, second viewing probabilities for viewing panelists in a plurality of learning households having a same household size as the tuning household. The example method also includes identifying, by executing an instruction with the processor, a matching one of the plurality of learning households based on a difference between the first viewing probabilities and respective ones of the second viewing probabilities.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to calculate video-on-demand and dynamically inserted advertisement viewing probability.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to calculate video-on-demand and dynamically inserted advertisement viewing probability.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to calculate video-on-demand viewing probability.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to assign viewers to media meter data.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to calculate video-on-demand viewing probability.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to calculate video-on-demand viewing probability.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to characterize households with media meter data. An example method includes identifying, with a processor, a power status and a first automatic gain control (AGC) value for an exposure minute from a panelist audience meter in a first household, the panelist audience meter comprising a power sensor, identifying a second AGC value and a daypart for a household tuning minute from a first media meter (MM) in the first household, the MM comprising microphones to collect audio data, and calculating model coefficients based on the exposure minute and the household tuning minute to be applied to data from a second MM in a second household, the model coefficients to facilitate a power status probability calculation in the second household devoid of the panelist audience meter having the power sensor.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to characterize households with media meter data. An example method includes identifying, with a processor, a household minute credited to a station in a household by a first media meter (MM) device, the first MM device collecting only audio data, determining whether a panelist audience meter device is crediting the station at the same time in the household, when the panelist audience meter device is crediting the same station, associating the household minute with an ambient tuning status, identifying a first automatic gain control (AGC) value and a first code status of the household minute, calculating model coefficients based on the ambient tuning status, the first AGC value and the first code status, and calculating a probability of ambient tuning based on the model coefficients.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to characterize households with media meter data.