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Early Childhood Longitudinal Study, Kindergarten Class of 1998–99 (ECLS-K)


Target Population

Kindergarten children enrolled in school in the United States during the 1998–99 school year are the target population for the ECLS-K cohort.


The ECLS-K followed a nationally representative sample of children from kindergarten through the spring of 2007, when most of the children were in eighth grade. Study children were included in data collections after the kindergarten year even if they were no longer in the modal grade for children who were in kindergarten in the 1998–99 school year.

Base-year (i.e., kindergarten) collections. A nationally representative sample of children enrolled in kindergarten programs during the 1998–99 school year was sampled for participation in the study. These children were selected from both public and private schools, offering both full-day and part-day kindergarten programs. The sample included both children in kindergarten for the first time and children who were repeating kindergarten. The sample was designed to support separate estimates of public and private school kindergartners; Black, Hispanic, White, and Asian/Pacific Islander children; and children grouped by SES.

In the kindergarten year, the study can also be used to general estimates of schools educating kindergarten-age children and kindergarten teachers. After the base year, the data are only representative at the child level.

The sample design for the ECLS-K was a dual-frame, multi-stage sample. First, 100 PSUs were selected from an initial frame of approximately 1,335 PSUs, representing counties or groups of contiguous counties. The 24 PSUs with the largest measures of size (where the measure of size was the number of 5 year-olds, taking into account a factor for oversampling Asian/Pacific Islander 5 year-olds) were included in the ECLS-K sample with certainty. The remaining PSUs were partitioned into 38 strata of roughly equal measures of size. The frame of these noncertainty PSUs was first sorted into eight superstrata by metropolitan statistical area (MSA) status and by census region resulting in four MSA superstrata and four non-MSA superstrata. Within the four MSA superstrata, the variables used for further stratification were race/ethnicity (high concentration of Asian/Pacific Islanders, Blacks, or Hispanics), size of class, and 1988 per capita income. Within the four non-MSA superstrata, the stratification variables were race/ethnicity and per capita income. Two PSUs were selected without replacement in each stratum, with probability proportional to size and with known joint probability of inclusion of the pair.

School selection occurred within the sampled PSUs. Public schools were sampled from a public school frame (the 1995–96 CCD), and private schools were sampled from a private school frame (the 1995–96 PSS). The school frame was freshened in the spring of 1998 to include newly opened schools that were not included in the CCD and PSS used for initial sample selection (as well as schools that were included in the CCD and PSS but that did not offer kindergarten, according to these sources). A school sample supplement was selected from the freshened frame. In the fall of 1998, approximately 23 kindergarten children were selected, on average, from each of the sampled schools. Asian/Pacific Islander children and private schools were oversampled.

A nationally representative sample of 22,782 children enrolled in 1,277 kindergarten programs during the 1998–99 school year was selected to participate in the ECLS-K.

Fall first-grade collection. The fall first-grade collection was designed to enable researchers to measure the extent of summer learning loss, examine the factors associated with such loss, and to better understand the relationships of school and home characteristics with children’s learning. The fall data collection consisted of a 30 percent sample of schools containing approximately 25 percent of the base-year students eligible to participate in the second year. Data collection was attempted for every eligible child (i.e., a base-year respondent) still attending the school in which he or she had been sampled during kindergarten. To contain the cost of collecting data for a child who transferred from the school in which he or she was originally sampled, a random 50 percent of movers (i.e., children who changed schools) were flagged to be followed for the fall first-grade data collection.

Spring first-grade collection. This data collection targeted all base-year respondents. In addition, the spring student sample was freshened to include current first-graders who had not been enrolled in kindergarten in the United States in 1998–99 and, therefore, had no chance of being included in the ECLS-K base-year kindergarten sample. While all students still enrolled in their base-year schools were recontacted, only a 50 percent subsample of base-year sampled students who had transferred from their kindergarten school was followed for data collection. For the spring first-grade data collection, approximately 18,080 children were eligible to participate (14,250 public school students and 3,840 private school students). Student freshening brought 170 first-graders into the ECLS-K sample.

Spring third-grade collection. The sample of children for the spring third-grade data collection consisted of all children who were base-year respondents and children who were brought into the sample in the spring of first grade through sample freshening. Sample freshening was not implemented in third grade. While all students still enrolled in their base-year schools were recontacted, slightly more than 50 percent of the base-year sampled students who had transferred from their kindergarten school were followed for data collection. This subsample of students was the same 50 percent subsample of base-year movers (i.e., those students who transferred from an originally sampled school) flagged for following in the spring of first grade, with the addition of movers whose home language was not English (who were followed at 100 percent). For the spring third-grade data collection, approximately 16,670 children were eligible to participate (13,170 public school students and 3,500 private school students).

Spring fifth-grade collection. In fifth grade, four groups of children were not followed for data collection. These were (1) children who became ineligible in an earlier round (because they had died or moved out of the country), (2) children who were subsampled out in previous rounds because they had moved out of their original schools and were not followed, (3) children whose parents emphatically refused to cooperate in any of the data collection rounds since the spring of kindergarten, and (4) children eligible for the third-grade data collection for whom neither first-grade nor third-grade data had been collected.

Of the remaining children, those who moved from their original schools during fifth grade or earlier were subsampled for follow-up. In order to contain the cost of data collection, the rate of subsampling was lower in fifth grade than it had been in previous years. The subsampling rates maximized the amount of longitudinal data available for key analytic groups. Children whose home language was not English (language minority (LM) children) continued to be sampled at higher rates (between 15 and 50 percent for base-year LM respondents, and between 15 and 75 percent for LM children in the first-grade freshened sample).

For the spring fifth-grade data collection, approximately 12,030 children were eligible to participate (9,570 public school students and 2,460 private school students).

A sampling strategy first implemented for the fifth-grade data collection was the subsampling of eligible children for the administration of mathematics and science questionnaires. While a child-level reading teacher questionnaire was fielded for all children included in the fifth-grade data collection, half of the children were selected to have a child-level questionnaire filled out by their mathematics teachers and the other half were selected to have a child-level questionnaire filled out by their science teachers.

Spring eighth-grade collection. Children who had moved out of the country, were deceased, or had moved to another school and were not subsampled for follow-up in an earlier grade were ineligible for the eighth-grade data collection. There was no subsampling of movers for follow-up as in previous rounds, since the majority of children did not remain in the same school from fifth grade to eighth grade (having moved out of elementary school into middle school). As in fifth grade, half of the children were selected to have a child-level questionnaire filled out by their mathematics teachers and the other half were selected to have a child-level questionnaire filled out by their science teachers.

For the spring eighth-grade data collection, approximately 11,930 children were eligible (9,480 in public schools and 2,450 in private schools).

Assessment Design

The design of the ECLS–K assessment was guided by the domain assessment framework proposed by the National Education Goals Panel’s Resource Group on School Readiness. A critical component of the ECLS–K is the assessment of children on a number of dimensions, including physical, socioemotional, and cognitive development. These domains were chosen because of their importance to success in school. The ECLS–K monitored the status and growth of its children along these domains:

  • Physical and psychomotor development: Children’s height and weight were measured at each data collection point in the ECLS-K. The psychomotor component was included only in the fall kindergarten collection. In that collection, kindergartners were asked to demonstrate their fine and gross motor skills through activities such as building a structure using blocks, copying shapes, drawing figures, balancing, hopping, skipping, and walking backward. Parents and teachers reported on other related issues, such as general health, nutrition, and physical activity. Beginning in third grade, the children also were asked to provide information about their eating habits and physical activity.
  • Socioemotional development: The ECLS-K indirect assessments of socioemotional development focused on the skills and behaviors that contribute to social competence. Aspects of social competence include social skills (e.g., cooperation, assertion, responsibility, self-control) and problem behaviors (e.g., impulsive reactions, verbal and physical aggression). Parents and teachers were the primary sources of information on children’s social competence and skills in kindergarten and first grade. The measurement of children’s social and emotional development at grades three, five, and eight included instruments completed by the children themselves along with data reported by parents and teachers.

  • Cognitive development: In kindergarten and first grade, the ECLS-K focused on three broad areas of competence: language and literacy, mathematics, and general knowledge of the social and physical worlds. The same assessments were fielded in both kindergarten and first grade. Starting in third grade, a science assessment replaced the general knowledge assessment. In the higher grades, children’s cognitive skills were expected to have advanced beyond the levels covered by the kindergarten and first-grade assessments; for this reason, a new set of assessment instruments was developed for third grade, for fifth grade, and again for eighth grade. Some of the assessment items were retained from one round to the next to support the development of longitudinal score scales in each subject area. The skills measured in each of these domains are a sample of the typical and important skills that are taught in American elementary schools and that children are expected to learn in school. The ECLS-K was developed to describe the behaviors, skills, and knowledge within broad cognitive domains that are relevant to school curricula at each grade level and to measure children’s growth from kindergarten to eighth grade. The ECLS-K assessment framework was based on current curricular domain frameworks for reading, mathematics, science, and social studies, as well as on existing assessment frameworks, such as those used in the National Assessment of Educational Progress (see NAEP chapter).

  • The cognitive assessments were developed through extensive field testing and analysis of item performance. The final items were selected based on their psychometric properties and content relevance.

  • Each direct cognitive domain subtest consisted of a routing test and second-stage tests that were tailored to different skill levels. All children were first administered a short routing test of domain-specific items having a broad range of complexity or difficulty levels. Performance on the routing test was used to determine the appropriate second-stage assessment form to be administered next to the child. The use of multilevel forms for each domain subtest minimized the chances of administering items that were all very easy or all very difficult for a given child. The assessments were administered in one-on-one, untimed sessions with a trained child assessor. If necessary, the session could take place over multiple periods.

Data Collection and Processing
The ECLS-K compiled data from four primary sources: children, children’s parents/guardians, teachers, and school administrators. Data collection began in fall 1998 and continued through spring 2007. Self-administered questionnaires, one-on-one assessments, and telephone or in-person interviews were used to collect the data.

Reference dates. Baseline data were collected from September through December 1998 and March through July 1999.

Data collection. The data collection schedule for the ECLS-K was based on a desire to capture information about children as critical events and transitions were occurring rather than measuring these events retrospectively. A large-scale field test of the kindergarten and first-grade assessment instruments and questionnaires was conducted in 1995–96. This field test was used primarily to collect psychometric data on the ECLS-K assessment item pool and to evaluate questions in the different survey instruments. Data from this field test were used to develop the routing and second-stage tests for the ECLS-K kindergarten and first-grade direct cognitive assessment battery and to finalize the parent, teacher, and school administrator instruments. A pilot test of the study systems and procedures, including field supervisor and assessor training, was conducted in April and May 1998 with 12 elementary schools in the Washington, DC, metropolitan area. Modifications to the data collection procedures, training programs, and systems were made to improve efficiency and reduce respondent burden. Modifications to address some issues raised by pilot test respondents were also made to the parent interview at this time.

Data on the kindergarten cohort were collected twice during the base year of the study—once in the beginning (fall) and once near the end (spring) of the 1998–99 school year. The fall 1998 data collection obtained baseline data on children at the very beginning of their exposure to the influences of school, providing measures of the characteristics and attributes of children as they entered formal school for the first time. The data collected in spring 1999, together with the data from the beginning of the school year, can be used to examine children’s first experiences with elementary school. Data were collected from the child, the child’s parents/guardians, and teachers in both fall and spring. Data were collected from school administrators and special education teachers in the spring. For the fall 1998 and spring 1999 collections, all child assessment measures were obtained through untimed assessments, administered one-on-one to the child by an assessor using a CAPI application. The assessment was normally conducted in a school classroom or library and took approximately 50 to 70 minutes per child. Children with a primary home language other than English (according to school records) were first administered an English language screener (OLDS) to determine whether their English language skills were sufficient enough to take the cognitive assessments in English. Children whose scores on the screener fell below the cut score for the OLDS and whose language was Spanish were administered a Spanish-language version of the OLDS and the ECLS-K mathematics and psychomotor assessments translated into Spanish. They also had their height and weight measured. Children whose scores on the screener fell below the cut score and whose language was neither English nor Spanish had only their height and weight measured. (A child was administered the OLDS in each round of data collection until he or she passed it; the OLDS was no longer used after the spring first-grade data collection because by then most children demonstrated sufficient English language skills to be assessed in English.) Most of the parent data were collected by computer-assisted telephone interviewing (CATI), though some of the interviews were conducted in person through CAPI when respondents did not have a telephone or were reluctant to be interviewed by telephone. All kindergarten teachers with sampled children were asked to fill out self-administered questionnaires providing information on themselves and their teaching practices. The teachers also were asked to complete a child-specific questionnaire for each of the sampled children they taught. In the spring, school administrators were asked to complete a self-administered questionnaire that included questions on the school characteristics and environment, as well the administrator’s own background. Also in the spring, the special education teachers or service providers of children in special education were asked to complete a self-administered questionnaire about the children’s experiences in special education and about their own background. In addition, school staff members were asked to complete a student record abstract after the school year ended.

In the fall of 1999, when most of the kindergarten cohort had moved on to first grade, data were collected from a 30 percent subsample of the cohort. The direct child assessment was administered during a 12-week field period (September–November 1999). The parent interview was administered between early September and mid-November 1999; it averaged 35 minutes and was conducted primarily by telephone.

Spring data collections in first grade, third grade, fifth grade, and eighth grade included direct child assessments, parent interviews, and teacher and school administrator questionnaires. In the spring of first grade, third grade, and fifth grade student record abstracts and facilities checklists were also completed. As in other rounds, the child assessments were administered with CAPI (March–June 2000 for the first-grade collection, March–June 2002 for the third-grade collection, February–June 2004 for the fifth-grade collection, and March–June 2007 for the eighth-grade collection), while both CATI and CAPI were used for the parent interview (March–July 2000 for first grade, March–July 2002 for third grade, February–June 2004 for fifth grade, and March–June 2007 for eighth grade). Self-administered questionnaires were used to gather information from teachers, school administrators, and student records (March–June 2000 for first grade and March–June 2002 for third grade, but field staff prompted by telephone for the return of these materials through October 2000 and October 2002, respectively. For fifth grade, data collection occurred between February and June 2004. For eighth grade, data collection occurred between March and June 2007).

A continuous quality assurance process was also applied to all data collection activities. Specifically, extensive testing of the CATI and CAPI applications and the data collection contractor’s Field Management System was conducted. Field procedures that maximized cooperation and thereby reduced the potential for nonresponse bias were developed. Field staff participated in trainings lasting several days in which they were instructed on proper administration of the parent interview and child assessments. During these trainings, field staff practiced conducting the parent interview in pairs and practiced the direct child assessment with kindergarten children brought to the training site for this purpose. After data collection began, field supervisors observed each assessor conducting child assessments and made telephone calls to parents to validate the interview. Field managers also made telephone calls to the schools to collect information on the school activities for validation purposes.

Editing. Within the CATI/CAPI instruments, the ECLS-K respondent answers were subjected to both “hard” and “soft” range edits during the interviewing process. Responses outside the soft range of reasonably expected values were confirmed with the respondent and entered a second time. For items with hard ranges, out-of-range values (i.e., those that were not considered possible) were usually not accepted. If the respondent insisted that a response outside the hard range was correct, the interviewer could enter the information as a comment. Data preparation and project staff reviewed these comments. Out–of–range values were accepted if the comments supported the response.

Consistency checks were also built into the CATI/CAPI data collection. When a logical error occurred during an interview, the assessor saw a message requesting verification of the last response and a resolution of the discrepancy, if possible. In some instances, if the verified response still resulted in a logical error, the assessor recorded the problem either in a comment or in a problem report.

The overall data editing process consisted of running range edits for soft and hard ranges, running consistency edits, and reviewing frequencies of the results. Where applicable, these steps also were implemented for hard-copy questionnaire instruments.

Estimation Methods

Weighting. Weights are used to adjust for disproportionate sampling at each sampling stage, survey nonresponse, and noncoverage of the target population when analyzing complex survey data. The weights are designed to eliminate or reduce bias that would otherwise occur with analyses of unweighted data.

Several sets of weights were computed for each of the seven rounds of data collection (fall kindergarten, spring kindergarten, fall first grade, spring first grade, spring third grade, spring fifth grade, and spring eighth grade). These weights include cross–sectional weights for analyses of data from one time point, as well as longitudinal weights for analyses of data from multiple rounds of the study. Unlike surveys that have only one type of survey instrument for one type of sampling unit, the ECLS–K is a complex study with multiple types of sampling units, each having its own survey instrument. Each type of unit was selected into the sample through a different mechanism: children were sampled directly through a sample of schools; parents of the sampled children were automatically included in the survey; all kindergarten teachers and administrators in the sampled schools were included; and special education teachers were included in the sample if they taught any of the sampled children. Each sampled unit had its own survey instrument: children were assessed directly using a series of cognitive and physical assessments; parents were interviewed with a parent instrument; teachers filled out at least two different types of questionnaires, depending on the round of data collection and whether they were regular or special education teachers; and school principals reported their school characteristics using the school administrator questionnaire. The stages of sampling, in conjunction with different nonresponse levels at each stage and the diversity of survey instruments, required that multiple sampling weights be computed for use in analyzing the ECLS–K data.

Weight development was driven by three factors: (1) how many points in time would be used in analysis (i.e., whether the analysis would be longitudinal or cross–sectional); (2) what level of analysis would be conducted (e.g., child, teacher, or school); and (3) what source of data would be used (e.g., child assessment, teacher questionnaire, parent interview).

For the kindergarten rounds of data collection, weights were computed in two stages. In the first stage, base weights were computed. The base weights are the inverse of the probability of selecting the unit. In the second stage, base weights were adjusted for nonresponse. Nonresponse adjustment cells were generated using variables with known values for both respondents and nonrespondents. Chi–squared Automatic Interaction Detector (CHAID) analyses were conducted to identify the variables most highly related to nonresponse. Once the nonresponse cells were determined, the nonresponse adjustment factors were calculated as the reciprocals of the response rates within the selected nonresponse cells. Beginning with the first–grade round of data collection, a third stage called raking was introduced into the weight development process to remove the variability due to the subsampling of schools and children who changed schools. In this stage, child weights were raked to sample–based control totals computed using the base–year child weights adjusted for nonresponse.

The base weight computed for each school is the inverse of the probability of selecting the PSU in which the school was located multiplied by the inverse of the probability of selecting the school within the PSU. The base weights for eligible schools were adjusted for nonresponse; this was done separately for public and private schools.

The base weight for each child in the sample is the school nonresponse–adjusted weight for the school the child attended multiplied by a poststratified within–school student weight (total number of students in the school divided by the number of students sampled in the school). The poststratified within–school weight was calculated separately for Asian/Pacific Islander and non–Asian/Pacific Islander children because different sampling rates were used for these two groups. Within a school, all Asian/Pacific Islander children have the same base weights and all non–Asian/Pacific Islander children have the same base weights. Again, these adjustments were made separately for students in public and private schools.

Weights for child–level analysis were developed for every round of data collection. Each child–level weight was developed to be used with data from specific survey components and has adjustments for nonresponse to those specific components. For example, there is a weight to be used in analysis of parent data that is the child base weight adjusted for nonresponse to the parent interview. Weights for analysis at the school and teacher levels (i.e., weights that allow for the generation of national estimates of schools educating kindergarten–age children and kindergarten teachers) were developed only for the kindergarten data collections. The sample is not representative of schools or teachers after the kindergarten year.

Scaling. To maximize information on which each estimate of ability is based, the majority of the direct cognitive assessment scores computed for the study are based on item response theory (IRT). IRT uses patterns of correct and incorrect answers to compute estimates on a scale that may be compared across different assessment forms. IRT was employed in the ECLS–K to calculate ability estimates and then derive assessment scores from those ability estimates that can be compared both within a round and across rounds, regardless of which second–stage form a student was administered. The items in the routing test, plus a core set of items shared among the different second–stage forms, made it possible to establish a common scale.

Imputation. Socioeconomic status (SES) component variables were imputed for the base–year, spring first–grade, spring third–grade, spring fifth–grade, and spring eighth–grade rounds. The percentages of missing data for the education and occupation variables were small (2 to 11 percent in the base year, 4 to 8 percent in the spring of first grade, 2 to 3 percent in the spring of third grade, 1 to 2 percent in the spring of fifth grade, and 3 percent in the spring of eighth grade). The household income variable had a higher rate of missing data (28.2 percent in the base year; 11 to 33 percent in the spring of first grade, depending on whether respondents were asked for income using a detailed set of income range categories or for exact household income; and 11.1 percent, 8.1 percent, and 7.0 percent of cases had missing data for the detailed income range in the spring of third grade, the spring of fifth grade, and the spring of eighth grade, respectively. A standard (random selection within class) hot–deck imputation methodology was used to impute for missing values of all the SES components in all years. From the spring of first grade on, the initial step in the imputation procedure was to fill in missing values from information gathered during an earlier interview with a parent if one had taken place. If no prior data were available, standard hot–deck imputation was used.

The SES component variables were highly correlated, so a multivariate analysis was appropriate to examine the relationship between the characteristics of respondents (donors) and nonrespondents. For the base year, CHAID was used to divide the data into cells based on the distribution of the variable to be imputed, as well as to analyze the data and determine the best predictors. These relationships were used for imputation in later rounds of the ECLS-K.

The variables were imputed in sequential order and separately by type of household. For households with both parents present, the mother’s and father’s data were imputed separately. If this was not the case, an “unknown” or missing category was created as an additional level for the CHAID analysis. As a rule, no imputed value was used as a donor. In addition, the same donor was not used more than two times. The order of the imputation for all the variables was from the lowest percentage missing to the highest.

Imputation for occupation involved two steps. First, the labor force status of the parent was imputed, whether the parent was employed or not. Then the parent’s occupation was imputed only for those parents whose status was identified as employed, either through the parent interview or the first imputation step. The variable for income was imputed last; if a respondent provided partial information about income, this information was used in the imputation process.

Imputation was also employed for composite variables related to the percentage of children in a school who received free or reduced–price lunch. Not all school principals answered all three questions that were used to derive the composite variables indicating the percentage of students in the school who received free lunch and the percentage who received reduced–price lunch: total school enrollment, number of children eligible for free lunch, and number of children eligible for reduced–price lunch. Prior to the fifth grade, if these three source variables had missing values, the composites were filled in with values computed using the most recent CCD data if they were not missing in the CCD, or left missing if they were missing in the CCD. Beginning in fifth grade, missing values in the composite variables were imputed. Missing values in the source variables, however, were not imputed.

A two-stage procedure was used for imputing the school lunch composite variables in fifth and eighth grade. First, if a school had nonmissing values for the school lunch composites in a prior round of data collection, missing values for the current round were filled in with the value from a previous year. Second, data still missing after this initial step were imputed using a hot–deck methodology. Imputation cells were created using the Title I status of the school and school longitude and latitude. School data that were imputed by hot deck were generally transfer schools with few sample children. Imputation was done for the free– and reduced–price lunch composite variables only for children in public schools.

Future Plans

Currently, NCES does not have plans to collect any more data from the students in the ECLS–K cohort or their families. NCES is continuing its program of longitudinal studies of young children with the ECLS–K:2011. More information can be found in the ECLS–K:2011 handbook chapter.