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13.Analysis Demonstration: Descriptive/Comparative Analysis Using Longitudinal Data

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13. Demonstration: Descriptive/Comparative Analysis Using Longitudinal Data 1 Prerequisites Recommended modules to complete before viewing this module 1. Introduction to the NLTS2 Training Modules 2. NLTS2 Study Overview 3. NLTS2 Study Design and Sampling NLTS2 Data Sources, either 4. Parent and Youth Surveys or 5. School Surveys, Student Assessments, and Transcripts 9. Weighting and Weighted Standard Errors

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13. Demonstration: Descriptive/Comparative Analysis Using Longitudinal Data 2 Prerequisites Recommended modules to complete before viewing this module (cont’d) NLTS2 Documentation 10. Overview 11. Data Dictionaries 12. Quick References Helpful Implications for analysis 6. Data Content 7. Parent/Youth Survey Data

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13. Demonstration: Descriptive/Comparative Analysis Using Longitudinal Data 3 Overview Posing questions to the NLTS2 database Types of questions; getting to the answers Comparative variables Analysis demonstration using longitudinal data Analysis plan for this demonstration Results output for this demonstration Interpretation of results for this demonstration Review steps required for this demonstration Closing Important information

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13. Demonstration: Descriptive/Comparative Analysis Using Longitudinal Data 4 Posing Questions to the NLTS2 database Types of questions Descriptive questions—e.g., means, frequency distributions for an identified group Comparative questions—e.g., differences between subgroups of interest, tests of significance Longitudinal questions—i.e., relationships between variables measured at different time points Explanatory questions—i.e., questions regarding relationships between variables, usually requiring controls for covariates

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13. Demonstration: Descriptive/Comparative Analysis Using Longitudinal Data 5 Examples of types of questions Descriptive/comparative How proficient are high school students with disabilities in reading? How does proficiency differ by disability category? By race/ethnicity? Longitudinal/comparative If youth has been employed, how does income change over time for youth with disabilities as a group? How does it change at the individual level? How do changes in wages over time differ by disability category? By race/ethnicity? Explanatory What factors relate to variations in the employment of youth with disabilities?

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13. Demonstration: Descriptive/Comparative Analysis Using Longitudinal Data 6 Getting to the answer 1.Develop an analysis plan. 2.Identify/create variables. Identify data source. Specify variable names. Be aware of variable forms (e.g., continuous, categorical,) and respondent subset. Restructure variables if applicable (e.g., regroup into different categories). Create variables as needed (e.g., scales from multiple items). 3.Select Analysis approach from options appropriate to the question. Software procedure(s) to execute approach. 4.Execute plan.

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13. Demonstration: Descriptive/Comparative Analysis Using Longitudinal Data 7 Comparative variables A set of demographic variables are commonly used for comparative analyses in NLTS2 reports and publications. NLTS2 demographic variables are by wave and included in every school and parent/youth sourced files. The demographic variables are Primary disability category Age category Race/ethnicity Gender Household income category

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13. Demonstration: Descriptive/Comparative Analysis Using Longitudinal Data 8 Comparative variables Grade level and urbanicity are used with school-based data. Demographic variables are based on location of the school district where youth attended secondary school. School related demographic variables are included with school data files but not applicable to parent/youth survey files.

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13. Demonstration: Descriptive/Comparative Analysis Using Longitudinal Data 9 Analysis demonstration using longitudinal data Research questions for this demonstration How do wages change over time for youth with disabilities as a group? How do they change at the individual level? How do changes in youth wages over time differ by parent’s household income?

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13. Demonstration: Descriptive/Comparative Analysis Using Longitudinal Data 10 Analysis plan for this demonstration Select procedures that will describe these changes. In this demonstration, we will use procedures that provide weighted frequencies, percentages, means, and standard errors. Select procedures to make comparisons. In this demonstration, we will produce crosstabulations and means by a comparison variable and determine whether differences are statistically significant.

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13. Demonstration: Descriptive/Comparative Analysis Using Longitudinal Data 11 Analysis plan for this demonstration Select variables that measure change. Compare items in Parent/Youth Wave 2 with Parent/Youth Wave 5. Wage youth earned at current or most recent job (Wave 2 np2HourlyWage compared with Wave 5 np5T4h_L4h). Select comparison variables. Comparison variables are often demographics such as primary disability category, gender, and race/ethnicity. Parent/guardian’s household income will be the comparison variable in this demonstration (W5_IncomeHdr2009).

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13. Demonstration: Descriptive/Comparative Analysis Using Longitudinal Data 12 Analysis plan for this demonstration Compare time 1 and time 2. One approach to observe a difference between time 1 and time 2 is to compare a time 1 mean with a time 2 mean calculated across the entire sample. To measure individual difference, an approach is to calculate the difference between that youth’s time 1 measure and his or her time 2 measure. To calculate the difference between time 1 and time 2, there must be a value for both time 1 and time 2 to be included in this analysis.

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13. Demonstration: Descriptive/Comparative Analysis Using Longitudinal Data 13 Analysis plan for this demonstration Missing values Expect to have fewer cases to analyze in a longitudinal analysis than in a separate analysis of either time 1 or time 2. Youth must have data in both waves to be included. Create new variables Difference = time 2 – time1 The order is important; subtracting time 1 from time 2 will result in a positive number if the change has been in a positive direction.

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13. Demonstration: Descriptive/Comparative Analysis Using Longitudinal Data 14 Results output for this demonstration Means: hourly wages in Wave 2 and Wave 5 Mean: difference in wages between Wave 2 and Wave 5 Percentage who had a decrease or increase in wages Crosstabs by parent/guardian household income

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13. Demonstration: Descriptive/Comparative Analysis Using Longitudinal Data NLTS2 restricted-use data NLTS2 data are restricted. Data used in these presentations are from a randomly selected subset of the restricted-use NLTS2 data. Results in these presentations cannot be replicated with the NLTS2 data licensed by NCES. 15

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13. Demonstration: Descriptive/Comparative Analysis Using Longitudinal Data 16 Means of Hourly Wages in Wave 2 and Wave 5 Estimate Standard Error Unweighted Count MeanHourly pay youth earned at job (in or out of sch) in Wave 2 (np2HourlyWage) MeanHourly wage young adult earns at current or most recent job in Wave 5 (np5T4h_L4h) Results output for this demonstration These results cannot be replicated with full dataset; all output in modules generated with a random subset of the full data.

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13. Demonstration: Descriptive/Comparative Analysis Using Longitudinal Data 17 Mean Difference in Wages between Wave 2 and Wave 5 EstimateStandard Error Unweighted Count MeanChange in wages between Waves 2 and 5 (W25_Wage_chg) Results output for this demonstration These results cannot be replicated with full dataset; all output in modules generated with a random subset of the full data.

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13. Demonstration: Descriptive/Comparative Analysis Using Longitudinal Data 18 Percentage Who Had a Decrease or Increase in Wages between Waves 2 and 5 W25_Wage_chgCat Estimate Standard Error Unweighted Count % of Total(1) Decrease in wages (2) No change (3) Increase in wages Total 7.7% 4.6% 87.7% 100.0% 1.6% 2.9% 3.2%.0% Results output for this demonstration These results cannot be replicated with full dataset; all output in modules generated with a random subset of the full data.

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13. Demonstration: Descriptive/Comparative Analysis Using Longitudinal Data 19 Mean Difference in Wages, by Household Income between Wave 2 and Wave 5 Household Income (W5_IncomeHdr2009)Estimate Standard Error Unweighted Count (1)$25,000 or lessMeanChange in wages between Waves 2 and 5 (W25_Wage_chg) (1)$25,001 - $50,000Mean(Change in wages between Waves 2 and 5 W25_Wage_chg) (1)More than $50,000 Mean(Change in wages between Waves 2 and 5 W25_Wage_chg) Results output for this demonstration These results cannot be replicated with full dataset; all output in modules generated with a random subset of the full data.

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13. Demonstration: Descriptive/Comparative Analysis Using Longitudinal Data 20 Percentage Decrease or Increase in Wages by Household Income between Waves 2 and 5 (W25_Wage_chgCat) (W5_IncomeHdr2009 Income) (1) $25,000 or less (2) $25,001– $50,000 (3) More than $50,000 Total (1) Decrease in wages% within (W5_IncomeHdr2009) Income category for tables Estimate4.6%6.8%10.5%7.7% Standard Error1.9%2.0%3.2%1.6% Unweighted Count (2) No change% within (W5_IncomeHdr2009) Income category for tables Estimate11.1%3.4%1.1%4.6% Standard Error9.3%1.9%.5%2.9% Unweighted Count (3) Increase in wages% within (W5_IncomeHdr2009) Income category for tables Estimate84.3%89.8%88.4%87.6% Standard Error9.0%2.9%3.3%3.2% Unweighted Count Total% within (W5_IncomeHdr2009) Income category for tables Estimate100.0% Standard Error.0% Unweighted Count Results output for this demonstration These results cannot be replicated with full dataset; all output in modules generated with a random subset of the full data.

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13. Demonstration: Descriptive/Comparative Analysis Using Longitudinal Data 21 Interpretation of results for this demonstration Is it significant? We use an Excel worksheet. Enter (1) the percentage and standard error for a category and (2) the percentage and standard error for the comparison category. The significance is calculated by a formula stored in Excel. These results cannot be replicated with full dataset; all output in modules generated with a random subset of the full data.

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13. Demonstration: Descriptive/Comparative Analysis Using Longitudinal Data 22 Interpretation of results for this demonstration Interpreting longitudinal results. A positive number indicates a positive change between time 1 and time 2 for difference in wage. A negative number indicates a negative change between time 1 and time 2. Numbers close to 0 indicate little or no change. What does it mean? A positive or negative change may be simple to interpret, but what does little or no change mean? What explains the change? For example, postsecondary attendance might mean a change in employment.

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13. Demonstration: Descriptive/Comparative Analysis Using Longitudinal Data 23 Interpretation of results for this demonstration Was anything significant? Not with the randomly selected subset of data. The full data set might have a different result if the standard errors were smaller. Or perhaps household income was not the best comparison variable to use. Things to think about What other comparison variables might show significant differences between comparison groups? Are there other difference in employment measures that might be worth exploring? Were these the best waves of data to compare? Should any types of youth be included or excluded from this analysis?

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13. Demonstration: Descriptive/Comparative Analysis Using Longitudinal Data 24 Interpretation of results for this demonstration More things to think about Did we have a good comparison group? Would it have been better to select Waves 3 and 5 instead of 2 and 5 as more youth are out of secondary school in Wave 3 than in 2? Would it have been better to run the analysis on a subgroup of only those who were out of secondary school at each wave? The answer is…it is important to select the appropriate subsample as well as to select the appropriate variables.

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13. Demonstration: Descriptive/Comparative Analysis Using Longitudinal Data 25 Review steps required for this demonstration Plan for analysis Identify the research question. Find the variables needed. Determine if any variables need to recoded. Collapse categories. Create a longitudinal measure. Identify the appropriate subsample if applicable.

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13. Demonstration: Descriptive/Comparative Analysis Using Longitudinal Data 26 Plan for analysis (cont’d) Identify the appropriate procedures. Descriptive Crosstabs Means Means by comparison variables if calculating means Test significance of comparisons Review steps required for this demonstration

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13. Demonstration: Descriptive/Comparative Analysis Using Longitudinal Data 27 Closing Topics discussed in this module Posing questions to the NLTS2 database Comparative variables Analysis demonstration using longitudinal data Analysis plan for this demonstration Results output for this demonstration Interpretation of results for this demonstration Review steps required for this demonstration

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13. Demonstration: Descriptive/Comparative Analysis Using Longitudinal Data 28 Closing Next module 14a. Accessing Data Files in SPSS or 14b. Accessing Data Files in SAS

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13. Demonstration: Descriptive/Comparative Analysis Using Longitudinal Data 29 Important information NLTS2 website contains reports, data tables, and other project-related information Information about obtaining the NLTS2 database and documentation can be found on the NCES website General information about restricted data licenses can be found on the NCES website address:

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