Chapter XV
A guide for data management of household surveys
Juan Mu?oz
Sistemas Integrales
Santiago, Chile
Abstract
The present chapter describes the role of data management in the design and implementation of national household surveys. It starts by discussing the relationship between data management and questionnaire design, and then explores the past, present and future options for survey data entry and data editing, and their implications for survey management in general. The following sections provide guidelines for the definition of quality control criteria and the development of data entry programs for complex national household surveys, up to and including the dissemination of the survey data sets. The final section discusses the role of data management as a support for the implementation of the survey sample design.
Key terms: consistency check, data cleaning, data editing, data management, household survey, quality control criteria
A. Introduction
1. Although the importance of data management in household surveys has often been emphasized, data management is still generally seen as a set of tasks related to the tabulation phase of the survey, in other words, activities that are conducted towards the end of the survey project, that use computers in clean offices at survey headquarters, and that are generally under the control of data analysts and computer programmers.
2. This restrictive vision of survey data management is changing. Experience from the past two decades shows that data management can and should play a critical role beginning with the very earliest stages of the survey effort. It is also becoming clear that data management does not terminate with the publication of the first statistical reports.
3. The clearest demonstration of effective data management efforts prior to the analytical phases has been given by the World Bank’s Living Standards Measurement Study and other surveys that have successfully integrated computer-based quality controls with survey field operations. Even when data entry is not implemented as a part of fieldwork, data managers should participate in the design of questionnaires to ensure that the statistical units observed by the survey are properly recognized and identified, that skip instructions for the interviewers are explicit and correct, and that deliberate redundancies are eventually incorporated into the questionnaires that can be later used to implement effective consistency controls.
4. At the other extreme of the survey project timeline, the notion that the end product expected from the survey is a printed publication, with a collection of statistical tables, has been replaced by the concept of a database that not only can be used by the statistical agency to prepare the initial tables, but will also be accessible to researchers, policymakers and the public in general. The descriptive summary report of survey results is no longer seen as the final step, but rather as the starting point of a variety of analytical endeavours that may last for many years after the project is officially closed and the survey team is disbanded.
5. The present chapter begins with a discussion of the relationships between survey data management and questionnaire design, followed by an exploration of the past, present and future options for survey data entry and data editing, and their implications for survey management in general. The subsequent sections provide guidelines for the definition of quality control criteria and the development of data entry programs for complex national household surveys, up to and including the dissemination of the survey data sets. The final section discusses the role of data management as a support for the implementation of the survey sample design.
B. Data management and questionnaire design
6. Survey data management begins concurrently with questionnaire design and may to a large extent influence the latter. The data manager should be consulted on each major draft of the questionnaire, since he or she will have an especially sharp eye for flaws in the definition of units of observation, skip patterns, etc. The present section explores some of the formal aspects of the questionnaire that deserve attention at this point.
7. Nature and identification of the statistical units observed. Every household survey collects information about a major statistical unit - the household - as well as about a variety of subordinate units within the household - persons, budget items, plots, crops, etc. The questionnaire should be clear and explicit about just what these units are, and it should also ensure that each individual unit observed is properly tagged with a unique identifier.
8. The identification of the household itself generally appears on the cover page of the questionnaire. It sometimes consists of a lengthy series of numbers and letters that represent the geographical location and the sampling procedures used to select the household. Although it may seem self-evident, the use of all these codes as household identifiers should be critically assessed, because it is cumbersome, error-prone and expensive (often 20 digits or more may be needed to identify just the few hundred households in the sample); sometimes it does not even ensure unique identification of the unit as, for instance, when geographical codes on the cover page identify the dwelling but do not consider the case of multiple households in a dwelling. An easier and safer alternative is to identify the households by means of a simple serial number that can be handwritten or stamped on the cover page of the questionnaire, or even pre-printed by the print shop. Geographical location, urban/rural status, sampling codes and the rest of the data on the cover page then become important attributes of the household, which as such must be included in the survey data sets, but not necessarily for identification purposes. A good compromise between these two extremes (the list of all detailed sampling codes and a simple household serial number) is to give a three- or four-digit serial number to the primary sampling units (PSUs) used in the survey, and then a two-digit serial number to the households within each PSU.
9. The nature of the subordinate statistical units is often obvious (for instance, the members of the households are individual persons), but ambiguities may present themselves when what seems like an individual unit is in fact a multiplicity of units of a different kind. This may occur, for instance, when a man who has been asked to report on the main activity of his job conducts multiple, equally important activities at the same time or has more than one job in a given reference period. Similarly, ambiguity is possible when a woman who has been asked about the gender or weight of her last child gave birth to boy-girl twins with different weights. However, although such situations should of course be averted through good questionnaire design and piloting, they often arise in subtle ways, and this is where the critical vision of an experienced data manager can offer invaluable assistance to the subject matter specialists in spotting them. 10. Whatever its nature, subordinate units within the household should always be uniquely identified. This can be done by means of numerical codes assigned by the interviewer, but it is generally better to have these identifiers pre-printed in the questionnaire whenever possible.
11. Built-in redundancies. The design of the questionnaire may consider the inclusion of deliberate redundancies, intended to detect mistakes of the interviewer or data entry errors. The most common examples are:
?Adding a bottom line for “totals” under the columns that contain monetary amounts. Generating these totals may often be the interviewer’s task, but even
when this is not the case, their inclusion is convenient because they are a very
effective way (often the only way) of detecting data entry errors or omissions. In
fact, totals may be added for quality control purposes at the bottom of any
numerical column, even when the sum of the numbers does not represent a
meaningful measure of magnitude (for instance, a total may be added at the
bottom of a column containing the quantities (not the monetary amounts) of
various food items purchased, even if that means adding heterogeneous numbers,
such as kilos of bread and kilos of potatoes (or even litres of milk). This point is
further elaborated in the discussion of typographic checks below.
?Adding a check digit to the codes of some important variables (such as the occupation or activity of a person, or the nature of the consumption item). A
check digit is a number or letter that can be deducted from the rest of the digits in
the code by means of arithmetic operations performed at data entry time. A
common check digit algorithm is the following: multiply the last digit in the code
by 2, the second from last by 3, etc. (if the code is longer than six digits, repeat
the sequence of multipliers 2, 3, 4, 5, 6, 7), and add the results. The check digit is
the difference between this sum and the nearest higher multiple of 11 (the number
10 is represented by the letter K). Check digit algorithms are constructed so that
the more common coding mistakes, such as transposing or omitting digits, will
produce the wrong check digit.
C. Operational strategies for data entry and data editing
12. Many household surveys still consider data entry and editing as activities to be conducted in central locations, after the survey is fielded, whereas other surveys are already implementing the concept of integrating data entry into field operations. In the near future, the idea may evolve towards the application of computer-assisted interviewing. The present section discusses the organizational implications of the various strategies and the common and specific features of the data entry and data editing software developed under each alternative.
13. Centralized data entry. Centralized data entry was the only known option before the emergence of microcomputers, and it is still used today in many surveys. It considers data entry an industrial process, to be conducted in centralized data entry workshops after the end of the interviews. The objective of the operation is to convert the raw material (the information on the paper questionnaires) into an intermediate product (machine-readable files) that needs to be further refined (by means of editing programs and clerical processes) in order that a so-called clean database may be obtained as a final product.
14. During the initial data entry phase, the priorities are speed and ensuring that the information on the files perfectly reflects the information gathered in the questionnaires. Data entry operators are indeed not expected to “think” about what they are doing, but rather to just faithfully copy the data given to them. Sometimes, the questionnaires are submitted to double-blind data entry, in order to ascertain that this is done correctly.
15. Until the mid-1970s, data entry was carried out with specialized machines having very limited capabilities. Although, at present, the process is almost always carried out with microcomputers that can be programmed with quality control checks, this capability is seldom used in practice. The prevalent belief is that few quality control checks should be included in the data entry process, since the operators are not trained to make decisions as to what to do if an error is found. Besides, the detection of errors and their solutions slow down the data entry process. This school of thought considers that quality control checks should be solely reserved for the editing process.
16. Data entry in the field. Starting in the mid-1980s, the integration of computer-based quality controls into field operations has been identified as one of the keys to improving the quality and timeliness of household surveys. These ideas were initially developed by the World Bank’s Living Standards Measurement Study (LSMS) surveys, and have been applied later to various other complex household surveys. Under this strategy, data entry and consistency controls are applied on a household-by-household basis as a part of field operations, so that errors and inconsistencies are solved by means of eventual revisits to the households.
17. The most important and direct benefit of integration is that it significantly improves the quality of the information, because it permits the correcting of errors and inconsistencies while the interviewers are still in the field rather than by office “cleansing” later. Besides being lengthy and time-consuming, office cleansing processes at best produce databases that are internally consistent but do not necessarily reflect the realities observed in the field. The uncertainty stems from the myriad of decisions - generally undocumented - that need to be made far from where the data are collected, and long after the data collection.
18. The integration of computer-based quality controls can also generate databases that are ready for tabulation and analysis in a timely fashion, generally just a few weeks after the end of field operations. In fact, databases may be prepared even as the survey is conducted, thus giving the survey managers the ability to effectively monitor field operations.
19. Another indirect advantage of integration is that it fosters the application of uniform criteria by all the interviewers and throughout the whole period of data collection, which is hard to achieve in practice with pre-integration methods. The computer indeed becomes an incorruptible and tireless assistant of the survey supervisors.
20. The integration of computer-based quality controls to field operations also has various implications for the organization of the survey, the most important being that it requires the field staff to be organized into teams. A field team is usually headed by a supervisor and includes a data entry operator in addition to two to four interviewers.
21. The organization of field operations depends on the technological options available. The two most used set-ups involve desktop and notebook computers and entail the following steps: ?Have the data entry operator work with a desktop computer in a fixed location (generally a regional office of the statistical agency,) and organize fieldwork so
that the rest of the team visits each survey location (generally a primary sampling
unit) at least twice, so as to give the operator time to enter and verify the
consistency of the data in between visits. During the second and subsequent visits,
the interviewers will re-ask questions where errors, omissions or inconsistencies
are detected by the data entry program.
?Have the data entry operator work with a notebook computer and join the rest of the team in its visits to the survey locations. The whole team stays in the location
until all the data are entered and certified as complete and correct by the data
entry program.
22. Both options have external requirements that need to be carefully considered by the survey planners and managers. One of them entails ensuring a permanent power supply for the computers, which may be an issue in poorly electrified countries. If desktops in fixed locations are used, this may require installing generators and ensuring that fuel for the generators is always available. If mobile notebooks are used instead, this may require the use of portable solar panels.
23. An obvious but important difference between the two strategies is that if computer-based quality controls are to be integrated into fieldwork, the data entry and editing program needs to be developed and debugged before the survey starts. With centralized data entry, this is also convenient (so that data entry can proceed in parallel with field operations,) but not absolutely necessary.
24. Paperless interviews. The use of hand-held computers to get rid of the paper questionnaires altogether is very appealing because of the advantages of automating certain parts of the interviews, such as skip instructions. However, although the technology has been available for almost 20 years, very little has been done to seriously apply this strategy to complex household surveys in developing countries. In fact, even in the most advanced national statistical agencies, paperless questioning has so far been restricted to relatively simple exercises, such as employment surveys and the collection of prices for the consumer price index.
25. A possible reason for this is that although paperless questioning lends itself well to interviews that follow a linear flow, with a beginning and an end, many household surveys conducted in developing and transition countries may require instead multiple visits to each household, separate interviews with each member of the household, or other procedures that are less strictly structured.
26. In spite of the absence of real empirical experience, certain observations about what needs to be taken into consideration in the design and implementation of a paperless questionnaire can be made:
?The data entry program interface will in some cases consist of a series of questions appearing one after the other on the computer screen, but in other cases
it will need to reproduce the structure and visual format of the paper
questionnaires, showing many data entry fields at the same time. This seems to be
particularly important in the modules on expenditure and consumption, where the
interviewer needs to “see” many consumption items simultaneously. The interface
must also allow for the possibility of marking questions in case of doubts, and it
should also make it possible to return to the household for a second interview
without repeating all questions.
?The questionnaire design process generally takes many months of work and involves many different people (subject-matter specialists, survey practitioners,
etc.). With a paper questionnaire, the process is carried out by preparing,
distributing, discussing and piloting various “generations” of the questionnaire
until the final version is agreed upon. The equivalent steps for something that will
never actually appear on paper still need to be defined.
?Interviewer training will need to be redesigned around the new technology. We know how to train interviewers to administer a paper questionnaire (theoretical
sessions, simulations, mock interviews, training manuals, etc.,) but little work has
been done to develop the equivalent techniques for a paperless survey.
?Finally, effective methods of supervision have to be developed. A large and rich set of procedures (visual inspection of the questionnaires, check-up interviews,
etc.) has evolved for over a half-century to verify the work done by interviewers
in the field. All these have been elaborated around the concept of a paper
questionnaire and need to be re-engineered for paperless interviewing. It is very
likely that the new technologies will offer completely different -- and possibly
much more powerful -- options for effective supervision; for instance, most hand-
held computers have voice recording capabilities that could be used to
automatically record random parts of the interview along with the data files. By
adding Global Position System (GPS) capabilities, it may also be possible to
automatically record the time and place of the interviews. Again, the details have
yet to be defined, field-tested and incorporated into the general scheme of survey
fieldwork.
D. Quality control criteria
27. Regardless of the strategy chosen for quality control, the data on the questionnaires need to be subjected to five kinds of checks: range checks, checks against reference data, skip checks, consistency checks and typographic checks. Here, we revise the nature of these checks and the way they can be implemented under the various operational set-ups.
28. Range checks are intended to ensure that every variable in the survey contains only data within a limited domain of valid values. Categorical variables can have only one of the values predefined for them on the questionnaire (for example, gender can be coded only as “1” for males or “2” for females); chronological variables should contain valid dates, and numerical variables should lie within prescribed minimum and maximum values (such as 0 to 95 years for age.)
29. A special case of range checking occurs when the data from two or more closely related fields can be checked against external reference tables. Some common situations involve the following:
?Consistency of anthropometric data. In this case, the recorded values for height, weight and age are checked against the World Health Organization’s standard
reference tables. Any value for the standard indicators (height-for-age, weight-
for-age and weight-for-height) that falls more than three standard deviations from
the norm should be flagged as a possible error so that the measurement can be
repeated.
?Consistency of food consumption data. In this case, the recorded values for the food code, the quantity purchased and the amount paid are checked against an
item-specific table of possible unit prices.
30. Even when data are entered in centralized locations, it is generally convenient to detect and correct range errors in the initial data entry phase, rather than postpone this control for the editing phase, because range errors are often a result of the data entry operation itself rather than of interviewer mistakes. An error flag, such as a beep and a flashing field on the screen, may be set off when an out-of-range value is entered. If the error is merely typographical, the data entry operator can correct it immediately. It should, however, be possible to override the flag if the value entered represents what is on the questionnaire. In that case, an error report should be made so that the clerical staff can correct the error later by inspecting the questionnaire (or by the interviewer during a second interview, if the data are being entered in the field.) In the meantime, the suspect data item may be stored in a special format that registers its questionable status.
31. Skip checks. These verify whether the skip patterns have been followed appropriately. For example, a simple check verifies that questions to be asked only of schoolchildren are not recorded for a child who answered no to an initial question on school enrolment. A more complicated check would verify that the right modules of the questionnaire have been filled in for each respondent. Depending on his or her age and gender, each member of the household is supposed to answer (or skip) specific sections of the questionnaire. For instance, children less than 5 years of age should be measured in the anthropometric section but the questions about occupation are not asked of them. Women aged 15-49 years may be included in the fertility section but men may not be.
32. Sometime in the future, computer-assisted (paperless) interviews for surveys in developing countries may become common, and then the skipping scheme will possibly be controlled by the data entry program itself, at least in some cases. However, under the other operational set-ups (central data entry locations and data entry in the field), the data entry program should not actually follow the skip patterns on its own. For example, if the answer no is entered to the question, Are you enrolled in school?, the fields in which to enter data about the kind of school attended, grade in school and so on, should still be presented to the data entry operator. If there are answers actually recorded on the questionnaire, they can then be entered and the program will flag an incorrect skip. The supervisor or interviewer (or the centralized editing clerical staff) can determine the nature of the mistake at a later time. It may well be that
the no was supposed to be a yes. If the data entry program had automatically skipped the following fields, the error would not have been detected or remedied.
33. Consistency checks. These checks verify that values from one question are consistent with values from another question. A simple check occurs when both values are from the same statistical unit, for example, the date of birth and age of a given individual. More complicated consistency checks involve comparing information from two or more different units of observation.
34. There is no natural limit imposed on the number of consistency checks that can exist. Well-written versions of the data entry program for a complex household survey may have several hundred of them. In general, the more checks that are defined, the higher the quality of the final data set. However, given that the time available to write the data entry and data editing programs is always limited (usually about two months), expertise and good judgement are required to decide exactly which should be included. Certain consistency checks that are applicable in almost all household surveys have proved to be particularly effective and thus have become something of a de facto standard. These encompass:
?Demographic consistency of the household. The consistency between the ages and genders of all household members is checked with a view to kinship relationships.
For example, parents should be at least (say) 15 years older than their children,
spouses should be of different genders, etc.
?Consistency of occupations. The presence or absence of certain sections should be consistent with occupations declared individually by household members. For
instance, the farming section should be present if and only if some household
members are reported as farmers in the labour section.
?Consistency of age and other individual characteristics. It is possible to check that the age of each person is consistent with personal characteristics such as
marital status, relationship to the head of the household, grade of current
enrolment (for children currently in school) or last grade obtained (for those who
have dropped out). For example, an 8-year-old child should not be in a grade
higher than third.
?Expenditures. In this case, several different consistency checks are possible. Only in a household where one or more of the individual records show that a child is
attending school should there be positive numbers in the household consumption
record for items such as school books and schooling fees. Likewise, only
households that have electrical service should report expenditures on electricity.
?Control totals. As said before, adding a control total wherever a list of numbers can be added is a healthy questionnaire design principle. The data entry program
should check that the control total equals the sum of the individual numbers.
35. Typographical checks. In the early years of survey data processing, checking for typographical errors was almost the only quality control conducted at the time of data entry. This was generally achieved by simply having each questionnaire entered twice, by two different operators. These so-called double-blind procedures are seldom used nowadays, on the grounds that the other consistency controls that are now possible make them redundant. However, this may in some cases be wishful thinking rather a solid assumption.
36. A typical typographical error consists in the transposition of digits (like entering “14” rather than “41”) in a numerical input. Such a mistake for age might be caught by consistency checks with marital status or family relations. For example, the questionnaire of a married or widowed adult age 41 whose age is mistakenly entered as 14, will show up with an error flag in the check on age against marital status. However, the same error in the monthly expenditure on meat may easily pass undetected, since either $14 or $41 could be valid amounts.
37. This emphasizes the importance of incorporating data management perspectives into the questionnaire design phase of the survey. Control totals, for instance, can significantly reduce typographical errors, because asking the interviewer to add up the figures with a pocket calculator is akin to entering them with double-blind data procedures. Check-digits can similarly be used for this purpose in some important variables. It is also possible to implement real double-blind methods for entering the data of certain parts of the questionnaire, but doing this for the whole questionnaire is both unnecessary and impractical – among other reasons, because modern data entry strategies are generally based on the work of a single data entry operator, not two different operators.
E. Data entry program development
38. The development of a good survey data entry and editing program is both a technique and
a craft. The present section discusses some of the development platforms that are available today to facilitate the technical aspects of the process and some of the subtler issues related to the design of interfaces for the data entry operators and the future users of the survey data sets.
39. Development platforms. There are many data entry and editing program development platforms available in the market, but few of them are specifically adapted to the data management requirements of complex household surveys. A World Bank review conducted in the mid-1990s had found that at that time two DOS-based platforms were adequate: the World Bank’s internally developed Living Standards Measurement Study (LSMS) package and the United States Bureau of the Census Integrated Microcomputer Processing System (IMPS) program. Both platforms have progressed since the review, in response to changing hardware and operating system environments. IMPS has been superseded by the Census and Survey Processing System (CSPro), a Windows-based application that provides some tabulation capabilities, besides serving in its primary role as a data entry and editing program development environment. The LSMS package has evolved towards LSD-2000, an Excel-based application that strives to develop the survey questionnaire and the data entry program simultaneously.
40. Both CSPro and LSD-2000 (or their ancestors) have proved their ability to support the development of effective data entry and editing programs for complex national household
surveys in many countries. These platforms are also easy to obtain and use. Almost any programmer -- in fact, almost anybody with a basic familiarity with computers -- can be expected to acquire in a couple of weeks the technical ability needed to initiate the development of a working data entry program.
41.Design principles. Unfortunately, development platforms cannot advise the programmers on just what data entry program needs to be developed. It may even be argued that the user-friendliness of the platforms risks making the development of inadequate data entry programs too easy. Confusing the mastery of the tools with the craft of putting the tools to good use is a mistake that survey managers should avoid by integrating both experienced programmers and subject-matter specialists in the development of the survey data entry and editing programs. Certain practical guidelines can be helpful in this regard:
?Data entry screen design. Data entry screens should look as much as possible like the corresponding pages of the questionnaire, but this rule has many exceptions.
For example, if the questionnaire presents personal questions in the form of a
matrix (with questions in rows and household members in columns, or the other
way around), it is generally better to prepare a separate data entry screen for each
person rather than to reproduce the paper grid on the computer screen. One reason
for not reproducing the whole grid on the screen is that the number of respondents
is variable. A stronger reason is that the statistical units observed are persons and
not households.
?Distinguishing between impossible and unlikely situations. The data entry program should of course flag as errors any situations that represent logical or
natural impossibilities (such as a girl’s being older than her mother), but it should
also react to situations that are not naturally impossible but very unlikely (such as
a girl’s being less than 15 years younger that her mother). Ideally, the data entry
program should assess the severity of the errors and react differently depending
on how serious they are, much as a human supervisor would if she or he was
visually inspecting the questionnaire. This kind of “smart” programming is
particularly important when data entry is integrated into field operations.
Unfortunately, some programmers do not invest enough effort in this issue. A
revealing sign is the tendency to always define the upper range of quantitative
variables as “999…”(as many nines as the data entry field is long). The
counterproductiveness of this practice is obvious: data entry fields should of
course be long enough to input even the largest possible values, but the upper
ranges should be small enough to flag unlikely values as possible outliers.
?Error reporting language. Some of the quality control criteria included in the data entry program may report on the errors detected by relatively simple means that
are either self-explanatory or require little training to be understood. For instance,
the LSMS data entry program reports range-checking errors by showing blinking
arrows pointing up “↑↑↑” or down “↓↓↓” along with the offending value,
depending on whether it is considered to be too low or too high. However, the
most complex consistency controls require much clearer and more explicit
reporting. For instance, a check on the demographic consistency of the household
could eventually produce a text such as “Warning: Lucy (ID Code 05, a girl 9
years old) is unlikely to be the daughter of Mary (ID Code 02, a woman 21 years
old)”, ideally on a printout rather than on the computer screen only. This kind of
“smart and literate” programming may take longer than seemingly simpler
alternatives (such as using error codes), but it will save many hours of fieldwork
and field staff training, and it will also free the programmers themselves from the
burden of writing an error codebook.
?Variable codes. A complex household survey typically contains hundreds of variables. The programmers in charge of the data entry program will need to refer
to them by means of codes, according to the specific conventions of the
development platform used. It is important that a rational and simple coding
system be selected for this purpose from the beginning of the data entry program
development process, because this will facilitate the communication between
members of the development team, and also because it will save time in the
subsequent steps of preparation and dissemination of the survey data sets. Finding
a good coding system, however, can be harder than it seems. The process may
start easily enough, with the first few variables getting codes such as “AGE”,
“GENDER” and so forth, but may soon become unmanageable, as finding
adequate mnemonic codes becomes harder. A good option is to simply refer to the
section and question numbers on the questionnaire, without any intent to make the
codes self-explanatory (for example, if “Age” and “Gender” are variables 4 and 5
of Section 1, they could be coded as “S1Q4” and “S1Q5”, respectively).
?Data entry workloads. When data entry is integrated into field operations, the most natural work unit for data entry is the household. This is because under these
conditions the data entry operator always has only one or just a few
questionnaires to work with, and also because consistency controls and error
reporting are conducted on a household-by-household basis. In a central data
entry location, the workloads can be blocks of 10-20 households (such as survey
localities or PSUs). The idea is that: (a) the block should be entered by a single
data entry operator in a single computer in at most a couple of days; and (b) the
corresponding pack of questionnaires should be easily stored and retrieved at all
times.
F. Organization and dissemination of the survey data sets
42. The structure of the survey data sets must reflect the nature of the statistical units observed by the survey. In other words, the data from a complex household survey cannot be stored in the form presented in table XV.1 directly below, that is to say, as a simple rectangular file with one row for each household and columns for each of the fields on the questionnaire.
Table XV.1. Data from a household survey stored as a simple rectangular file
Variable 1 Variable 2 … Variable j … Variable m Household 1 ……
Household 2 ……
…
…
……
… … …
Household i …Datum i,j …
…
…
… … …
……
Household n ……
43. Such a structure (also known as a “flat file”) would be adequate if all of the questions referred to the household as a statistical unit, but as discussed before, this is not the case. Some of the questions refer to subordinate statistical units that appear in variable numbers within each household, such as persons, crops, consumption items and so forth. Storing the age and gender of each household member as different household-level variables would be both wasteful (because the number of variables required would be defined by the size of the largest household rather than by the average household size) and extremely cumbersome at the analytical stage (because even simple tasks such as obtaining the age-gender distribution would entail laboriously scanning a variable number of age-gender pairs in each household).
44. Both the CSPro and LSD-2000 platforms use a file structure that handles well the complexities that arise from dealing with many different statistical units, while minimizing storage requirements, and interfacing well with statistical software at the analytic phase.
45. The data structure maintains a one-to-one correspondence between each statistical unit observed and the records in the computer files, using a different record type for each kind of statistical unit. For example, to manage the data listed on the household roster, a record type would be defined for the variables on the roster and the data corresponding to each individual would be stored in a separate record of that type. Similarly, in the food consumption module, a record type would correspond to food items and the data corresponding to each individual item would be stored in separate records of that type.
46. The number of records in each record type is allowed to vary. This economizes the storage space required, since the files need not allow every case to be the largest possible.
47. In principle, only one record type is needed for each statistical unit, although sometimes more than one record type may be defined for the same unit for practical reasons. For instance, questions on education and health may be stored in two separate record types, even if the statistical unit is the person in both cases.
48. Each individual record is uniquely identified by a code in three or more parts. The first part is the "record type", which appears at the beginning of each record. It tells whether the information is, for example, from the cover page, or the health module, or for food expenditures. The record type is followed - in all records - by the household number. In most record types, a third identifier will be necessary to distinguish between separate statistical units of the same kind within the household, for instance, the person's identification number or the code of the expenditure item. In a few cases, there will be only one unit for the level of observation and thus the third identifier will be unnecessary. For example, housing characteristics are usually gathered for only one home per household. In a few cases, there may be an additional, fourth code. For example, the third identifier might be the household enterprise, and the fourth code would apply to each piece of equipment owned for each enterprise.
49. After the identifiers, the actual data recorded by the survey for each particular unit follow, recorded in fixed-length fields in the same order as that of the questions in the questionnaire. All data are stored in the standard American Standard Code for Information Interchange (ASCII) format.
50. The survey data sets need to be organized only as separate flat files (one for each record type) for dissemination, because the fixed-length field format of the native structure is also adequate for transferring the data to standard Database Management Systems (DBMSs) for further processing, or to standard statistical software for tabulation and analysis. Transferring the data to DBMSs is very easy because the native structure translates almost directly into the standard database format (DBF) that is accepted by all of them as input for individual tables (in this case, the record identifiers act as natural relational links between tables.)
51.Dissemination also requires that the structure of each record type be properly documented in a so-called Survey Codebook, which needs to be given to any user interested in working with the data sets. The codebook should clearly specify the position and length of each variable in the record. For categorical variables, it should also specify the encoding. The figure XV.1 below presents a page of the Nepal Living Standards Survey codebook (the encodings of certain variables were abridged).
52. Both the CSPro and LSD-2000 platforms permit producing the survey codebook as a by-product of the data entry program development process. LSD-2000 also provides interfaces to convert the data entry files into DBF files and to transfer the data into the most commonly used statistical software (Ariel, CSPro, SAS, SPSS and Stata). This emphasizes the importance of defining a variable encoding system carefully at the data entry program development phase: if this is done well, the survey analysts will be able to immediately use the survey data when the data sets become available.
G. Data management in the sampling process
53. The present section discusses the role of data management in the design and implementation of household survey samples. It contains recommendations for the computerization of sampling frames and for conducting the first stages of sampling selection, including practical methods for implicit stratification and sampling of primary sampling units (PSUs) with probability proportional to size (PPS). The development of a database with the penultimate sampling units as a by-product of the prior sampling stages is discussed, emphasizing its role as a management tool while the survey is fielded, and how its contents can
be updated with field-generated information (such as the results of the household listing operation and the data on non-response) in order to generate the sampling weights to be used at the analytical stage.
54. Organization of the first stage sampling frame. The first-stage sampling units for many household surveys are the census enumeration areas (CEAs) defined by the most recently available national census. Creating a computer file with the list of all CEAs in the country is a convenient and efficient way to develop the first-stage sampling frame. Except in countries where the number of CEAs is massive (such as Bangladesh with over 80,000), the best way to do this is with a spreadsheet program such as Excel, with one row for each CEA, and columns for all the information that may be required. It must include the full geographical identification of the CEA and a measure of its size (such as the population, the number of households or the number of dwellings). It is generally more convenient to create a different worksheet for each of the sample strata. Figure XV.2 below shows how a first-stage sampling frame could look in the “Forest” stratum of a hypothetical country (the Excel screen has been split into two windows to show the first and last CEAs simultaneously).
Figure XV.2. Using a spreadsheet as a first-stage sampling frame
55. In this example, the 1,326 CEAs in the Forest stratum are identified by means of the geographical codes and names of the country’s administrative divisions (provinces and wards) and by a serial number within each ward. The sampling frame also contains the number of households and the population of each CEA at the time of the census, and indicates whether the CEA is urban or rural.
56. Before proceeding with the next steps of sampling selection, it is critical to verify that the sampling frame is complete and correct by checking the population figures with the census totals published by the statistical agency. It is also important to verify that the size of all CEAs is sufficiently large to permit their use as primary sampling units. If the sample design calls for
penultimate-stage clusters of, for example, 25 households each, it will not be possible to meet that requirement in CEAs of fewer than 25 households. In that case, small CEAs should be combined with geographically adjacent CEAs to constitute primary sampling units. This process may be tedious if the quest for neighbouring CEAs has to be conducted by hand, by continuous reference to the census maps. However, since statistical agencies often assign the CEA serial numbers according to some geographical criterion (the so-called serpentine or “spiral” orderings), so that the CEAs that are neighbours in the spreadsheet are also neighbours in the territory, it is generally possible to make the combinations automatically in the spreadsheet. In our example, every CEA has over 30 households, so no grouping is needed. It should be noted, however, that the illustration above is somewhat unrealistic for effectuating this procedure because urban and rural CEAs are mixed in the numerical listing, a situation not likely to be encountered in an actual country. In other words, grouping of adjacent CEAs by computer cannot be effected when urban and rural CEAs are scattered in the list rather than grouped together. 57. Another step preceding the first sampling stage is deciding if the sampling frame needs to be sorted by certain design criteria in order to implicitly stratify the sample within each of the explicit strata. Administrative divisions are almost always used for this purpose but, in some cases, another criterion -- that is to say, urban/rural stratification -- may be considered even more important. Assuming that in our example, this is the case in respect of the urban/rural classification, the sampling frame needs to be sorted by urban/rural, then by province, after that by ward, and finally by the CEA serial number. This can be easily done with the “sort” command provided by the spreadsheet program in figure XV.3.
Figure XV.3. Implementing implicit stratification
58. Selecting primary sampling units with probability proportional to size. Most household surveys select the primary sampling units using probability proportional to size (PPS). When it is available in the sampling frame, the number of households in the CEA is generally used as a measure of its size, but in some cases the population or the number of dwellings can be used instead. We will now illustrate the PPS procedure, assuming that the design calls for the selection of 88 CEAs with probability proportional to the number of households (column G of the worksheet) in the Forest stratum (see figure XV.4).
59. First, create a new column in the spreadsheet, with the cumulated size of the CEAs. Enter the formula =I1+G2 in cell I2 and copy it all the way down to the last row of column I (notice that the last row in column I will contain the total number of households in the Forest stratum (110,388).
60. Second, create another column with the scaled cumulated size of the CEAs, multiplying the values in column I by the scaling factor 88/110,388 (the idea is to have a column that grows from zero to the number of CEAs to be selected, proportionally to the size of the CEAs; see figure XV.5). Enter the formula =I2*88/110388 in cell J2 and copy it all the way down to the last row of column J:
《模拟联合国》社团课 名称：模拟联合国科目：英语课程类型：选修 适合对象：高一、二学生课程开发类型：自编讲义授课教师：卢洋 一、课程简介： 模拟联合国（Model United Nations，简称MUN）是对联合国以及其它国际组织和机构的会议流程进行模拟的一种活动，简称模联。在模拟联合国会议中，参与者主要是青年学子们，他们扮演各国的外交官，依据联合国及其它国际组织的议事规则，讨论当今国际上的热点问题。作为各国代表，参与者在会议中代表的是一个“国家”（双代表制），在会议主席团的主持和引导下，他们通过演讲阐述“自己国家”的立场和观点，与其它国家的代表沟通与协作，共同解决矛盾与冲突。 二、课程意义： 模拟联合国社团课的目的在于使青年学生们对联合国的运作方式和议事规则有更加深入的了解，同时通过扮演各国外交官、模拟联合国会议流程、讨论国际热点问题的方式。同时，模拟联合国社团课着重于培养青年学生们进行有效演讲、有效辩论、有效沟通的能力以及锻炼参与者们解决问题的能力。此外，在模拟联合国会议中，各位代表所代表的国家并非是自己真正所属的国家，对其他国家政治、经济、外交政策以及一些具体问题的调研，有助于增进国家之间的理解以及加强对多元文化的认同及包容性。 三、课程目标： 1、了解联合国的相关知识及模拟联合国的基本知识； 2、能围绕某一过意热点话题，收集资料，并形成自己的观点，撰写立场文件； 3、掌握辩论和游说的技巧； 4、拓展国际视野、激发学习能力、培养领袖气质和合作精神； 5、培养团队合作精神，培养风度、气质、领导力； 6、提高沟通和表达的能力，增强社会责任感 四、课程内容： 1、了解联合国和“模拟联合国”相关知识 运用资料和视频简述“模拟联合国”会议的流程和联合国常识；运动资料说明“模拟联合国”会议文件写作要求、时事要点、逻辑分析常识。 2、撰写立场文件 简述搜集资料的方法和途径；分小组搜集资料并汇总；举例说明撰写立场文件的方法；根据某一话题独立撰写立场文件。 3、正式会议流程的学习 熟悉“模拟联合国”会议的具体流程；辩论技巧及游说技巧的专项学习；草案、修正案的撰写指导；选择话题开展完整的“模拟联合国”会议。 五、课程评价： 本课程以小组和个人相结合的方式开展评价，以等级制（A、B、C、D）来表示。 平时表现包括：1.上课发言和专注情况；2.分工合作情况；3.文件撰写质量； 4.正式活动时的活动表现。
小学数学课程标准 第一部分前言 数学是研究数量关系和空间形式的科学。数学与人类发展和社会进步息息相关，随着现代信息技术的飞速发展，数学更加广泛应用于社会生产和日常生活的各个方面。数学作为对于客观现象抽象概括而逐渐形成的科学语言与工具，不仅是自然科学和技术科学的基础，而且在人文科学与社会科学中发挥着越来越大的作用。特别是20世纪中叶以来，数学与计算机技术的结合在许多方面直接为社会创造价值，推动着社会生产力的发展。 数学是人类文化的重要组成部分，数学素养是现代社会每一个公民应该具备的基本素养。作为促进学生全面发展教育的重要组成部分，数学教育既要使学生掌握现代生活和学习中所需要的数学知识与技能，更要发挥数学在培养人的理性思维和创新能力方面的不可替代的作用。 一、课程性质 义务教育阶段的数学课程是培养公民素质的基础课程，具有基础性、普及性和发展性。数学课程能使学生掌握必备的基础知识和基本技能；培养学生的抽象思维和推理能力；培养学生的创新意识和实践能力；促进学生在情感、态度与价值观等方面的发展。义务教育的数学课程能为学生未来生活、工作和学习奠定重要的基础。 二、课程基本理念 1．数学课程应致力于实现义务教育阶段的培养目标，要面向全体学生，适应学生个性发展的需要，使得：人人都能获得良好的数学教育，不同的人在数学上得到不同的发展。 2．课程内容要反映社会的需要、数学的特点，要符合学生的认知规律。它不仅包括数学的结果，也包括数学结果的形成过程和蕴涵的数学思想方法。课程内容的选择要贴近学生的实际，有利于学生体验与理解、思考与探索。课程内容的组织要重视过程，处理好过程与结果的关系；要重视直观，处理好直观与抽象的关系；要重视直接经验，处理好直接经验与间接经验的关系。课程内容的呈现应注意层次性和多样性。 3．教学活动是师生积极参与、交往互动、共同发展的过程。有效的教学活动是学生学与教师教的统一，学生是学习的主体，教师是学习的组织者、引导者与合作者。 数学教学活动应激发学生兴趣，调动学生积极性，引发学生的数学思考，鼓励学生的创造性思维；要注重培养学生良好的数学学习习惯，使学生掌握恰当的数学学习方法。 学生学习应当是一个生动活泼的、主动的和富有个性的过程。除接受学习外，动手实践、自主探索与合作交流同样是学习数学的重要方式。学生应当有足够的时间和空间经历观察、实验、猜测、计算、推理、验证等活动过程。 教师教学应该以学生的认知发展水平和已有的经验为基础，面向全体学生，注重启发式和因材施教。教师要发挥主导作用，处理好讲授与学生自主学习的关系，引导学生独立思考、主动探索、合作交流，使学生理解和掌握基本的数学知识与技能、数学思想和方法，获得基本的数学活动经验。 4．学习评价的主要目的是为了全面了解学生数学学习的过程和结果，激励学生学习和改进教师教学。应建立目标多元、方法多样的评价体系。评价既要关注学生学习的结果，也要重视学习的过程；既要关注学生数学学习的水平，也要重视学生在数学活动中所表现出来的情感与态度，帮助学生认识自我、建立信心。 5．信息技术的发展对数学教育的价值、目标、内容以及教学方式产生了很大的影响。数学课程的设计与实施应根据实际情况合理地运用现代信息技术，要注意信息技术与课程内容的整合，注重实效。要充分考虑信息技术对数学学习内容和方式的影响，开发并向学生提供丰富的学习资源，把现代信息技术作为学生学习数学和解决问题的有力工具，有效地改进教与学的方式，使学生乐意并有可能投入到现实的、探索性的数学活动中去。 三、课程设计思路 义务教育阶段数学课程的设计，充分考虑本阶段学生数学学习的特点，符合学生的认知规律和心理特征，有利于激发学生的学习兴趣，引发数学思考；充分考虑数学本身的特点，体现数学的实质；在呈现作为知识与技能的数学结果的同时，重视学生已有的经验，使学生体验从实际背景中抽象出数学问题、构建数学模型、寻求结果、解决问题的过程。 按以上思路具体设计如下。
Sponsors: Bangladesh, Bosnia, Switzerland, Turkey, Norway, United Arab Emirates, United States of America Acknowledging ?t he need to pre-empt future border disputes from escalating. Acknowledging ?t he right of sovereign States to determine internal security 1)With acknowledgement of the Responsibility to Protect Commends ?t he success of American efforts of the Afghan Police Training program. Further commends ?t he continuing military and strategic advice provided by American forces in Northern Africa Recognises ?t hat state self-determination and sovereign policy on border security is the most preferred mechanism in meaningful policy regarding border security. Noting ?t hat whilst such a preference may exist, not all states, particularly in armed conflict zones are severely underfunded and under-resourced making meaningful and enforceable border security policy nigh impossible.
小学数学新课标解读 《全日制义务教育数学课程标准（修定稿）》（以下简称《标准》）是针对我国义务教育阶段的数学教育制定的。根据《义务教育法》.《基础教育课程改革纲要（试行）》的要求，《标准》以全面推进素质教育，培养学生的创新精神和实践能力为宗旨，明确数学课程的性质和地位，阐述数学课程的基本理念和设计思路，提出数学课程目标与内容标准，并对课程实施（教学.评价.教材编写）提出建议。 《标准》提出的数学课程理念和目标对义务教育阶段的数学课程与教学具有指导作用，教学内容的选择和教学活动的组织应当遵循这些基本理念和目标。《标准》规定的课程目标和内容标准是义务教育阶段的每一个学生应当达到的基本要求。《标准》是教材编写.教学.评估.和考试命题的依据。在实施过程中，应当遵照《标准》的要求，充分考虑学生发展和在学习过程中表现出的个性差异，因材施教。为使教师更好地理解和把握有关的目标和内容，以利于教学活动的设计和组织，《标准》提供了一些有针对性的案例，供教师在实施过程中参考。 二、设计理念 数学是研究数量关系和空间形式的科学。数学与人类的活动息息相关，特别是随着计算机技术的飞速发展，数学更加广泛应用于社会生产和日常生活的各个方面。数学作为对客观现象抽象概括而逐渐形成的科学语言与工具，不仅是自然科学和技术科学的基础，而且在社会科学与人文科学中发挥着越来越大的作用。数学是人类文化的重要组成部分，数学素养是现代社会每一个公民所必备的基本素养。数学教育作
为促进学生全面发展教育的重要组成部分，一方面要使学生掌握现代生活和学习中所需要的数学知识与技能，一方面要充分发挥数学在培养人的科学推理和创新思维方面的功能。 义务教育阶段的数学课程具有公共基础的地位，要着眼于学生的整体素质的提高，促进学生全面.持续.和谐发展。课程设计要满足学生未来生活.工作和学习的需要，使学生掌握必需的数学基础知识和基本技能，发展学生抽象思维和推理能力，培养应用意识和创新意识，在情感.态度与价值观等方面都要得到发展；要符合数学科学本身的特点.体现数学科学的精神实质；要符合学生的认知规律和心理特征.有利于激发学生的学习兴趣；要在呈现作为知识与技能的数学结果的同时，重视学生已有的经验，让学生体验从实际背景中抽象出数学问题.构建数学模型.得到结果.解决问题的过程。为此，制定了《标准》的基本理念与设计思路。 基本理念 数学课程应致力于实现义务教育阶段的培养目标，体现基础性.普及性和发展性。义务教育阶段的数学课程要面向全体学生，适应学生个性发展的需要，使得：人人都能获得良好的数学教育，不同的人在数学上得到不同的发展。课程内容既要反映社会的需要.数学学科的特征，也要符合学生的认知规律。它不仅包括数学的结论，也应包括数学结论的形成过程和数学思想方法。课程内容要贴近学生的生活，有利于学生经验.思考与探索。内容的组织要处理好过程与结果的关系，直观与抽象的关系，生活化.情境化与知识系统性的关系。课程内容
模拟联合国大会流程 委员会结构 Committee Structure 一个委员会由一个主席团（Members of the Dais）负责。主席（The Chair）主持会议，主席的任务是按照联合国正式的程序规则监督并推动会议进程(Oversees Debate and Guides Delegates Through the Rules of Procedure)。会议指导(Director)负责审阅代表提交的会议文件。主席助理(Rapporteur)负责点名、录入发言人名单(Speakers' list)。 流程与规则 Rules of Procedures 一、正式辩论 Formal Debate 1、点名 Roll Call 在这一阶段，主席助理会按国家字母顺序依次点出国家名，被点到的国家举起国家牌(Placard)，并回答：“present(到)”。 E.g. The rapporteur: Honorable delegates, now we are going to have the roll call. Those countries called please raise your placards and answer PRESENT. Afghanistan...... 2、确定议题 Setting the Agenda 本次会议将会有两个议题供代表们选择，代表们通过讨论、投票，确定出要首先讨论的议题。在确定议题的阶段，主席分别在赞成首先讨论议题A和赞成首先讨论议题B的国家中，各随机点出3名代表（即，共6名）进行发言，阐述选择先讨论该议题的原因、动机等，发言时间为90秒。6名代表都发言完毕后，将进行投票，投票原则为简单多数，即50％＋1。 E.g. The Chair: Since we have two topic areas, we are going to set the agenda first. Those countries in favor of talking about topic area A first please raise your placards... China, Russia, Singapore, thanks. Those countries in favor of talking
小学数学课程标准 一、总目标 通过义务教育阶段的数学学习，学生能： 1. 获得适应社会生活和进一步发展所必需的数学的基础知识、基本技能、基本思想、基本活动经验。 2. 体会数学知识之间、数学与其他学科之间、数学与生活之间的联系，运用数学的思维方式进行思考，增强发现和提出问题的能力、分析和解决问题的能力。 3. 了解数学的价值，提高学习数学的兴趣，增强学好数学的信心，养成良好的学习习惯，具有初步的创新意识和实事求是的科学态度。 总目标从以下四个方面具体阐述： 知识技能 1.经历数与代数的抽象、运算与建模等过程，掌握数与代数的基础知识和基本技能。 2.经历图形的抽象、分类、性质探讨、运动、位置确定等过程，掌握图形与几何的基础知识和基本技能。 3.经历在实际问题中收集和处理数据、利用数据分析问题、获取信息的过程，掌握统计与概率的基础知识和基本技能。 4.参与综合实践活动，积累综合运用数学知识、技能和方法等解决简单问题的数学活动经验。 数学思考
1.建立数感、符号意识和空间观念，初步形成几何直观和运算能力，发展形象思维与抽象思维。 2.体会统计方法的意义，发展数据分析观念，感受随机现象。 3.在参与观察、实验、猜想、证明、综合实践等数学活动中，发展合情推理和演绎推理能力，清晰地表达自己的想法。 4.学会独立思考，体会数学的基本思想和思维方式。 问题解决 1.初步学会从数学的角度发现问题和提出问题，综合运用数学知识解决简单的实际问题，增强应用意识，提高实践能力。 2.获得分析问题和解决问题的一些基本方法，体验解决问题方法的多样性，发展创新意识。 3.学会与他人合作交流。 4.初步形成评价与反思的意识。 情感态度 1.积极参与数学活动，对数学有好奇心和求知欲。 2.在数学学习过程中，体验获得成功的乐趣，锻炼克服困难的意志，建立自信心。 3.体会数学的特点，了解数学的价值。 4.养成认真勤奋、独立思考、合作交流、反思质疑等学习习惯，形成实事求是的科学态度。 总目标的这四个方面，不是相互独立和割裂的，而是一个密切联系、相互交融的有机整体。在课程设计和教学活动组织中，应同时兼顾这四
立场文件 立场文件的英文是Position Paper，可定义为“表明一国在某一个问题上的基本观点和立场的文件”。立场文件反映了一个国家对特定话题的立场和解决建议，许多代表也会用立场文件作为大会中第一次发言的发言稿，由此可见其重要性。一篇立场文件只阐述对于一个议题的国家立场观点，如果一个委员会的议题有两个，那么就需要提交两份立场文件。一份立场文件的篇幅应尽量控制在一到两页。立场文件作用其他国家可以通过一国的立场文件来了解该国在这一议题上的基本立场，确定该国是否与自己的国家具有共同的目标和利益等等，这样便于各国协会协商和合作。也可以提醒本国代表自己的底线。立场文件同时也是会议开始阶段各国陈述观点的主要参照发言材料，但它并不是发言稿。立场文件的 格式和要点1）开头立场文件的开头应该包括以下部分：代表姓名（delegates） 代表来自的学校（schools）国家（country）委员会（committee）议题（topic）2）正文正文的内容一般安排如下：背景介绍：简要陈述议题。这一部分所占篇幅最小，只需简单概括该问题的历史，并提出讨论解决该问题的重要性即可。 过去的行动：这部分包括联合国在该问题上的决议或行动及本国在该问题上已经作出的 行动和决议等。本国的立场/政策/解决措施：这部分主要表明本国的立场，提供本国对于该问题的解决办法和措施，并提供理由等。3）篇幅书写语言为中文，字体用宋体，5号字，单倍行距。篇幅尽量控制在一到两页左右。4）充实内容前面介绍的正文内容安排只是一种参考。立场文件不是考试作文的八股文，其内容的安排顺序和文章结 构都可以根据代表国的实际情况调整。还可以从以下很多方面充实立场文件的内容： 对该国在这个议题上的立场的总体概括和对该国在此问题上的历史介绍该问题是怎样影响该国的该国在此问题上的政策和实施相关政策的理由 该国在此问题上签署的协议或批准的决议在此问题上别国的立场怎样影响该国 的立场国家领导在相关问题上发言的引言为支持该国在此问题上的对策的相关数据5）写作中需要注意的问题如非必要不需要陈述这个国家的详细信息（GDP、 人口、资源等）尽量使文体显得官方和正式，写作的时候再选词方面要特别注意 举出实例，而不是空讲政策和态度用脚注或尾注来表明所引用的资料（不强求） 立场文件作为会议要求的正式文件，在用词方面也有固定的要求。用词要求正式，避免口语化的表达。一般，立场文件都采用第三人称“中国认为（China thinks）、美国代表团相信（the US Delegation believes）”等等，而不用“你、我、他”。同时立场文件的表达应当简洁明了，尽量不要采用修辞、排比等过于文学的表达方式。由于篇幅有限，立场文件应该围绕议题和国家立场展开，确保每一句话都是为了表达国家立场服务。我们建议代表使用一个简明的架构来书写立场文件。你的工作不是把你所收集的大量信息填充到结构中，而是把你的观点、建议以及解决办法等等信息融合起来加以整理，使他们能够很好的融合到你的立场文件结构中，让你的立场文件更有建设性。一篇好的立场文件应包括以下几个方面：简要阐述议题，并说明代表国认为对这个议题对于其本国以及整个国际社会所具有的重要性。代表国所持有的立场、态度。可包括该国与此相关的国内事务及外交政策，以及过去在国际工作中所支持过的决议等等。对于联合国所采取过的主要措施代表国持怎样的态度。代表国对委员会的建议。可包括代表国认为有效可行的解决措施，以及国际社会应承当的角色。所以确切的说，立场文件更接近说明文，而不是一般认为的议论文或者辩论陈词。后者往往要求先明确表达观点（比如托福作文或GRE的Argument部分），然后旁征博引来证明观点的正确正当。但是立场文件的写作不是要通过具有冲击力的词汇说服阅读者，而是要通过合理引用客观资料说明问题，并在此基础上得出结论，更强调体现出一种分析的过程。换
福建省高等教育自学考试应用心理学专业（独立本科段） 《心理学研究方法》课程考试大纲 第一部分课程性质与目标《心理学研究方法》是福建省高等教育自学考试应用心理学专业（独立本科段）的一门专业基础必修课程，目的在于帮助考生了解和掌握心理学研究的理论基础和主要方法，检验考生对心理学研究理论基础与主要方法，检验考生对心理学研究方法的基本知识和主要内容的掌握水平与应用能力。 心理学研究的对象是心理现象。它的研究主题十分广泛：即涉及人的心理也涉及动物的心理；即涉及个体的心理也涉及群体的心理；即涉及有意识的心理也涉及潜意识的心理；即涉及与生理过程密切相关的心理也涉及与社会文化密切相关的心理。心理学研究是一种以经验的方式对心理现象进行科学探究的活动。由于心理学的研究方法是以经验的或实证的资料为依据的，因而使心理学与哲学相区别，也与人文学科相区别。 设置本课程的具体目的要求是，学习和掌握心理学研究方法的基本理论和基本技能，将有助于学生们理解心理学的基本概念、基本原理和基本理论。理解心理学家在探索心理与行动时所做的一切，有助于考生将来为心理学的发展做出有益的贡献。 第二部分考核内容与考核目标 第一编心理学研究基础 第一章心理学与科学 一、学习目的与要求 通过本章学习，要求考生了解心理学的性质，了解心理学科学研究的方法、特征及基本步骤，理解心理学研究的伦理问题和伦理规范。 二、考核知识点与考核目标 1、识记： （1）心理学的含义； （2）心理学科学研究的特征：系统性、重复性、可证伪性和开放性； （3）知情同意。 2、领会： （1）一般人探索世界的常用方法； （2）心理学研究主要包含哪几个步骤； （3）科学研究的开放性主要表现在哪几方面； 3、应用： （1）根据科学研究的特征来分析某些心理学的研究； （2）心理学研究的伦理问题及以人为被试的研究的伦理规范来分析是否可以在心理学研究中使用欺骗的方法。
模拟联合国会议规则流程 1.点名（Roll Call） ?点名的作用：确定到场国家总数，由此计算简单多数（1/2多数）和三分之二多数。 这些数据决定了表决通过的标准。 ?点名的方式：主席助理按照国家名单上的顺序点名，点到的国家高举国家牌并答“到(Present) ”。主席助理重复“某某国代表出席”。 2.设定议题（Setting the Agenda） ?设定议题的条件：一个委员会同时有两个或两个以上的议题待讨论。 ?设定议题的目的：确定多数国家感兴趣的议题为优先讨论的议题。 3.正式辩论（Formal Debate） ?辩论的概念：不同于平时的辩论比赛，模联中的发言、磋商、游说等均被视为辩论。 ?正式辩论：按照发言名单顺序进行的辩论叫正式辩论。 ?发言名单(Speakers’ List) ?发言名单的产生：确定议题后，正式辩论开始。主席会请需要发言的代表举国家牌，并随机点出国家名，当代表听到自己国家被点到后，放下国家牌。主席助理同步记 录，代表便可在大屏幕上看到发言名单。 ?发言名单的作用：供各国代表根据自己既定的发言主题发表讲话。 ?发言名单的缺点：各讲各的，主题分散，不利于促进共识的形成。 ?追加发言机会（国家名未在发言名单上或已经完成发言），可向主席台传意向条（Page）要求在发言名单上添加其代表国家，主席会将该国家名加在发言名单最后。 如代表已在发言名单上并还未发言，则不能追加发言机会。 ?发言时间：每位代表有120秒的初始发言时间（Speaking Time），可通过动议（Motion to Set Speaking Time）更改。 ?让渡：代表在发言时间内结束发言，可将剩余时间让渡 ?让渡给他国代表（Yield Time to Another Delegate）：让渡国A代表和被让渡国B代表协商一致后（传意向条，会前游说等），B代表在A代表剩余的时间内进行发言。如B代表发言结束还有时间剩余，不能再次让渡，主席将继续主持会议。 ?让渡给问题（Yield Time to Questions）：代表将剩余时间让渡给问题，主席会请需要提问的代表举牌，并随机点出代表进行提问，发言代表可在剩余时间内回答任 何被提出的问题，提问时间不占用剩余时间，提问内容必须根据发言者的意思来问。 这种方式可以体现发言代表的思辨、反应和口语表达能力。 ?让渡给评论（Yield Time to Comments）：代表将剩余时间让渡给评论，主席会请需要评论的代表举牌，并随机点出代表进行评论，让渡者没有权利再一次进行观点
小学数学新课程标准(修改稿)解读 一、前言 《全日制义务教育数学课程标准（修改稿）》（以下简称《标准》）是针对我国义务教育阶段的数学教育制定的。根据《义务教育法》、《基础教育课程改革纲要（试行）》的要求，《标准》以全面推进素质教育，培养学生的创新精神和实践能力为宗旨，明确数学课程的性质和地位，阐述数学课程的基本理念和设计思路，提出数学课程目标与内容标准，并对课程实施（教学、评价、教材编写）提出建议。 《标准》提出的数学课程理念和目标对义务教育阶段的数学课程与教学具有指导作用，教学内容的选择和教学活动的组织应当遵循这些基本理念和目标。《标准》规定的课程目标和内容标准是义务教育阶段的每一个学生应当达到的基本要求。《标准》是教材编写、教学、评估、和考试命题的依据。在实施过程中，应当遵照《标准》的要求，充分考虑学生发展和在学习过程中表现出的个性差异，因材施教。为使教师更好地理解和把握有关的目标和内容，以利于教学活动的设计和组织，《标准》提供了一些有针对性的案例，供教师在实施过程中参考。 二、设计理念 数学是研究数量关系和空间形式的科学。数学与人类的活动息息相关，特别是随着计算机技术的飞速发展，数学更加广泛应用于社会生产和日常生活的各个方面。数学作为对客观现象抽象概括而逐渐形成的科学语言与工具，不仅是自然科学和技术科学的基础，而且在社会科学与人文科学中发挥着越来越大的作用。数学是人类文化的重要组成部分，数学素养是现代社会每一个公民所必备的基本素养。数学教育作为促进学生全面发展教育的重要组成部分，一方面要使学生掌握现代生活和学习中所需要的数学知识与技能，一方面要充分发挥数学在培养人的科学推理和创新思维方面的功能 义务教育阶段的数学课程具有公共基础的地位，要着眼于学生的整体素质的提高，促进学生全面、持续、和谐发展。课程设计要满足学生未来生活、工作和学习的需要，使学生掌握必需的数学基础知识和基本技能，发展学生抽象思维和推理能力，培养应用意识和创新意识，在情感、态度与价值观等方面都要得到发展；要符合数学科学本身的特点、体现数学科学的精神实质；要符合学生的认知规律和心理特征、有利于激发学生的学习兴趣；要在呈现作为知识与技能的数学结果的同时，重视学生已有的经验，让学生体验从实际背景中抽象出数学问题、构建数学模型、得到结果、解决问题的过程。为此，制定了《标准》的基本理念与设计思路基本理念。 （一）总：六大理念 1、人人学有价值的数学，人人都能获得必需的数学，不同的人在数学上得到不同的发展 2、数学是人们生活、劳动和学习必不可少的工具，数学是一切重大技术发展的基础，数学是一种文化。 3、数学学习的内容要有利于学生主动地进行观察、实验、猜测、验证、推理、与交流，动手实践、自主探索与合作交流是学生学习数学的重要方式。 4、学生是数学学习的主人，教师是数学学习的组织者、引导者、合作者。 5、评价的目的—了解学生的数学学习历程，改进教师的教学；目标多元，方法多样；重过程，轻结果；关注情感态度。 6、把现代信息技术作为学生学习数学和解决问题的强有力的工具。 （二）分六大理念的解读: 数学课程应致力于实现义务教育阶段的培养目标，体现基础性、普及性和发展性。义务教育阶段的数学课程要面向全体学生，适应学生个性发展的需要，使得：人人都能获得良好的数学教育，不同的人在数学上得到不同的发展。 1、关于数学课程的功能 （1）“人人学有价值的数学”是指作为教育内容的数学，应当是适合学生在有限的学习时间里接触、了解和掌握的数学。 怎样理解有价值的数学?
策划编号：YT-FS-3912-17 模拟联合国大会活动策划 书(完整版) Develop Detailed Rules Based On Expected Needs And Issues. And Make A Written Plan For The Links To Be Carried Out T o Ensure The Smooth Implementation Of The Scheme. 深思远虑目营心匠 Think Far And See, Work Hard At Heart
模拟联合国大会活动策划书(完整 版) 备注：该策划书文本主要根据预期的需求和问题为中心，制定具体实施细则，步骤。并对将要进行的环节进行书面的计划，以对每个步骤详细分析，确保方案的顺利执行。文档可根据实际情况进行修改和使用。 一．活动主题 “模拟联合国大会”活动 二．活动目的 旨在通过本次活动来增加同学们对联合国组织的认识，同时培养让大家以国际的眼光来观察问题的能力，提高同学们组织策划管理能力和自身交际能力。 三．活动对象 电子学院全体学生 四．活动时间 （1）学院选拔时间：XX.11.18——XX.11.21 （2）全校活动时间：XX.12.5下午1：30 五．活动开展
（一）学院选拔 （1）宣传方式： 大一新生中到班宣传 （2）选拔方式： 1.自己选取一则英语小演讲进行演讲； 2.接受现场评委临场提问之后有评委通过综合评定，选出两名选手代表电子学院参加全校活动。 （二）全校活动 （1）活动议题： 农业发展和食品安全 （2）活动目标： 激发热情 generating young passion 社会责任 social responsibility 想象创新 imagination for innovation 勇于尝试 willing to try 追求卓越 excellence in execution （3）活动大概： 学院自己内部选出两个代表（一个是正外交官，
解读《义务教育小学数学课程标准》（2011年版）一 【新旧课标比较】与旧课标相比，新课标从基本理念、课程目标、内容标准 到实施建议都更加准确、规范、明了和全面。具体变化如下： 一、总体框架结构的变化 2001年版分四个部分：前言、课程目标、内容标准和课程实施建议。 2011年版把其中的“内容标准”改为“课程内容”。前言部分由原来的基本理念和设计思路，改为课程基本性质、课程基本理念和课程设计思路三部分。 二、关于数学观的变化 2001年版： 数学是人们对客观世界定性把握和定量刻画、逐渐抽象概括、形成方法和理论，并进行广泛应用的过程。 数学作为一种普遍适用的技术，有助于人们收集、整理、描述信息，建立数学模型，进而解决问题，直接为社会创造价值。 2011年版： 数学是研究数量关系和空间形式的科学。 数学作为对于客观现象抽象概括而逐渐形成的科学语言与工具。 数学是人类文化的重要组成部分，数学素养是现代社会每一个公民应该具备的基本素养。 三、基本理念“三句”变“两句”，“6条”改“5条” 2001年版“三句话”： 人人学有价值的数学，人人都能获得必需的数学，不同的人在数学上得到不同的发展。 2011年版“两句话”： 人人都能获得良好的数学教育，不同的人在数学上得到不同的发展。 “6条”改“5条”： 在结构上由原来的6条改为5条，将2001年版的第2条关于对数学的认识整合到理念之前的文字之中，新增了对课程内容的认识，此外，将“数学教学”与“数学学习”合并为数学“教学活动”。 2001年版：数学课程——数学——数学学习——数学教学活动——评价——现代信息技术 2011年版：数学课程——课程内容——教学活动——学习评价——信息技术 四、理念中新增加了一些提法 要处理好四个关系 数学课程基本理念（两句话） 数学教学活动的本质要求 培养良好的数学学习习惯 注重启发式 正确看待教师的主导作用 处理好评价中的关系
模拟联合国会议主要流程 一、点名 roll call 在这一阶段，主席助理会按国家字母顺序依次点出国家名，被 点到的国家举起国家牌(placard)，并回答：present(到)。点名为 每一个session都需要做的。 二、确定议题 setting agenda 本次会议将会由两个议题供代表们选择，代表们通过讨论、投票，确定出要首先讨论的议题。在确定议题的阶段，主席分别在赞 成首先讨论议题A和赞成首先讨论议题B的国家中，各随机点出2名代表（即，共4名）进行发言，发言顺序为一正一反交叉进行。阐述选择先讨论该议题的原因、动机等，发言时间为90秒。4名代表都 发言完毕后，将进行投票，投票原则为简单多数,50％＋1。 三、发言名单Speakers’ list 代表们确定议题之后，正式辩论开始。主席会请需要发言的代 表举国家牌（也就是所有的国家都举牌），并随机读出国家名，代表们发言的顺序即主席点名的顺序，当代表们听到自己国家被点到之后，便放下国家牌。每个代表有120秒的发言时间（初始时间，可更改），代表可在大屏幕上看到发言名单。如果需要追加发言机会（国家名未在发言名单上或已经完成发言），代表可向主席台传意向条(page)要求在发言名单上添加其代表的国家，主席会将该过的名字 加在发言名单的最后。如果代表已在发言名单上，并且还没有发言，则不能在其发言之前追加发言机会。一旦发言名单上所有国家已发言，并且没有任何代表追加发言，会议直接进入投票表决阶段。 四、让渡时间 yield time
代表在发言时间内结束发言之后，可将剩余时间让渡（让渡时间仅出现在120秒的发言名单中），让渡对象如下： ——让渡给他国代表(yield time to another delegate)这是我们最提倡的一种让渡方式。让渡国a代表和被让渡国b代表私下协商一致后（传意向条，会前游说等），b代表在a代表剩余的时间内进行发言。如果b代表发言结束还有时间剩余，b代表不能将其再次让渡，主席将继续主持会议。 ——让渡给问题(yield time to questions) 一旦代表将剩余时间让渡给问题，主席会请需要提问的代表举牌，并随机点出代表进行提问，提问时间不占用剩余时间，提问内 容必须根据发言者的意思来问。发言代表可在剩余时间范围内回答 任何被提出的问题，提问不占用时间。这种让渡方式可以体现发言 代表的思辩、反应及口语表达能力，但如果代表水平还有一定差距，选择这种让渡方式就是比较不明智的。 ——让渡给评论(yield time to comments) 一旦代表将剩余时间让渡给评论，主席会请需要评论的代表举牌，并随机点出代表进行评论，让渡时间给评论的代表没有权利再 一次进行观点的陈述或者对评论进行反驳。这种让渡方式是比较冒 风险的。如果发言代表和盟国交涉成功让盟国进行有利评论，且盟 国保证他到时会举牌并让主席点到他，这样会对发言代表比较有利。但如果评论机会落入反对国手中，该国将会对发言代表的立场进行 反驳及批判，这样就会使发言代表进入比较被动的劣势状态。所以，请代表们慎重选择。 ——让渡给主席(yield time to the chair)
人教版小学数学四年级下册新课标解读 崔庙镇实验小学 四年级
人教版小学数学四年级下册新课标解读 一、教材的主要内容: 小数的意义与性质，小数的加法和减法，四则运算，运算定律与简便计算，三角形，位置与方向，折线统计图，数学广角和数学综合运用活动等。 其中小数的意义与性质，小数的加法和减法，运算定律与简便计算，以及三角形是本册的重点教学内容。 二、教材的学习目标 1、理解小数的意义和性质，体会小数在日常生活中的应用，进一步发展数感，掌握小数点位置移动引起小数大小变化的规律，掌握小数的加法和减法。 2、掌握四则混合运算的顺序，会进行简单的整数四则混合运算;探索和理解加法和乘法的运算定律，会应用它们进行一些简便运算，进一步提高计算能力。 3、认识三角形的特性，会根据三角形的边、角特点给三角形分类，知道三角形任意两边之和大于第三边以及三角形的内角和是 180°。 4、初步掌握确定物体位置的方法，能根据方向和距离确定物体的位置，能描述简单的路线图。 5、认识折线统计图，了解折线统计图的特点，初步学会根据统计图和数据进行数据变化趋势的分析，进一步体会统计在现实生活中的作用。
6、经历从实际生活中发现问题、提出问题、解决问题的过程，体会数学在日常生活中的作用，初步形成综合运用数学知识解决问题的能力。 7、了解解决植树问题的思想方法，培养从生活中发现数学问题的意识，初步培养探索解决问题有效方法的能力，初步形成观察、分析及推理的能力。 8、体会学习数学的乐趣，提高学习数学的兴趣，建立学好数学的信心。 9、养成认真作业、书写整洁的良好习惯。 三、教材的编写特点: 本册实验教材具有内容丰富、关注学生的经验与体验、体现知识的形成过程、鼓励算法多样化、改变学生的学习方式，体现开放性的教学方法等特点。同时，本实验教材还具有下面几个明显的特点。 1. 改进四则运算的编排，降低学习的难度，促进学生的思维水平的提高。 四则运算的知识和技能是小学生学习数学需要掌握的基础知识和基本技能。以往的小学数学教材在四年级时要对以前学习过的四则运算知识进行较为系统的概括和总结，如概括出四则运算的意义和运算定律等。对于这些相关的内容，本套实验教材在本册安排了“四则运算”和“运算定律与简便计算”两个单元。“四则运算”单元的教学内容主要包括四则混合运算和四则运算的顺序。而关于四则运算的意义没有进行概括，简化了教学内容，降低了学习的难度。
工作文件范文一 议题：中北非难民的接纳和安置问题 起草国：美利坚合众国 严重关切中北非地区局势， 斥责暴力和对平民使用武力， 关切那些被迫逃离中北非发生暴力冲突的国家境内暴力行为的难民的困境， 要求中北非国家担负起保护其人民的责任， 认为中北非的近期的武装冲突与革命中造成的人民流离失所的状况正在蔓延，并且越来越影响到周边国家的稳定安宁， 注意到欧洲联盟在难民接纳问题处理中的分歧， 欢迎联合国1970号和1973号决议，包括对存在暴力冲突的国家的近期制裁措施。 回顾中北非各国当局有责任保护其人民， 针对中北非难民的接纳和安置问题，美国提出： 1、认识到联合国难民署的职权的有限性，加之资金的缺乏，限制了其作用的充分发挥， 2、呼吁联合国难民署会员国在此决议通过之日起三十天内提交关于其融资问题的切实有效的决议， 3、决定对联合国难民署的职能做进一步的深入和稳固，加强监督和制约机制，建立完善的违约制裁措施： （1）要求各成员国在本决议通过之日起，六个月之内根据本国法律，配合联合国难民署有关规定，着重对难民问题的解决建立相关法律法规；（2）加强联合国与难民署的密切关系，形成统一机制，从而可以提升难民署的制约力量； 4、确认难民的国际流动对有关国家的影响，采取暂时应对机制，特定国家以特定标准接纳难民的方法，让难民的接纳可以成为一些国家经济发展的促进因素， 5、铭记国际法有关难民的处理原则，包括《难民公约》规定的“不推回”原则以及“给予以宽容和提供便利”原则， 6、谴责本着狭隘的国家利益观对返国的或来自邻国的难民加以迫害甚至屠杀的行为， 7、支持存在经济困难和治安状况恶化、生态环境遭到破坏的国家勇于承担国际责任，争取发达国家或本区域内强国大国的合作，共同处理难民问题， 8、针对突发性大规模难民潮，应建立地区性和国际性的预警应对机制，鼓励各国对话协商，完善监督制度，把危害降到最低， 9、敦促各国对于本国境内的长期难民在尊重难民自身意愿的情况下，使难民在就业、教育、医疗卫生等方面享有同等的权利并受到本国法律的保护， 10、希望欧洲国家对中北非的难民问题给予关注，尽量采取积极措施，根据本联盟内部的需要与具体的国家国情，尽快出台关于难民问题的规定， 11、鼓励难民通过有效途径尽快提高自身素质，投入到移入国家的经济建设和社会发展的潮流中， 12、鉴于一些国家的对难民准入的限制政策，通过相关国际机构的协商使各国在难民入境和临时庇护方面予以积极配合，使难民安全转入第三国安置或等到来源国危机解除后同意其自愿返遣的原则， 13、尊重非政府组织在解决难民问题上的积极贡献，鼓励国际社会对非政府组织的支持，加强各方对话以促进该问题的更好解决， 14、申明联合国将不断关注中北非的难民流动与安置问题，并准备审议上文第3条所列措施是否适当，包括届时视有关国家对难民问题的处理情况，根据需要，加强、修改、中止或解除这些措施， 15、强调，如果有必要采取补充措施，则须进一步做出决定， 16、决定继续积极处理此问题。 范文二 介于8月3日，各个国家的讨论，中国，俄罗斯，日本，尼日利亚，印度，加拿大提出对安置气候难民的解决方案。 结合巴西，瑞士等国家对于开辟新土地以为安置难民和中国，俄罗斯等国家照联合国分配，得出解决方案，步骤如下： 1．由联合国发出提议，并尊重各个国家的意愿，全世界每一个国家可以选择，是以资金资助开辟和建设新的土地以安置难民，或是以本国之力接受难民，以资金资助的国家必须拿出联合国理应分配给他的难民所需金额的总值，以及一定百分数的联合国从其他国家手中买取土地的手续费。 2．补充第一条，联合国会参照各个国家的经济能力和国情发展来分配各个国家应接受的难民总数，联合国一定以公平，公正为原则，进行分配，请各国监督。同时介于现在社会上以没有无领土主权并且可供于人类居住的土地，联合国会用意向于资助国家的资金向国土大国进行租赁（例如：加拿大，澳大利亚），为保证被租赁国的领土完整，所有被分配去租赁国的难民必须学习当地的法律并严格遵守，如有严重伤害
心理学研究方法复习题 一、重要概念 1、研究的效度：即有效性，它是指测量工具或手段能够准确测出所需测量的心理特质的程度。 2、内部一致性信度：主要反映的是测验内部题目之间的信度关系，考察测验的各个题目是否测量了 相同的内容或特质。内部一致性信度又分为分半信度和同质性信度。 3、外推效度：实验研究的结果能被概括到实验情景条件以外的程度。 4、半结构访谈：半结构化访谈指按照一个粗线条式的访谈提纲而进行的非正式的访谈。该方法对访谈 对象的条件、所要询问的问题等只有一个粗略的基本要求，访谈者可以根据访谈时的实际情况灵活地做出必要的调整，至于提问的方式和顺序、访谈对象回答的方式、访谈记录的方式和访谈的时间、地点等没有具体的要求，由访谈者根据根据情况灵活处理。 5、混淆变量：如果应该控制的变量没有控制好，那么，它就会造成因变量的变化，这时，研究者选定 的自变量与一些没有控制好的因素共同造成了因变量的变化，这种情况就称为自变量混 淆。 6、被试内设计：每个被试接受接受自变量的所有情况的处理。 7、客观性原则：是指研究者对待客观事实要采取实事求是的态度，既不能歪曲事实，也不能主观臆断。 8、统计回归效应：在第一次测试较差的学生可能在第二次测试时表现好些,而第一次表现好的学生则 可能相反,这种情形称为统计回归效应.。统计回归效应的真正原因就是偶然因素变化导致的随机误差，以及仅仅根据一次测试结果划分高分组和低分组。 9、主体引发变量：研究对象本身的特征在研究过程中所引起的变量。 11、研究的信度：测量结果的稳定性程度。换句话说，若能用同一测量工具反复测量某人的同一种心理特质，则其多次测量的结果间的一致性程度叫信度，有时也叫测量的可靠性。 12、分层随机抽样：它是先将总体各单位按一定标准分成各种类型（或层）；然后根据各类型单位数与总体单位数的比例，确定从各类型中抽取样本单位的数量；最后，按照随机原则从各类型中抽取样本。13、研究的生态效度：生态效度就是实验的外部效度，指实验结果能够推论到样本的总体和其他同类现象中去的程度，即试验结果的普遍代表性和适用性。 14、结构访谈：又称为标准化访谈，指按照统一的设计要求，按照有一定结构的问卷而进行的比较正式的访谈，结构访谈对选择访谈对象的标准和方法、访谈中提出的问题、提问的方式和顺序、访谈者回答的方式等都有统一的要求。 15、被试间设计：要求每个被试者（组）只接受一个自变量处理，对另一被试者（组）进行另一种处理。