ࡱ> ` ubjbj .amT'''8' (\jnH)*(:*:*:*@,..H)j+j+j+j+j+j+j$lhnROj2+","22Oj:*:*dj7772:*:*)j72)j77cDOg:*<) 7;'R3pd&)jzj0jdfn3nLOgOgnh(./7`0|0...OjOjZ6...j2222 $$  Proposal for the documentation and handling of the repeated cross-national survey as well as of related comparison procedures 1. Working name for proposal RC-NS (The Repeated Cross-National Survey approach to comparison issues) 2. Working Group SIDOS / MetaDater / CDG 3. Alliance Member Sponsors Center for Surveys, Methods and Analysis (GESIS/ZUMA) Central Archive (GESIS/ZA) Centro de Investigaciones Sociolgicas (CIS) Finnish Social Science Data Archive (FSD) German Socio-Economic Panel (SOEP) Inter-university Consortium for Political and Social Research (ICPSR) Minnesota Population Center Norwegian Social Science Data Services (NSD) Roper Center Swedish Social Science Data Service (SDA) Swiss information and data archive service for the social sciences (SIDOS) Washington State University 4. Architect Reto Hadorn, SIDOS  HYPERLINK "mailto:reto.hadorn@sidos.unine.ch" reto.hadorn@sidos.unine.ch Submitted on 24.9.2005 5. Summary 5.1 Class of studies being addressed The proposal addresses the so called comparative studies. This label is yet a bit general, so the scope has to be stated in more precise terms. Any data analysis is comparative in nature, so the reference to the comparative character of the study is not very enlighten. A study is usually qualified as comparative when it is designed to compare data from distinct samples. Now, to make comparison sensible, some communality must exist between those samples. Three dimensions are considered: on the time dimension, the samples are drawn with the same method from the same universes at distinct points in time on the space dimension, the samples are drawn from distinct universes, yet defined along the same criteria, at a given point in time (where the point can be of various sizes) the data are collected using the same of a functionally equivalent data schema. The comparative study can be designed to be comparative; It can also be constructed post hoc, using data from various sources. In the first case, comparability will be insured by standard definition, regulations, control on the various levels involved (universe, sampling method, data schema); in the second case, comparability is insured by checking the similarity of universe, sampling method, space and time dimensions, data schema; comparability will usually require additional computations of various kinds (harmonization). No comparison is considered here, which would not be made inside a study. It is our epistemological and methodological position that the comparison of any pair of variables across the whole database makes little sense, even if the whole hierarchy of metadata from variable to study and project level is taken into account. Any decision about the comparability of two variables or the harmonization procedure is dependent on the scope of the comparison, i.e. a conceptual and a methodological framework. The so called 'harmonization study' is the appropriate place to give that information. This is the reason why we refer here to comparative studies and not to comparable variables. No comparison should be made or documented outside the framework given by a study. The easiest way to understand how comparability can be constructed is to approach it in the framework given by a comparative study by design. The reason is that the comparative study by design makes most of the dimensions of comparability explicit, as well as the limits imposed on comparability by the difficulty to attain functional equivalence between distinct (cultural) universes, coordination problems and methodological inconsistencies. The respective roles of the space and the time dimensions are another issue. When space and time are involved, space comes first, since you will have first a set of samples distributed over space, and then eventually a second wave, which opens the time dimension. Studies can involve the time dimension only, e.g. repeated cross-sections; but when both dimensions are considered, the space dimension is the first one to structure the 'dataset space'. Now, this space dimension can obviously involve a variety of space elements: countries, states, departments or cantons, cities. The rules, which are formulated in this proposal, do not depend on nature of the spatial unit. What makes the specificity of the so called comparative study is that it compares samples or the universes they are drawn from. The most general denomination for a comparative study over space would be of the kind "cross-sample study" or even "cross-universe study". Since the most common comparative study over space involves countries, we renounce to those barbarous denominations and propose to refer to the well known Cross-National Studies. The reader (and the modeler) must yet bear in mind that any kind of spatial units appropriate for sampling a universe can be involved. An additional advantage of the reference to the cross-national studies is the organizational complexity of the whole. Making the data from the various samples comparable supposes a social organization of the study, involving some coordination committee or agency and implementation teams or agencies. Implementation agencies may even enjoy some degrees of freedom in selecting optional modules or adding modules of their own. As a result, a comparative study involves distinct kind of 'authorities', whose activities cannot be described exactly in the same terms. This situation can be rendered referring to the cross-national study program on the one side and the local implementation study on the other side. These two kind of 'studies' can be seen as resulting from a differentiation of the well known 'study description', or at least the general part of that description. So one issue which will not be solved in the present proposal concerns the handling of the information related to the two levels, either by defining two distinct objects, the study program and the implementation study (the second being equivalent to the study description presently in use), or by defining a generalized study object, characterized by a type attribute and a generalized sub-element describing specific activities and authorities. The RC-NS approach demonstrates that comparison does not just involve data and technical criteria. We have insisted on the fact that any comparison bases on a broader framework, which defines the intention and scope of the comparison. Taking the RC-NS as a general paradigm, we can construct a formal correspondence between the study program and the harmonization study the implementation study and each single study, which is to be considered in a post hoc comparison. This allows seeing the data model for the RC-NS as an appropriate paradigm for all kind of comparisons. The harmonization study, which treats comparisons between unconnected studies and datasets, can be considered as a 'harmonization program'; a harmonization study must be defined to describe the intent of the comparisons to be made; this harmonization study occupies the same position in the overall structure as the program study of the cross-national program. A lot of structures and processes defined for the cross-national study program can be re-used for the harmonization study. As a matter of fact, the comparative study program and the harmonization study are not so far apart. Depending on the grade of control in a program/project, there is often a mixture of strict input harmonization (=standardization), ex-ante output harmonization (=planned standard harmonization where strict standards are not possible to implement), and even ex-post harmonization of the harmonization study type, where matching problems were not anticipated. In present data catalogues, series of comparable studies are commonly treated as 'collections' of 'families' of studies. These concepts are still important to account for older sets of datasets, which cannot be converted to the kind of structure proposed below. They are yet considered as too weak and undefined to be of any use when it comes to structure detail work with comparative survey programs. For this reason, the representation of collections of families of datasets is not covered the present proposal it does not present any difficulty anyway. The proposal is oriented at handling 'new' comparative programs (or starting a new work methodology for an existing program. Since the term 'comparative' being rather badly defined, we prefer to distinguish clearly the space and the time dimensions, using in the following and in the accompanying documents dedicated terms for describing data elements and relationships; for example we will write of the integration of country datasets on the space dimension and of the cumulation of datasets on the time dimension. This may seem a bit arbitrary, but rests on some practice. 5.2 Brief problem statement The problems to be solved are related to the life-cycle view on metadata. The data model for social science data should be overall consistent, so that the same data model can be used from the birth of a new data collection activity to the final use of the data. At whatever stage they are created, new metadata should be captured in a structure which makes them readily re-usable at later stages in the data life without any additional human intervention (copy/pasting, re-typing etc.) A stage by stage documentation may facilitate later operations to be done on the data and metadata; we will see that the appropriate documentation of variations at implementation stage can prepare the integration and cumulation work to be done later. Metadata is not primarily a description of data; it is the product of actions: defining concepts, a methodology, creating a questionnaire, questions, variable definitions, variable constructions, implementing a standard question, translating etc. etc. etc. Life is action. A life cycle is a succession of actions. Basically, the metadata in the store should just be the sub product of such actions and not the product of a special 'documentation' activity. These requirements apply to metadata of all types: concepts, general study information, general methodology, questionnaire and questions etc. etc. The data go through multiple stages in an RC-NS, which are not accounted for by DDI 2.0. Starting with a standard definition, data collection delivers a set of sample-specific (country-specific) datasets, which can be integrated over space or cumulated over time. The new data model shall describe those various states: standard definitions, various sorts of compound datasets, synthetic datasets. The RC-NS involves series of identical or similar questions and variables, related in various ways to the standard definition and over time. This characteristic opens three possibilities: Using those relationships and redundancies to make metadata capture more economical Documenting those relationships while capturing metadata, which makes them available as base information on questions and variables series Using that documentation of direct relationships between questions and variables to support integration and cumulation processes, while creating the synthetic datasets. In a similar way, the relationships between the various kind of studies must be defined, namely the survey program study and the local implementation studies. 5.3 Brief verbal summary of solution Given: the distinction between Projects, Studies and Datasets, the (conceptual or logical) distinction between Program Studies and Implementation Studies and a close relationship between Questions and Variable definitions (defined elsewhere), the solution consists in the definition of References between Studies (mainly between implementation studies and program studies) between Questions (implemented questions and standard questions, repeated questions and first occurrence of the question, standard question in the integrated dataset and country specific questions, standardized question in the cumulated dataset and various formulations over time) between Variables (same relationships as between questions). The references are documented in two modes: coded (closed list of variation types) textual (any kind of comment on the substance of the variation and the implications of the variation for data analysis). This is the place where the degree of functional equivalence achieved may be discussed. References are structural links and substantive information. As structural links, they integrate information all over the metadata base and allow for the publication of hyperlinked (navigable) metadata; they also support the integration and cumulation work necessary to produce the synthetic datasets. As substantive information, the references are available to deliver information in any kind of metadata publication. Space and time are defined on the study level as domains, formally similar to multilingual value domains. The composition of the two domains defines a dataset space. Single simple datasets, defined by a concrete sample, are positioned in the dataset space by their space and time coordinates, defined within the study specific domains. Sets of datasets sharing the same space and/or time coordinates are defined as compound datasets. Compound datasets, which can be used by software systems to support integration and cumulation work, have only that functional definition (they are not represented by a specific object). Prerequisites The proposal above constitutes the hard core of what comparative data structures are about. The proposed structure is yet supposed to work within a data model, where other domains are appropriately handled. The Study Description as defined in DDI 2.0 is not differentiated enough to map definition structures for more complex datasets. In the MetaDater project, this information was split up between three kinds or levels of information: Project information, which covers mainly funding and authoring information, Study information, defining the general data schema, and Dataset information, defining a specific data collection operation based on a real sample. A single Project may give birth to several distinct studies (say, interviews with managers, workers and consumers). On single study may be funded and produced by more than one project over time. One study (general data schema) can define one single dataset or several datasets. The proposal supposes also a close relationship between Questions and Variable definitions, expressing the fact that question structures mainly define the relationship between a question and the variables it feeds. An extensive description of this approach is available: 1_QVTypesText (timestamped)  HYPERLINK "http://www.sidos.ch/mmg/vi/html/Documents/1_QVTypesText_050707.doc" Word /  HYPERLINK "http://www.sidos.ch/mmg/vi/html/Documents/1_QVTypesText_050707.pdf" PDF General presentation of the issue and first attemt at a typology based on question levels and data types. Several types have to be further defined 2_QVTypesTable HYPERLINK "http://www.sidos.ch/mmg/vi/html/Documents/2_QVTypesTable.doc"Word / HYPERLINK "http://www.sidos.ch/mmg/vi/html/Documents/2_QVTypesTable.pdf"PDF The original typology, presented in the form of a table - large screen and A3-printer recommended 3_QVTypesModel HYPERLINK "http://www.sidos.ch/mmg/vi/html/Documents/3_QVTypesModel.doc"Word / HYPERLINK "http://www.sidos.ch/mmg/vi/html/Documents/3_QVTypesModel.pdf"PDF Proposal of a data model, which would allow storage of value domains, item lists, answer instances in various combinations and in a re-usable way. 4_QVTypesMovie - HYPERLINK "http://www.sidos.ch/mmg/vi/html/Documents/4_QVTypesMovie.ppt"PowerPoint Dynamic presentation of the various Q/V types and their rendering, using the proposed data model. Initially, it was a presentation at the Congress of e-social science in Manchester, June 2005. The discussion showed that there was a difference of perspective between the two approaches. The ID group seems to work on the instrument as a separate object (the instrument), containing sub-objects (response objects) which can be later linked to variables found in the data file. The MetaDater approach states that formulating questions, on takes responsibility for a definite variable structure, which can later be confronted to variable definitions in the data file for consistency checking. Further discussions may show that the perspectives are not so far apart from each other The RC-NS proposal supposes at least the multilingual capture of text elements belonging to the questions. This is a fundamental requisite for the documentation of data collected across multiple linguistic and cultural universes. Given the existence of multiple brands of single languages (Swiss French, French French, Belgian French to take just this example), the language identification fields should actually take the country into account. The data model must of course also include data elements informing systems using the data model about the languages to be expected in the metadata for a given study. Examples for a solution can be found in the MetaDater data model as well as in the  HYPERLINK "http://www.sidos.ch/mmg/vi/html/17.htm" VarInfo data model. Further developments must be thought of. The translation of an instrument is never a straightforward operation. Translation mismatches must be documented to be available to the data analyst. Documentation of translation operations starts on the translator's desk: the ultimate data model for full metadata should include the data elements necessary to account for more or less complex translation procedures (think of ESS' multiple translation and integration strategy) and for commenting on problems discussed. Translation is just one expression of a more fundamental search for functional equivalence between the stimuli adapted to distinct linguistic and cultural and institutional universes. It is the scope of the textual fields in the Reference data elements to store/provide information on strategies followed to attain equivalence and an evaluation of the actually achieved equivalence. External references may be necessary to provide more detailed information. The documentation of the distinct language versions of the instrument is a good start; actually, the whole bunch of metadata may be published in more than one language. Given that all multilingual publications of the metadata will not translate the same metadata elements, a device defining language policies is necessary. A language policy defines for a given authority the attribution of single metadata elements to language levels (e.g. instrument, variable level documentation, general study description etc. etc), and select a given subset of languages for each level. The proposal supposes at last a hyperlinked concept for variable construction. As recalled in the documents related to the proposal, where a harmonized variable is necessary, it must be computed separately in each of the single datasets involved. The derivation element in DDI 2.0 is quite appropriate. Another example can be found in the  HYPERLINK "http://www.sidos.ch/mmg/vi/html/1064.htm" VarInfo data model. Statements about other kinds of solutions Since single datasets in a repeated cross-national study are identified by their co-ordinates in space and time, the question raises, whether the work done defining time and geographic elements could be used in some way for the comparative datasets. Since any comparison is set within the context or framework of a study, a comparative study program or a harmonization study, the set of space and time coordinates is finite and actually a base information on the study considered. As a consequence, space and time coordinates will rather be handled as space and time 'domains' defined on the program study or harmonizing study level. Time and geographic elements can of course be successfully used in the search for possibly comparable data; once a set of 'comparable' data found by the automatic search procedure, the next step is a decision, whether or not the comparison between the data makes really sense from the researcher's point of view: only then, we can speak of comparable data, data which are comparable for the specific scope of the data analyst. The DDI often uses the 'statement' approach to structure various elements of information. So, the description of 'comparison' in the form of a 'comparability statement' appears as a legitimate way to describe comparisons. Unfortunately, it is an illustration of the variable-to-variable approach to comparison, and not of the comparison in context, advocated by this proposal. From our point of view, one comparability statement can refer only to one specific comparison scope, so there is place for a multiplicity of variable-to-variable comparability statements for the same pair of variables. There are practical limits also: imagine that you integrate into each variable-to-variable comparability statement information on all involved metadata levels The challenge of extending the DDI data model is less a modeling problem than an epistemological and conceptual issue regarding the definition of the conditions of comparison. This context involves (in short): sampled universes, population characteristics, sampling method, choice of observation objects (statistical units) non-response analysis, concepts and indicators, precise question formulations, interviewer instructions, translation and functional mismatches, treatment of missing values, harmonization hypotheses, constructions It is our conviction that this work needs the context of a study, where decisions on comparability are documented with reference to the scope of the comparison, because it will hardly be re-constructed starting just with two variables showing some resemblance. 6. Issues Basically, the following is all 'doing' something, not just describing; the description is basically a sub product of the action. Life-cycle means 'living data and metadata'. This means that we cannot stay with a conception of the DDI, which would show as a snapshot of the metadata taken at a specific point in time. We must think of a model rendering the development and evolution of metadata over time, i.e. the projection on a single plane of all elements needed at some time in the life cycle. The main issues are the following: Setting up the standard definition for a RC-NS, including standard questions and expected (eventually pre-harmonized) dataset definition. Using the standard definition to facilitate the definition of the metadata sets for the country datasets Defining references from the country questions and variables to the standard definition; references describe also possible variations on the standard. Making a global and detailed diagnosis about variations between standard and country Q/V. Defining an integrated dataset; adding harmonized variables, copying them into the country dataset definitions, defining the necessary computations in the local datasets, integrating harmonized data into the integrated dataset. All this is done using the references defined between related questions and variables. Using the first standard definition to facilitate the definition of the standard for a repetition of the survey; references from the second to the first standard created automatically, documenting the possible variations. Defining references on country level between the first and the second implementations of the standard; those references document possible variations. Making a global and detailed diagnosis about variations between the first and the second wave (more generally: multiple waves) Defining a cumulated dataset; adding harmonized variables, copying them into the wave specific dataset definitions, defining the necessary computations for the various waves, cumulating harmonized data into the cumulated dataset. All this is done using the references defined between related questions and variables. The procedure is the same for cumulating country datasets or integrated datasets. Defining a harmonization study, building references from candidate single studies to the harmonization study, defining possible harmonized variables, building references from variables which are candidates for harmonization. This results in a space x time structure of references along which integration and cumulation can be made the same way as in the planned RC-NS. Needless to say, there are some problems which can be addressed only then, when the real metadata will reveal the limits of this conceptual model. This is a normal situation in the development of a complex model and works since the publication of DDI 1.0. 7. End result sought To be able to support metadata capture, for all metadata involved in a RC-NS and related comparative procedures; to document accurately the relationships between the metadata elements and the variations between similar objects; to support efficiently the procedures involved in publishing metadata for the compound datasets; to support the analysis of variations over space and time and the integration/cumulation in synthetic files; to facilitate the publication of the full documentation for the synthetic files, based on the references built into the metadata model and the metadata entered and produced on the way incl. as a sub product of variable harmonization and computation procedures; to define and conduct harmonization studies, in which data from uncoordinated surveys are harmonized and compared. 8. Rationale for change RC-NS are among the most used datasets. RC-NS offer an appropriate framework for other kind of comparative projects. RC-NS are a type of datasets, where metadata collected on various levels can be inherited and composed in various ways for the publication of the metadata in their final state (integrated/cumulated) 9. Use cases The approach is defined in much detail in a  HYPERLINK "http://info1.za.uni-koeln.de/ddicdg/documents/RCNS_050706.zip" set of documents available on CDG's website. Please refer to them. 10. Background information Included in the documents referred to in topic 9. 11. Data elements The data elements are described in the style sheet 4_RC-NS.xls included in the set above. To be noted: other parts of the DDI have to be remodeled to let the RC-NS approach fit into. These are notably the study description and the question structure. Data elements are presented in the XML sheet in a way that refers to a relational model. The relationships between peer objects (Studies, Questions and Variables) may cause some problem since circular references should be avoided. Quoting Wendy Thomas (e-mail 23.9.2005): "What I meant to say was that some of your links might not be explicitly in the DDI XML primarily in that we are trying to avoid circular links. For instance a child element would point to a parent, but the parent would not point to the child. In the same way, sibling to sibling relationships would be established by both elements pointing to the same parent. This is primarily due to a decision made by the DDI SRG to keep pointers going in a single direction and to facilitate the addition of materials without requiring additional editing of established (persistent) modules." This difficulty may be overcome by the definition of a 'Reference' class, associated with RefRef Classes. The Reference Class describes a (possibly symmetrical) relationship between objects (id_Ref, ObjectType, FK_Source, FK_Target, RefType). Repeatable, placed on the Root. The RefRef Class is an element of each of the referenceable objects, which points to a specific Reference Element, identified by its id (id_RefRef, ObjectType, SourceOrTarget, id_Ref). At least two elements of the same type have RefRef elements pointing to the same Reference element. RH 26.9.2005     RC-NS 16.9.2005  ____________ - p.  PAGE 1 of  NUMPAGES 9 -  #0akħ{e{O{::(h6eh CJOJQJ^JaJmH sH +h6eh6e5CJOJQJ^JaJmH sH +h6eh!^5CJOJQJ^JaJmH sH +h6ehi 5CJOJQJ^JaJmH sH +h6eh 5CJOJQJ^JaJmH sH 8jh6eh CJOJQJU^JaJmHnHsH u(h6ehCJOJQJ^JaJmH sH (h6eh  CJOJQJ^JaJmH sH "hCJOJQJ^JaJmH sH    5 6 l  G c gdSgd]$a$gd gdauu   5 6 . / 2 Ӿ~i~T~龓龓~>+h6ehx>5CJOJQJ^JaJmH sH (h6eh CJOJQJ^JaJmH sH (h6eh6eCJOJQJ^JaJmH sH (h6eh CJOJQJ^JaJmH sH (h6ehSCJOJQJ^JaJmH sH +h6ehS5CJOJQJ^JaJmH sH (h6eh]CJOJQJ^JaJmH sH +h6ehXY5CJOJQJ^JaJmH sH +h6eh]5CJOJQJ^JaJmH sH   . / < = P y z _ ` KKL]^gd]S & FgdLgd gd]gdS2 ; < = O P Q Ӿ{_H{2+h6eh?5CJOJQJ^JaJmH sH ,h6eh]S0JCJOJQJ^JaJmH sH 7jh6eh]SCJOJQJU^JaJmH sH (h6eh]SCJOJQJ^JaJmH sH 1jh6eh]SCJOJQJU^JaJmH sH (h6eh CJOJQJ^JaJmH sH (h6ehuVCJOJQJ^JaJmH sH +h6ehx>5CJOJQJ^JaJmH sH +h6ehuV5CJOJQJ^JaJmH sH   ! # ; ӽ|gQg<'<(h6eh6eCJOJQJ^JaJmH sH (h6ehLCJOJQJ^JaJmH sH +h6eh]S5CJOJQJ^JaJmH sH (h6eh]SCJOJQJ^JaJmH sH +h6ehE5CJOJQJ^JaJmH sH +h6eh!5CJOJQJ^JaJmH sH (h6eh!CJOJQJ^JaJmH sH +h6ehuV5CJOJQJ^JaJmH sH +h6eh?5CJOJQJ^JaJmH sH +h6ehE5CJOJQJ^JaJmH sH ; ] &2Liq(MZ\]^7 TUԿԪjT?4?T?h6ehMAmH sH (h6ehMACJOJQJ^JaJmH sH +h6ehMA5CJOJQJ^JaJmH sH (h6eh TCJOJQJ^JaJmH sH (h6ehxM0CJOJQJ^JaJmH sH +h6ehWe5CJOJQJ^JaJmH sH (h6ehWeCJOJQJ^JaJmH sH (h6eh6eCJOJQJ^JaJmH sH (h6ehLCJOJQJ^JaJmH sH +h6ehL5CJOJQJ^JaJmH sH U[\^| UYi"?MRex땀jXCC(h6eh;KCJOJQJ^JaJmH sH "h6eCJOJQJ^JaJmH sH +h6eh6e5CJOJQJ^JaJmH sH (h6eh6eCJOJQJ^JaJmH sH (h6ehECJOJQJ^JaJmH sH +h6ehE5CJOJQJ^JaJmH sH +h6eh`n5CJOJQJ^JaJmH sH (h6ehMACJOJQJ^JaJmH sH (h6eh`nCJOJQJ^JaJmH sH ^]^5 {!! "Y$Z$'&(&((**gd Tgd]S & Fgd3= & FgdPF(gd`ngdMAx{|Salfi_cԿԿ鿪iSS+h6ehi5CJOJQJ^JaJmH sH +h6eh`n5CJOJQJ^JaJmH sH (h6ehiCJOJQJ^JaJmH sH +h6eh6e5CJOJQJ^JaJmH sH (h6eh6eCJOJQJ^JaJmH sH (h6eh;KCJOJQJ^JaJmH sH (h6eh`nCJOJQJ^JaJmH sH +h6eh;K5CJOJQJ^JaJmH sH %2;O4 5 ? U !z!{!""u"y"֖ցkV(h6eh`nCJOJQJ^JaJmH sH +h6eh3=5CJOJQJ^JaJmH sH (h6eh3=CJOJQJ^JaJmH sH (h6ehf{>CJOJQJ^JaJmH sH (h6eh6eCJOJQJ^JaJmH sH +h6ehi5CJOJQJ^JaJmH sH (h6ehiCJOJQJ^JaJmH sH (h6ehPF(CJOJQJ^JaJmH sH y""X$Y$Z$s$x$z$~$$%%&&'&(&e&m&y&Կn_O_:(h6eh BCJOJQJ^JaJmH sH h6ehV\5OJQJmH sH h6ehV\OJQJmH sH "h6eCJOJQJ^JaJmH sH (h6eh6eCJOJQJ^JaJmH sH (h6ehV\CJOJQJ^JaJmH sH (h6eh:^CJOJQJ^JaJmH sH (h6ehXCJOJQJ^JaJmH sH (h6eh`nCJOJQJ^JaJmH sH +h6eh`n5CJOJQJ^JaJmH sH y&&&&&.'((((T(((((((()))O)ԿԿԿԪkVkV@+h6eh]S5CJOJQJ^JaJmH sH (h6eh]SCJOJQJ^JaJmH sH (h6eh TCJOJQJ^JaJmH sH (h6eh BCJOJQJ^JaJmH sH (h6ehV\CJOJQJ^JaJmH sH (h6eh41CJOJQJ^JaJmH sH (h6ehUCCJOJQJ^JaJmH sH (h6ehXCJOJQJ^JaJmH sH +h6ehX5CJOJQJ^JaJmH sH O)P)X)))*"*********ԿԿԕjU@*+h6eh|5CJOJQJ^JaJmH sH (h6eh|CJOJQJ^JaJmH sH (h6eh!CJOJQJ^JaJmH sH +h6eh!5CJOJQJ^JaJmH sH (h6eh!CJOJQJ^JaJmH sH (h6eh3=CJOJQJ^JaJmH sH (h6eh6eCJOJQJ^JaJmH sH (h6eh TCJOJQJ^JaJmH sH (h6eh]SCJOJQJ^JaJmH sH +h6eh 5CJOJQJ^JaJmH sH ****++,-`/a////11B22!333j4k4l444gd| & F gd| & F gd. & FgdF)$gdKgd]*1+C++,,!,(,1,2,,,,,--- /땀jU?U+h6ehq5CJOJQJ^JaJmH sH (h6ehqCJOJQJ^JaJmH sH +h6eh[p5CJOJQJ^JaJmH sH (h6eh[pCJOJQJ^JaJmH sH (h6eh$CJOJQJ^JaJmH sH +h6eh|5CJOJQJ^JaJmH sH (h6eh|CJOJQJ^JaJmH sH +h6ehF)5CJOJQJ^JaJmH sH (h6ehF)CJOJQJ^JaJmH sH  //`/a//// 00u000011111122-222 333lWWWWlWWBWBW(h6eh|CJOJQJ^JaJmH sH (h6eh.CJOJQJ^JaJmH sH +h6eh?5CJOJQJ^JaJmH sH (h6eh?CJOJQJ^JaJmH sH (h6ehS VCJOJQJ^JaJmH sH (h6ehF)CJOJQJ^JaJmH sH (h6eh$CJOJQJ^JaJmH sH (h6ehqCJOJQJ^JaJmH sH (h6eh6eCJOJQJ^JaJmH sH 334R4`4k4l4444,505o555555_7r7뫕mZmZGZmZ3ZG'h6eh[5CJOJQJaJmH sH $h6ehjzCJOJQJaJmH sH $h6eh[CJOJQJaJmH sH $h6ehKCJOJQJaJmH sH (h6eh_HCJOJQJ^JaJmH sH +h6eh!5CJOJQJ^JaJmH sH (h6eh6CJOJQJ^JaJmH sH (h6eh6eCJOJQJ^JaJmH sH +h6eh|5CJOJQJ^JaJmH sH (h6eh|CJOJQJ^JaJmH sH 44405556!7^7_77788':(:<<<<<v=w=U@gdjz gdggd  & F gdjzgd[ & F gd[ & F gd[gdi r7|7*88888899999':(:Y:`::ز{gTA-A'h6ehg5CJOJQJaJmH sH $h6ehgCJOJQJaJmH sH $h6eh[CJOJQJaJmH sH 'h6eh 5CJOJQJaJmH sH h6eCJOJQJaJmH sH 'h6eh6e5CJOJQJaJmH sH $h6eh6eCJOJQJaJmH sH $h6eh CJOJQJaJmH sH $h6ehCJOJQJaJmH sH $h6ehjzCJOJQJaJmH sH 'h6ehjz5CJOJQJaJmH sH ::;;1;K;;;;<<<<<<<<=ز؋wcN9N(h6eh6eCJOJQJ^JaJmH sH (h6ehjzCJOJQJ^JaJmH sH 'h6ehjz5CJOJQJaJmH sH 'h6eh6e5CJOJQJaJmH sH $h6ehjzCJOJQJaJmH sH 'h6eh 5CJOJQJaJmH sH $h6eh CJOJQJaJmH sH $h6eh6eCJOJQJaJmH sH $h6ehgCJOJQJaJmH sH 'h6ehg5CJOJQJaJmH sH =u={==??V@s@@ZAcAfAAAAA֫֘qaNa:a'jhKcCJOJQJUaJmH sH $hKchKcCJOJQJaJmH sH hKcCJOJQJaJmH sH $h6eh6eCJOJQJaJmH sH 'h6eh;S5CJOJQJaJmH sH $h6eh;SCJOJQJaJmH sH (h6eh6eCJOJQJ^JaJmH sH +h6ehjz5CJOJQJ^JaJmH sH (h6ehjzCJOJQJ^JaJmH sH (h6eh;SCJOJQJ^JaJmH sH U@V@eAfA7BBBCCCD)E*EESFTFUFFoGEHHHHKKMMgd?4 & Fgd?4gdKcgdi gdjzAAAAAAA0B1B2B5B6BBBB%C&C'C,C-C/C0CxCѼѬѬѼoUѼѬAo'jh/NCJOJQJUaJmH sH 3jh/NhJQCJOJQJUaJmH sH h/NCJOJQJaJmH sH $hKchKcCJOJQJaJmH sH 3jhKchJQCJOJQJUaJmH sH hKcCJOJQJaJmH sH (hKchKc0JCJOJQJaJmH sH 'jhKcCJOJQJUaJmH sH 3jhKchJQCJOJQJUaJmH sH xCyCzC}C~CCCCDCDDDFDGDDDDDD8E;E*CJOJQJ^JaJmH sH +h6ehu5CJOJQJ^JaJmH sH (h6ehuCJOJQJ^JaJmH sH O[r[[[[[[[[t\\O]Y]]]]]]]]]]]ɸɸɣ{eOe:(h6eh_HCJOJQJ^JaJmH sH +h6ehj=5CJOJQJ^JaJmH sH +h6eh_H5CJOJQJ^JaJmH sH (h6ehiOCJOJQJ^JaJmH sH $h6eh|CJOJQJaJmH sH (h6eh|CJOJQJ^JaJmH sH  h6eh6eOJQJ^JmH sH  h6eh5POJQJ^JmH sH  h6ehwUOJQJ^JmH sH (h6ehwUCJOJQJ^JaJmH sH ]3^>^M^z^^^____N`X`aabbbcc#e-eeelftfffffgg`gmgghhhiւււm(h6ehpYCJOJQJ^JaJmH sH (h6eh?CJOJQJ^JaJmH sH (h6eh6eCJOJQJ^JaJmH sH (h6ehgCJOJQJ^JaJmH sH (h6eh5CJOJQJ^JaJmH sH (h6eh!^CJOJQJ^JaJmH sH (h6ehCJOJQJ^JaJmH sH &___n`o```paqaaacccc}d~dddffghiiii#igda\gd & Fgdgd]iii]jjkkkkkk+l5lDlElPlQl]l^lllll/m2mmmmmmlYYYYllY$h6ehqCJOJQJaJmH sH +h6ehpY5CJOJQJ^JaJmH sH +h6ehR5CJOJQJ^JaJmH sH (h6eh5CJOJQJ^JaJmH sH $h6eh!^CJOJQJaJmH sH $h6eha\CJOJQJaJmH sH +h6eh55CJOJQJ^JaJmH sH +h6ehj=5CJOJQJ^JaJmH sH #iii]jjkDlEl]l^lllllmmmminjnnnnnnn'o(ogd$gdqgd] & Fgda\mm#n$n%n6n7nhninjnnnnջ蓀jT?*(h6eh$CJOJQJ^JaJmH sH (h6ehj=CJOJQJ^JaJmH sH +h6ehj=5CJOJQJ^JaJmH sH +h6ehpY5CJOJQJ^JaJmH sH $h6ehpYCJOJQJaJmH sH $h6ehqCJOJQJaJmH sH (h6ehq0JCJOJQJaJmH sH 3jh6ehUCJOJQJUaJmH sH $h6eh$CJOJQJaJmH sH -jh6eh$CJOJQJUaJmH sH  nnnnnnno o$oWo`oooooppptaN?0h  h6eOJQJmH sH h  h vOJQJmH sH $h  h vCJOJQJaJmH sH $h6eh6eCJOJQJaJmH sH $h6eh4*B*ph4@4 xHeader  p#4 @4 xFooter  p#.)@!. x Page NumberFV@1F qFollowedHyperlink >*B*phm56lGc./<=Pyz_`KKL] ^ ] ^ 5{ YZ'( """""##$%`'a''''))B**!+++j,k,l,,,,,0---.!/^/_///00'2(244444v5w5U8V8e9f97:::;;;<)=*==S>T>U>>o?E@@@@CCEEnGoGIIIQKRKSK}K~KyLzLMMOOORRUUUUUWWWWnXoXXXpYqYYY[[[[}\~\\\^^_`aaaa#aaa]bbcDdEd]d^dddddeeeeifjfffffff'g(ggg k!kk5llRmSmTmamcmdmfmgmimjmlmmm~mmmmmmmm0000000000000000000000000000000000000000000000 0 0 0000000000000 0 0000 0 0000000000000 0 0 0 00000000 0 0 00000000 0 0 00 0 0 000 0 00000000000000000000000000000 0 000000000000000000000000000000000000 00 00 00 00 00 00 00 00 00 0 000000 0 0 0 0 0 000000000000000000000000000 0 00000@0y00@0y00@0y00@0y00@0@0@0@0@0@0@0y00/<=e9f97:::;;;<)=*==S>jfffTmmK00tK00K00 0{004y00{00y00y00{00y00y00{00y00y00 0I00K00 0 0{000 !!SSSV2 ; Uxy"y&O)* /3r7:=AxCEHLQPSyTO[]imnptuu;>@ABCEFGHIKLMOPQSTUVWYZ[\]_abdef ^*4U@M_#i(ouu<?DJNRX^`cgu=P99991:5::&;,;/;y;};;=<C<F<<<;===UCCCK;KNKe$f6fmXXXXXXXXXXX3:<ALNV!@   @ n(  6    B S  ?(  HB  C DmLL#tVw#wu _Hlt115468137 _Hlt115468548 _Hlt115468549 _Hlt115468511 _Hlt115468523 _Hlt115468524 _Hlt115468350 _Hlt115468351 _Hlt115468407 _Hlt115468408 _Hlt115468437 _Hlt115468438 OLE_LINK1 _Hlt115468308 _Hlt115468309 _Hlt114480206 _Hlt11448020793:3:(;|;|;?<?<?<?<<<*===.f.fm@@@@@@@@@ @ @ @ @@@@94:4:);};};@<@<@<@<<<8===/f/fm} } }̦}l}#}\}i }t }/}ԏ }\}܍ } }!} tGGQ\#<><>m     $$P[bb"--F>F>m   = *urn:schemas-microsoft-com:office:smarttags PlaceName= *urn:schemas-microsoft-com:office:smarttags PlaceTypeV*urn:schemas-microsoft-com:office:smarttagsplacehttp://www.5iantlavalamp.com/h*urn:schemas-microsoft-com:office:smarttagsCity0http://www.5iamas-microsoft-com:office:smarttags v    Kjjjjjk kXkYkbkekkkkkTm`mamamcmcmdmdmfmgmimjmlmrmtm~mmm==amamcmcmdmdmfmgmimjmlmmm}mmm3&2M Z $MRSa_cZem""" ''R,`,0(233444477-9f97::;;<*==>@ A!A*A\BCDDFFHIJSK~KzLOUYUUUVVWg`ghkkkTm`mamamcmcmdmdmfmgmimjmlmmm~mmmmmmmmm:-;/;~;;D<F<<;==T>T>@@CC ItIvIyIIIJPKhh1i2iyiziiij jJjKjjjjjk kXkYkbkekkkkkTm`mamamcmcmdmdmfmgmimjmlmrmtm~mmmEg%GZ(Z/wb9N,tnB;"PC/ *(0%w0>n -+I,}P|`.'Q6fR X;0O[jHz[kf"AozIh^`o()h ^`hH.h pLp^p`LhH.h @ @ ^@ `hH.h ^`hH.h L^`LhH.h ^`hH.h ^`hH.h PLP^P`LhH.^`o() ^`hH. pLp^p`LhH. @ @ ^@ `hH. ^`hH. L^`LhH. ^`hH. ^`hH. PLP^P`LhH.h^`OJQJo(hHh^`OJQJ^Jo(hHohpp^p`OJQJo(hHh@ @ ^@ `OJQJo(hHh^`OJQJ^Jo(hHoh^`OJQJo(hHh^`OJQJo(hHh^`OJQJ^Jo(hHohPP^P`OJQJo(hHh ^`hH.h ^`hH.h pLp^p`LhH.h @ @ ^@ `hH.h ^`hH.h L^`LhH.h ^`hH.h ^`hH.h PLP^P`LhH.h^`OJQJo(hHh^`OJQJ^Jo(hHohpp^p`OJQJo(hHh@ @ ^@ `OJQJo(hHh^`OJQJ^Jo(hHoh^`OJQJo(hHh^`OJQJo(hHh^`OJQJ^Jo(hHohPP^P`OJQJo(hHh^`OJQJo(hHh^`OJQJ^Jo(hHohpp^p`OJQJo(hHh@ @ ^@ `OJQJo(hHh^`OJQJ^Jo(hHoh^`OJQJo(hHh^`OJQJo(hHh^`OJQJ^Jo(hHohPP^P`OJQJo(hHh^`OJQJo(hHh^`OJQJ^Jo(hHohpp^p`OJQJo(hHh@ @ ^@ `OJQJo(hHh^`OJQJ^Jo(hHoh^`OJQJo(hHh^`OJQJo(hHh^`OJQJ^Jo(hHohPP^P`OJQJo(hHh^`OJQJo(hHh^`OJQJ^Jo(hHohpp^p`OJQJo(hHh@ @ ^@ `OJQJo(hHh^`OJQJ^Jo(hHoh^`OJQJo(hHh^`OJQJo(hHh^`OJQJ^Jo(hHohPP^P`OJQJo(hHh^`o()h ^`hH.h pLp^p`LhH.h @ @ ^@ `hH.h ^`hH.h L^`LhH.h ^`hH.h ^`hH.h PLP^P`LhH.h^`OJQJo(hHh^`OJQJ^Jo(hHohpp^p`OJQJo(hHh@ @ ^@ `OJQJo(hHh^`OJQJ^Jo(hHoh^`OJQJo(hHh^`OJQJo(hHh^`OJQJ^Jo(hHohPP^P`OJQJo(hHh ^`hH.h ^`hH.h pLp^p`LhH.h @ @ ^@ `hH.h ^`hH.h L^`LhH.h ^`hH.h ^`hH.h PLP^P`LhH.h^`OJQJo(hHh^`OJQJ^Jo(hHohpp^p`OJQJo(hHh@ @ ^@ `OJQJo(hHh^`OJQJ^Jo(hHoh^`OJQJo(hHh^`OJQJo(hHh^`OJQJ^Jo(hHohPP^P`OJQJo(hHh^`OJQJo(hHh^`OJQJ^Jo(hHohpp^p`OJQJo(hHh@ @ ^@ `OJQJo(hHh^`OJQJ^Jo(hHoh^`OJQJo(hHh^`OJQJo(hHh^`OJQJ^Jo(hHohPP^P`OJQJo(hHh^`OJQJo(hHh^`OJQJ^Jo(hHohpp^p`OJQJo(hHh@ @ ^@ `OJQJo(hHh^`OJQJ^Jo(hHoh^`OJQJo(hHh^`OJQJo(hHh^`OJQJ^Jo(hHohPP^P`OJQJo(hHh ^`hH.h ^`hH.h pLp^p`LhH.h @ @ ^@ `hH.h ^`hH.h L^`LhH.h ^`hH.h ^`hH.h PLP^P`LhH.h^`OJQJo(hHh^`OJQJ^Jo(hHohpp^p`OJQJo(hHh@ @ ^@ `OJQJo(hHh^`OJQJ^Jo(hHoh^`OJQJo(hHh^`OJQJo(hHh^`OJQJ^Jo(hHohPP^P`OJQJo(hHAoz[k}P-+I/ (0/w"[%G%w0EX,t'Qd        d                                                              d                                                                        a|hc|hI|hZ;.+|hT<|h(qB|hLW5d|h5dv|hww|ha4||hi .X ~ }  ;y UCx6[pYR  !~$PF(*f*o,xM07]2?4 5g509j=x>f{>MA BqF_HEJ;K4mp@UnknownGz Times New Roman5Symbol3& z Arial;" Helvetica?5 z Courier New;Wingdings"1sfЙ&f%S]7S]7!4d*m*m 2qHP ?]21watteler Reto HadornL           Oh+'0  8 D P \hpx1 watteler NORMAL.DOT Reto Hadorn37Microsoft Office Word@j@h2@")@5;S]՜.+,D՜.+,, hp|  ZA7*m 1 Titleh 8@ _PID_HLINKSA BjO>http://info1.za.uni-koeln.de/ddicdg/documents/RCNS_050706.zip )http://www.sidos.ch/mmg/vi/html/1064.htm 00'http://www.sidos.ch/mmg/vi/html/17.htm Fi=http://www.sidos.ch/mmg/vi/html/Documents/4_QVTypesMovie.ppt Y`=http://www.sidos.ch/mmg/vi/html/Documents/3_QVTypesModel.pdf Rq=http://www.sidos.ch/mmg/vi/html/Documents/3_QVTypesModel.doc _v =http://www.sidos.ch/mmg/vi/html/Documents/2_QVTypesTable.pdf Tg =http://www.sidos.ch/mmg/vi/html/Documents/2_QVTypesTable.doc pcChttp://www.sidos.ch/mmg/vi/html/Documents/1_QVTypesText_050707.pdf {rChttp://www.sidos.ch/mmg/vi/html/Documents/1_QVTypesText_050707.doc xT"mailto:reto.hadorn@sidos.unine.ch   !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghjklmnoprstuvwxyz{|}~Root Entry F08;Data i1TableqoWordDocument.SummaryInformation(DocumentSummaryInformation8CompObjq  FMicrosoft Office Word Document MSWordDocWord.Document.89q