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to it, since it s saying something about the thinking of your sample as a whole as well as each individual member! If the H-I-L indices are mixed, the idea behind that particular construct reveals no particular consensus In the sample as a whole, there s a certain ambivalence about the construct s relevance or importance to the topic And particularly, if you notice that two or more constructs which express essentially the same meaning, obtained from the same person, have mixed H-I-L indices, set the constructs aside There s no point in preserving ambiguity or ambivalence here If the H-I-L indices are low, it looks as though the sample as a whole agree that the construct doesn t relate particularly well to the topic in general Note this Table 79 shows the result of this step for our running example (11.

3) If there are subthemes within a category, group them according to the meaning being expressed The nal result will re ect your overall purposes in doing a content analysis; but, typically, you would aim to end up with 40% to 80% of the original number of constructs which show the consensus you re looking for (Of course, if there was little consensus, you d say so!) The result will be a table in which the columns are categories, each one divided into two subcolumns, in the rst of which the chosen constructs appear, and, in the other, the % similarity scores and the H-I-L index (12) Summarise the table: state the frequency under the category headings.

3 / Presentation of Financial Statements (IAS 1) Consolidated Statement of Recognized Income and Expenses

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A triple can be modeled as a predicate: triple(subject, property, object) A set of triples equals a list of triples and a connected graph is decomposed into a set of triples For our example this gives Triple(John, owns, computer) Triple(computer, make, Apple) This sequence is equivalent to: [Triple(John, owns, computer) Triple(computer, make, Apple)] From Figure 8-2 the triples are Triple( who, owns, computer) Triple(computer, make, what) This sequence is equivalent to: [Triple( who, owns, computer) Triple(computer, make, what)] From Figure 8-3 the triple is Triple([Triple(X, owns, computer)], implies, [Triple(X, must buy, software)]) From Figure 8-4 the triple is Triple( who, must buy, software) A uni cation algorithm for RDF can handle subgraph matching and embedded rules by the term subgraph matching with rules The uni cation algorithm divides the sequence of RDF statements into sets where each set constitutes a connected subgraph.

How many constructs are there in each category (and subcategory, if relevant) at this stage Report the number, and if this varies markedly from the original number of constructs in each category at the start of step 10, assess and discuss the signi cance On what kinds of issues is there a consensus, and on which ones don t people agree This is a good point at which to calculate any sums, averages, and so on You might, for example, use the % similarity scores to provide a mean importance score of each category for the sample as a whole, if this makes sense in your analysis (13) Complete any differential analysis which your investigation requires, as before, when you bootstrapped.

If you tag each construct according to the different subsamples of interviewee, or cross-tabulate them in different rows of your table, you may see differences among the subsamples in terms of:.

Unit number the apartment number Tenant s name the name of your tenant Type of apartment for example, two bedroom, two bath

SCADA networks are widely disbursed across the world to provide mission critical information for a wide range of applications, including resource extraction (well and pipeline monitoring), natural resource management (dams, waterways, water distribution systems, sewers, etc.), security monitoring (alarms, report by exception, polling, camera, etc.), and sensors (heat, cold, intrusion, uid or pressure levels, light switches, etc.). All of these applications require both purchase and maintenance of fairly low volume and expensive equipment that is only required to transport what are now considered extremely narrowband data messages, typically between 4.8 and 19.2 Kbps. The arrival of ubiquitous broadband shared IP networks will revolutionize both the ef ciency and the utility value of SCADA over broadband, improving reliability (commercial networks are monitored on a full-time basis 7 24 365 and have strict restoration time if an outage is detected); such oversight and service restoral response is rare among private SCADA networks, and if it does exist, it is likely to prove cost-prohibitive as we move

Putnam Putnam Putnam Putnam Putnam Putnam Putnam Putnam Domestic hybrid World stock Foreign stock Large growth Large growth Mid-cap growth Mid-cap growth Large growth Domestic hybrid Mid-cap growth Multisector bond Large growth Large growth Large growth Large growth Foreign stock Large growth World stock 12.71 5.83 6.68 14.89 9.17 13.02 14.66 9.09 11.23 9.24 12 74 20 6 39 11 7 10 20 9 8.08 9.89 9.75 10.40 11.67 51 31 32 26 17 Domestic hybrid Large blend Large blend Large value Large blend 8 91 44 48 74 81 85 46 79 35 4 26 32 17 30 32 2 29 9 11 2 2 7 4 6 86 31 42 68 29

Staff members report to functional managers as well as project managers. Project managers have equal authority to functional managers.

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