please rephrase these answers

1. Give an example of Primary, Secondary and Tertiary sources?

An example of primary source is an original research results published for the first time.

An example of secondary source is an information that has been disclosed by third parties, such as, corporate reports and press releases.

An example of tertiary source is for data which have been aggregated, categorized and/or reworked in databases.

2. Foster (1986) Identified a number of problems associated with data collection from secondary sources, in both cross-section and time-series studies. For Cross-section Data, there seven of them. List and explain all seven.

1^st problem is that data may exclude some current companies. This may be a particular problem if multiple databases are being used which do not overlap completely, so that some companies fall 'between the cracks'. In any case, small companies may not be included if there are size 'hurdles' specified for their inclusion. The same principles would apply to those companies which are not actively traded on stock markets. These conditions may also lead to the exclusion of private or foreign-owned companies. A common reason for such exclusions is the non-availability of the data. Particularly annoying in this respect is the absence of data for subsidiary companies where there is no requirement for them to report separately from the parent.

2^nd problem is that data may exclude non-surviving firms. Merged, acquired and bankrupt firms will normally be omitted from current databases, necessitating searches from other sources if these are the subject of the research. Much past research in the failure prediction area has been criticised for suffering from a survivorship bias because, by definition, failed companies tend to be omitted from the analysis due to unavailable information.

3^rd problem is that data may not be right up to date in that the most recent data may not have been incorporated. This is becoming less of an issue with more online and web-based databases operating either in a real-time mode or being capable of uploading information on a daily basis. 4^th problem is that data may be incomplete in that they omit some financial items. For example, earnings forecasts, or 'notes to the accounts', may not be there, necessitating the use of alternative sources.

5^th problem is that there may be inconsistent classification of some financial items across firms. If the database comprises other than camera copies of original documents, then some assumptions are inevitable in order to produce systematic cross-company classifications. For example, where firms are permitted differences in reporting line items, there will be different levels of aggregation, which may only be separable with arbitrary decisions. Thus, one firm might include overhead expenses in 'costs of goods sold', while another might include overheads in expenses attributable to 'marketing, administrative and general'. Unreliable entries may thus result for items such as 'overhead' where disaggregation assumptions have to be made. These kinds of problems are exacerbated by non-synchronous reporting periods (resulting in large differences both within and between countries) and the non-uniformity of accounting methods, especially across industries, which makes comparisons difficult because different choices may still be consistent with accounting standard compliance.

6^th problem is that there may be recording errors, necessitating checks against other comparable databases where feasible, and necessitating the use of simple internal validity checks. For example, computing the mean and standard deviation of items allows all of those outside the range of two standard deviations, either side of the mean, to be identified and questioned. Similarly, simple comparisons of quick assets with current assets may reveal basic errors. Industry classification poses a particular problem here because there is no single, accepted definition of 'industry' and different databases may adopt alternative classifications. Although 'product group' or 'production process' would normally form the basis of classification, without reference to some external regulatory classification, problems may occur.

7^th problem is that the earlier sections on social media and big data readily illustrate that the nature of disclosure is expanding all the time, making it more and more difficult for researchers to be confident that they have captured the most reliable and comprehensive sources. In the financial reporting environment, most studies still rely on the content of the corporate report, but increasingly newspaper sources, social media, analyst reports and conference calls are being used because they provide more timely media. The Financial Times Index (UK) and Wall Street Journal Index (USA) provide popular sources for company news items. Internet, email and Twitter disclosures represent additional sources that have remained relatively untapped until recently, but which provide potentially important information. Bloomfield et al. (2016) make a neat distinction between structured and unstructured datasets for accounting researchers: Dow-Jones providing Factiva (an archive of press releases and news articles) and the SEC providing EDGAR (an archive of corporate report information) as publicly available unstructured raw data. The structured commercial data sources include I/B/E/S for analyst forecasts, CRSP for market data and COMPUSTAT for financial data. Researchers would generally prefer to use structured data if available but must be aware that it may not include precisely the variables sought - in which case we must return to the unstructured forms as complementary data sources.

8^th problem is that there is a wealth of evidence that companies are disclosing information through these means to investment analysts prior to its availability to the stock market so that analysts' reports themselves have become an increasingly popular source. Standard and Poor's COMPUSTAT is prominent among the databases commonly used for the analysis of financial information, with accounting and market data, including multiple financial ratios, readily available for most companies in the developed world for periods extending over 20 years. Friendly interfaces permit the researcher to examine single companies at a point in time, or multiple companies over many years, embracing many possible variables. (The latter example is often termed panel data and its analysis is examined in detail in Chapter 6).

3. For Time-series data, there are three of them. List and explain all three.

1^st problem is that structural changes may have taken place in the company or the industry, making comparisons between time periods fraught with danger. Internally, these may be due to mergers, acquisitions or divestments; externally, they may be attributable to new government policy, deregulation, new products, new competitors or technological change.

2^nd problem is that accounting method changes, particularly those associated with voluntary choices or switches, may make the financial numbers from successive periods difficult to reconcile. Where this constitutes deliberate obfuscation, it is a particular cause for concern.

3^rd problem is that accounting classification issues may occasion different corporate interpretations being placed on particular items, perhaps again to cloud the communication issue. Thus, a firm may elect to consolidate the results of a subsidiary in one year, but not the next, even though there appears to have been no material change in circumstances between periods. Similarly, the flexibility in reporting the timing and amounts associated with accounting for 'extraordinary items' and 'goodwill write-downs' frequently necessitates adjustments being made in data if a comparative base is to be maintained.