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SPSS software is a software that has been used by students, statisticians, and researchers to solve varities of mathematical problems. SPSS is preferred by these people because of the benefits and facilities provided. This chapter starts with history and development of SPSS. The next section is strength and weakness of SPSS. The next section illustrates the comparison between SPSS, R programming and Minitab. Then, it is continued with an explanation about descriptive statistic in section 2.4 and inferential statistic in section 2.5. Finally, a summary is provided at the end of this chapter.

2.1 History and Development of SPSS

The history of SPSS can be traced back to 1967, when Norman H. Nie, then a 22-year-old Ph.D. candidate at Stanford University, decided to develop his own solution after becoming \"frustrated trying to use a computer to analyze data describing the political culture of five nations,\" according to the September 22, 2003, issue of the Chicago Tribune.

The application Nie was trying to use was created for biologists, not social scientists. With that in mind, Nie took detailed notes about what he needed in a software application and enlisted the help of Dale H. Bent, a fellow doctoral candidate whose background was in operations research, to design a file structure. Hadlai \"Tex\" Hull, who had recently received his MBA from Stanford, was tapped to write the code, and by 1968 the Statistical Package for the Social Sciences (SPSS) was born. Nie and Hull left Stanford to pursue careers at the University of Chicago, and they brought their SPSS program along with them. However, their main focus was on academics and research--not on developing or selling software. Hull became head of the university\'s Computation Center. Nie joined its National Opinion Research Center and eventually was named chairman of the political science department.

Nie became a top authority on social science data and statistical analysis. His specialty was working with voting patterns, and he went on to author a well-known book on American politics called The Changing American Voter. Through the years, Nie would receive national awards for his books, and by the early 2000s he had become professor emeritus in the University of Chicago\'s political science department, as well as a research professor in political science at Stanford University\'s Graduate School of Business. With help from a university librarian and the University of Chicago\'s support, Nie and Hull began selling SPSS to academicians at other universities. As they concentrated on their academic careers, SPSS grew by itself. The data analysis application became increasingly popular and by 1974 was earning revenues of $200,000 per year--without any marketing or promotional effort at all.

As the company Web site later explained: \"The early success of SPSS was directly related to the quality and availability of the documentation that accompanied the software. McGraw-Hill published the first SPSS user\'s manual in 1970. Once the manual was available in college bookstores, demand for the program took off. Nie, Bent, and Hull received a royalty from sales of the manual but nothing from distribution of the program. In Nie\'s words, \'It was like Gillette selling razors at cost and getting its profits from the blades\'.\"

SPSS became so successful that the IRS took notice in 1971, indicating that it considered SPSS a small software company. This determination in turn called into question the University of Chicago\'s status as a tax-exempt organization. Mainly for that reason, Nie and Hull incorporated their operation in 1975, and SPSS officially became an independent company. Dale Bent, who had played a role in the design of SPSS at Stanford, opted to accept an academic position at the University of Alberta in his native Canada instead of becoming involved with the new Chicago-based enterprise. For several years, SPSS remained a part-time endeavor for Nie and Hull. \"Initially, this was an ego trip,\" Hull explained in a September 2003 article in the Chicago Tribune. He noted: \"It was fun to do, and it was neat when people knew your name at computer conferences. But there was no money in it.\"

While a competitor called SAS Institute was making strides during the late 1970s by partnering with the likes of IBM, SPSS remained focused on its base of academic users. The company sought to keep its software easy to use for those who were not computer savvy, and even paid fellow academicians to make upgrades. According to SPSS, the portable nature of its software enabled colleges and universities to use it on a variety of mainframe computer systems, including Burroughs large systems, the Control Data 6000 series, Digital Equipment Corporation (DEC) large systems, GE/Honeywell large systems, and the Univac 1108.

SPSS eventually was adopted by government and business users as well. In the mid-1970s SPSS software was employed by NASA to calculate the mean time between Space Shuttle part failures. The National Forest Service used SPSS to track bear encounters and injury reports in national parks. Within the business sector, SPSS became a tool for consumer marketing research and gained popularity among Anheuser-Busch, Procter & Gamble, and other consumer products companies. According to company lore, while attending a company picnic in 1980, Nie and Hull looked about and were impressed with the growing number of people who were involved with SPSS and depending on the firm for their livelihood. The partners made the important decision to take the enterprise to a new level.

The early 2000s at SPSS were characterized by a focus on so-called predictive analytics, which, according to company literature \"connects data to effective action by drawing reliable conclusions about current conditions and future events.\" In 2003, SPSS introduced Predictive Web Analytics, a new product that combined the Clementine data-mining program with its NetGenesis Web analysis software. With prices starting at $135,000, the new program made it possible for users to see patterns in Web data and design more effective, relevant Web sites. An automated predictive analytics application for marketers called PredictiveMarketing also debuted that year, and the company held an informational summit in Stockholm to promote the technology and the products.

Looking toward the future, SPSS had its eye on audio and video mining as a new niche. As Noonan explained in the September 22, 2003, issue of the Chicago Tribune, \"Market research firms are especially interested in video mining. When you get feedback from focus groups, it is data and text, but if you could add in the body language, what their eyes do, that adds a lot to what\'s not said.\" The company had several products in the pipeline, but it also faced financial and legal challenges. While sales remained good, earnings declined. Moreover, allegations heard in U.S. District Court that the company had deceived stockholders by issuing misleading financial information, caused SPSS stock prices to slip, and a late filing with the Securities Exchange Commission raised some red flags at NASDAQ. Nevertheless, management remained optimistic about restoring earnings and stockholder confidence to prior levels, given, in particular, new leadership in the sales department and new markets for predictive analytics.

2.2 Strengths and Weaknesses of SPSS.

There are a few strong features of SPSS that make it a suitable statistical computing tool for teaching and learning inferential statistical analysis. These strengths are reviewed in Section 2.2.1 as a justification of using SPSS in this research. Nonetheless, SPSS still consists a few weakness that will examined in Section 2.2.2. However, SPSS development still in progress to overcome the weaknesses that SPSS have.

2.2.1 Advantages of SPSS.

First and foremost, SPSS is well known and supported software (Harrington, McLeod, & M. Clark, 2009) . SPSS has been in the marketplace for many years and many textbooks for introductory statistics courses are based on this application. Other programs can easily import SPSS data files. Students would have relatively little difficulty in obtaining support for their SPSS work from textbooks, Internet resources, and other students.

SPSS that allows commandline input and programming, as well as the use of graphical user interface analysis (Liga Paura & Irina Arhipova, 2012) . Many students and instructors find this software attractive as it is user friendly. Because of its graphical user interface, a large of number of statistical functions are easy to use and access. The graphical user interface provides the student with quick access to the statistical routines needed for an introductory course; student also has the ability to customize SPSS results including the display of graphic output

SPSS is that students can import data from other sources, when data is organized as a database, including Excel (Liga Paura & Irina Arhipova, 2012). Importing an Excel spreadsheet to SPSS for the data analysis is a fairly simple process, requiring some preparation and a few basic steps. One of the opportunities to reduce time for the teaching of software is to include one statistical program at all levels of the study.

Lastly, SPSS is has availability of help (Harrington, McLeod, & M. Clark, 2009). There are number of Excellent books that provide comprehensive information on using SPSS; the availability of the list serve for this package also provides another source of guidance for specific issues. The student has access to a large number of excellent books that provide comprehensive information on using SPSS.

2.2.2 Weakness of SPSS.

SPSS has limitations with high cost. SPSS is a commercial software that is available at a relatively high cost [ JourneyEd (2009) list SPSS Graduate Pack for Windows at $199.98] ; some applications are available only as another product with a separate fee. At price point of almost $200 even for the Graduate version of the SPSS package, the cost is an issue for student.

The license to use SPSS is time limited; the license only allows installation on a limited number of computers; add-ons to the SPSS require acquisition to additional licenses. The licensing is not user friendly (Harrington, et al., 2009). Although students would be able to use their purchased package for the duration of the introductory statistics course subsequent use of the package in graduate and professional work would become a licensing problem.

Lastly, student will lag in newer techniques. For academic use SPSS lags notably behind SAS, R and even perhaps others that are on the more mathematical rather than statistical side for modern data analysis (e.g. robust and bootstrapping approaches available easily conducted elsewhere are nonexistent or very difficult to do, basic tests of analytical assumptions are often not available) (Harrington, et al., 2009).

2.3 Comparison between SPSS, R and Minitab.

SPSS ( Statistical Package for the Social Sciences) and Minitab are among the few recognized softwares that are commanly used for statistical analysis. The comparison of differences features of R, SPSS and Minitab are summarized in Table 2.1.

Both R and Minitab are having good access to all features. SPSS, however, not determined conclusively. From Table 2.1, R and Minitab have good in independent installation which is an attribute SPSS lacks. In contrast, SPSS has good information for blind users but R is for third party and Minitab does not found. Nonetheless, R has more superior graphing capabilities compared to the other two softwares.

Table 2.1. Comparison of SPSS, R programming and Minitab.

Minimum Criterion SPSS R Minitab

Independent installation Not easily Yes Yes

Import data Yes, but wizard is challenging

Yes Yes

Review data Yes Yes No

Edit data Yes Yes No

Simple EDA Yes Yes Yes

Exporting output Yes Yes Yes

Update graph Start from scratch Start from scratch

Start from scratch

Help documentation Limited

Yes Limited

Access to all features Not determined conclusively Yes No

Desirable criterion SPSS R Minitab

Graph presentation No Yes No

Information for blind users Yes Third party None found

Minors errors Yes, if command language is used Yes Yes, if

command language is used

Knowledge transfer Not guaranteed Yes Not guaranteed

Flexibility Yes, but seldom used Yes No

Graphic formate Not SVG Yes Cannot directly save postscript files, no SVG

Output formats Yes, including HTML, but not LATEX Yes, but add-on packages required Rich text or HTML but not LATEX

Source: Journal of Statistical Software, software review 1, volume 58

From the comparison that has been made, it is fair enough to say that R programming has more desirable critirion campared to SPSS and Minitab. This also indicates that inferential analysis, which is a part of statistics, can be performed by using SPSS as an environment computing.

2.4 About Descriptive Statistics.

Most authors seem to agree that descriptive statistics include numbers of cases, measures of central tendency, measures of variability, measures of shape, and frequency and percentage distributions (Cohen & Holliday, 1979; Fleischman & Williams, 1996; Guilford & Fruchter, 1978; TEA, 1997; Wilde & Sockey,1995). Correlation and chi-square are also considered descriptive by some authors (Cohen & Holliday, 1979; Guilford & Fruchter, 1978; Fleischman & Williams, 1996). But in this study, will more about descritive statistics. Descriptive statistics usually involve measures of central tendency (mean, median, mode) and measures of dispersion (variance, standard deviation, etc.) (DeCaro, S. A. ,2003). Descriptive Statistics refers to a discipline that quantitatively describes the important characteristics of the dataset (Surbhi S, 2016). For the purpose of describing properties, it uses measures of central tendency, i.e. mean, median, mode and the measures of dispersion i.e. range, standard deviation, quartile deviation and variance, etc.

Descriptive statistics are used to describe data, not to show causality (Worthen, Sanders & Fitzpatrick, 1997). These involve a sophistication of statistical knowledge valuable for many purposes, but are timeconsuming and difficult for many school stakeholders to understand. In Program Evaluation, (Worthen et al. ,1997) remind us that most o f the questions of concern by these stakeholders can be answered through the use of descriptive statistics.

While principals must be concerned with summative evaluation, more of their time is devoted to formative evaluation. Descriptive statistics are most useful for formative evaluations because they help principals decide what to change and how to change it (Wilde & Sockey, 1995; Worthen et al., 1997).

The value of descriptive statistics should not be overlooked. It is always important to know the data thoroughly before summarizing them, even when using inferential statistics. Careful examination of the data helps in discovering errors in data collection, entries, or coding (Worthen et al., 1997). Descriptive statistics can fulfill this function.

2.5 About Inferential Statistics.

Inferential Statistics is all about generalising from the sample to the population, i.e. the results of analysis of the sample can be deduced to the larger population, from which the sample is taken (Surbhi S, 2016). It is a convenient way to draw conclusions about the population when it is not possible to query each and every member of the universe. The sample chosen is a representative of the entire population; therefore, it should contain important features of the population.

Inferential Statistics is used to determine the probability of properties of the population on the basis of the properties of the sample, by employing probability theory (Surbhi S, 2016). The major inferential statistics are based on the statistical models such as Analysis of Variance, chi-square test, student’s t distribution, regression analysis, etc.

inferential statistics, which draws conclusions from data that are subject to random variation (e.g., observational errors, sampling variation).

Inferential statistics consists of generalizing from samples to populations, performing estimations and hypothesis tests, determining relationships among variables, and making predictions ( Bluman, 2011). Inferential statistics uses probability, i.e., the chance of an event occuring. You may be familiar with the concepts of probability through various forms of gambling.

2.6 Summary

This chapter examines the history and development of SPSS with its strengths and weaknesses. Other than that, SPSS is compared with R and Minitab to evaluate its minimum criterion and desirable criterion and its found out that R is have more criterion that fullfil both criterion. This chapter also discussed about the descriptive and inferential statistics. Techniques and commands of executing descriptive and inferential statistics analysis using SPSS will be illustrated in the next chapter.

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