SPSS COURSE

Bootstrap Procedure

2012-01-07T12:57:00.000+07:00

The bootstrap procedure was proposed by Effron in order to estimate the standard errors of a sample for those statistics and for those sampling distributions where a conventional formula could not be used. It is increasingly being employed for complex and highly stratified samples, and for statistics whose sampling distribution is not known. The procedure involves multiple sampling at random with replacement from among the primary sampling units, to obtain the specified number of primary sampling units and a very large distribution of estimated values. The mean and standard deviation of the estimated values of the distribution so obtained are taken as the mean and standard deviation respectively of the estimated statistic.

Compiled by John P. Keeves 1995

Bonferroni Test Statistic

2011-12-31T09:07:00.001+07:00

In the testing of multiple comparisons in analysis of variance and regression analysis it is necessary to make allowance for the fact that the comparisons are not independent of one another. This allowance is made by multiplying the probability estimate by the number of comparisons involved. Also referred to as the Dunn multiple comparison test, although initially the test was proposed by R.A. Fisher.

Compiled by John P. Keeves 1995

Bivariate Analysis

2011-12-03T13:05:00.000+07:00

Bivariate analysis Where only two variables are considered at any one time in the analysis of data, the type of analysis is referred to as bivariate analysis. Commonly a large number of bivariate relationships are estimated by the same analytical procedure which produces bivariate correlation coefficients, or bivariate regression coefficients, or chi-square statistics.

Compiled by John P. Keeves 1995

Biserial Correlation

2011-11-19T07:27:00.001+07:00

The correlation coefficient between a dichotomously categorised variable and a continuous variable is referred to as a biserial correlation. In calculating the coefficient, allowance is made to estimate what the correlation would have been if the dichotomous variable had not been categorised and had remained as a continuous variable.

Compiled by John P. Keeves 1995

Binomial Distribution

2011-04-11T05:46:00.000+07:00

A binomial variable has two values and is said to be dichotomous (e.g. right, wrong; male, female). The probability distribution of a binomial or dichotomous variable is referred to as the binomial distribution.

Compiled by John P. Keeves 1995

Bias

2011-04-07T15:34:00.001+07:00

(1) The occurrence of systematic error in contrast to random error in an estimated value or relationship involves the presence of bias.

(2) Bias may also refer to the effect of an identifiable factor that was not expected to influence an estimated value or relationship.

Compiled by John P. Keeves 1995

Analysis of Variance and Covariance

2011-03-23T16:44:00.000+07:00

Among the statistical procedures used in the analysis of data are the analysis of variance (ANOVA), analysis of covariance (ANCOVA), multivariate analysis of variance (MANOVA), multivariate analysis of covariance (MANCOVA) procedures which require that cases are randomly allocated to treatment and control groups or randomly selected from a population where the groups occur naturally.

These procedures seek to partition the total variance into its components by the analysis of sums of squared terms derived from the data. Where the effect of a variable confounds the analysis, the effect of the variable may be removed or controlled by a covariance adjustment.

However, alternative procedures of analysis, such as regression analysis, must be used where the variable, whose effects are being controlled in statistical analysis, might have influenced the treatment conditions under consideration. Where there is more than one criterion variable, the term multivariate analysis is commonly employed.

Compiled by John P. Keeves 1995

Beta

2011-03-23T16:33:00.002+07:00

(1) A beta coefficient is a standardised *regression coefficient.

(2) A beta level involves the probability of accepting a null hypothesis when in fact it is false.

Compiled by John P. Keeves 1995

Compiled by John P. Keeves 1995

Attitude Measures

2011-03-18T09:33:00.000+07:00

Instruments are developed in educational and behavioural science research to measure the positive or negative evaluation and disposition of individuals towards objects of attention, including people, groups, policies, practices and abstract concepts and relationships.

Such attitudes are learned and internalised and are consequently relatively persistent and stable characteristics of individuals. Attitude measures commonly involve a scaled series of statements or questions designed to assess the strength and direction of the underlying attitude.

Compiled by John P.Keeves 1995

Alternative Hypothesis

2011-03-16T09:52:00.002+07:00

Any hypothesis that is contrasted with the one being tested, commonly the null hypothesis (H0) of no effect, is referred to as the alternative hypothesis (H1).

Rejection of the null hypothesis implies that the alternative hypothesis, commonly the hypothesis of interest in research and sometimes referred to as the research hypothesis can be accepted.

Compiled by John P. Keeves 1995

SPSS Inc. has been acquired by IBM

2010-04-26T12:43:00.000+07:00

SPSS Inc. has been acquired by IBM. Find more ...

Modeling in SPSS

2010-04-26T12:34:00.000+07:00

Uncover key insights and use them to solve real business problems, with the Modeling family from SPSS, an IBM Company. Find more ...

Harvard Business Review: Make Better Decisions

2010-04-26T12:32:00.000+07:00

Traditionally, decision making in organizations has rarely been the focus of systematic analysis.

That may account for the astounding number of recent poor calls, such as decisions to invest in and securitize subprime mortgage loans or to hedge risk with credit default swaps.

Find more ...

Affective Domain, Aggregation Bias, and Alpha

2008-12-03T11:08:00.003+07:00

Affective domain: In the classification of educational objectives, those objectives concerned with attitudes, interests and values are said to involve 'affect' or emotion and feelings and are considered to lie in the affective domain in contrast to the *cognitive or *psychomotor domains.

Aggregation bias: The combining of students within classrooms or schools in the analysis of data commonly gives rise to inflated estimates of effect when the data are analysed at the group rather than the individual level. Unbiased estimates of effect are only obtained through procedures of *multilevel analysis. Failure to allow for aggregation bias and the effects of data aggregation are the most frequent sources of error in statistical analysis in educational research.

Alpha (a) (1) See * coefficient alpha. (2) An alpha level involves the *probability of rejecting a *null hypothesis when in fact it is true.

Compiled by
John P. Keeves
1995

Acquiescence

2008-10-30T04:59:00.003+07:00

Acquiescence: The tendency or predisposition by some individuals to provide an 'agree' response, or to say 'yes' to a statement, without regard for what they think or feel, because to do so is considered socially desirable. [ERMM, 425]

Syn: yea-saying, social desirability bias, compliance effect.

Compiled by John P. Keeves 1995

Describe and Explore your Data with Bar Graph Using SPSS 16.0

2008-08-22T04:38:00.009+07:00

Bar graphs can be simple or very complex, depending on how many variables are included. Bar graph can be used to show the number of cases in particular categories or it can show the score on some continuous variable for different categories.

Procedure for creating a bar graph:

From the menu at the top of the screen click on Graphs, then Bar.
Click on Clustered.
In the Data in chart are section, click on Summaries for groups of cases. Click on Define.
In the Bars represent box, click on Other statistics (e.g., mean).
Click on the continuous variable you are interested in (e.g. confidence in press). This should be appear in the box listed as Mean. This indicates that the mean on the confidence in press for different groups will be displayed.
Click on your first categorical variable (e.g. agegp3). Click on the arrow button to move it into the Category axis box. This variable will appear across the button of your bar graph (X axis).
Click on another categorical variable (e.g. sex) and move it into the Define Clusters by: box. This variable will be represented in the legend.
Click on the Options button. Remove the tick from Display groups defined by missing values. To do this, click once on the box.
Click on OK

The output generated from this procedure is presented below:
Reading the output: The output from this procedure shows a summary of the distribution of scores for the groups that have been requested (i.e. males and females from the different age groups). The graph presented above suggests that females had slightly higher confidence in press than males, and that this difference is more pronounced among the two older age groups. Among the 18 to 35 age group and 36-51 age group the difference in scores between males and females is very small.

Describe and Explore your Data with Histogram Using SPSS 16.0

2008-08-14T08:57:00.010+07:00

With SPSS Program, you can describe and explore your data using different types of graphs, such as histogram, bar graphs, scatterplots, boxplots, and line graphs. At the moment, I will explain how to create a histogram. Histogram is used to display the distribution of a single continuous variable, like age and Likert-type scores.

From the menu at the top of the screen, click on Graphs, then on Histogram.
Click on your variable of interest and move it to the Variable box (e.g. age).
Click on Display normal curve. This option will give the distribution of your variable and superimposed over the top, how a normal curve for this distribution would look.
If you want to give your graph a title click on the Titles button and type the desired title in the box (e.g. Histogram of age).
Click on Continue, then OK.

This procedure will generate an output as follows:
Reading the output:
Inspection of the shape of the histogram provide you with the information about the distribution of scores on the continuous variable. In this example, the scores are not reasonably normally distributed, with most scores occurring in the center. However, it is common to find data not normally distributed in the social sciences. This issue is well-discussed in the normality part of statistics books.

Join the Review of the New SPSS Program

2008-08-04T11:02:00.007+07:00

What's New in SPSS Statistics 17.0: After the release of SPSS 17.0 (SPSS Statistics), SPSS.com offers an invaluable opportunities for the users to join what is called 'a complimentary sneak preview of SPSS Statistics 17.0, recorded on Thursday, September 11, 2008.

SPSS 17.0 offers something for everyone: Improved research and reporting tools; greater accessibility for business users combined with new functionality for statistical programmers; easier enterprise integration, deployment, and management. attendants of this webcast will learn many capabilities of SPSS Statistics 17.0:

The new multiple imputation procedure in SPSS Missing Values that helps you more easily complete datasets for more reliable analysis; an updated Syntax Editor so you can more easily create, test, and correct syntax; improved integration with Microsoft® Office, making it easier to create cleanly formatted reports. Find details ...

Checking for Outliers

2008-08-01T09:32:00.010+07:00

SPSS Survival Manual by Julie Pallant: Many statistical techniques are sensitive to outliers. The previous techniques that we have talked about under the descriptive section can also be used to check for outliers. However, there is alternative way to assess them.

Procedure for Identifying Outliers:

From the menu at the top of the screen, click on Analyze, then click on Descriptive Statistics, then Explore.
In the Display section, make sure Both is selected. This provides both Statistics and Plots.
Click on your variable (e.g. most important problems in 12 months), and move it into the Dependent list box.
Click on id from your variable list and move into the section Label cases. This will give you the ID number of the outlying case.
Click on the Statistics button. Click on Outliers. Click on Continue.
Click on the Plots button. Click on Histogram. Ask for a Stem and Leaf plot as well.
Click on the Options button. Click on Exclude cases pairwise. Click on Continues and then OK.

The output generated from this analysis as follows:

Reading the Output:

Have a look at the Histogram and check the tails of distribution if there are data points falling away as the extremes.
Inspect the Boxplot whether SPSS identifies outliers. These outliers are displayed as little circles with a ID number attached.
Make sure that the outlier's score is genuine and not an error.
Descriptive table provide you with an indication of how much a problem associated with these outlying cases. The expected value is the 5% Trimmed Mean. SPSS removes the top and bottom 5 per cent of the cases and calculated a new mean value to obtain this Trimmed Mean value. If you compare the original mean and this new trimmed mean, you can see if your more extreme scores are having a lot of influence on the mean. If you find these two mean values are very different, you need to investigate the data points further.
The Extreme values table gives you with the highest and the lowest values recorded for that variable and also provide the ID of the person with that score. It helps to identify the case that has the outlying values. SPSS Survival Manual by Julie Pallant

Assessing the Normality of your Data

2008-07-28T02:04:00.010+07:00

Many statistical analysis techniques hold the assumption that the distribution of scores on the dependent variable is normal. Normality is described as a symmetrical bell-shaped curve where the greatest frequency of the scores in the middle and with the smaller frequencies toward the extremes. Apart from checking normality of the skewness and kurtosis values, using the Explore option of the Descriptive Statistics menu is another way of doing it.

Procedure for Assessing Normality Using Explore

From the menu at the top of the screen click on Analyze, then click on Descriptive Statistics, then Explore.
Click on the variable/s you are interested in (e.g. total perceived stress). Click on the arrow button to move them in the Dependent List box.
Click on any independent or grouping variables that you wish to split your sample by (e.g. sex).
In the Display section make sure that Both is selected. This displays both the plots and statistics generated.
Click on the Plots button. Under Descriptive click on the Histogram. Click on Normality plots with tests.
Click on Continue.
Click on the Options button. In the Missing Values section click on Exclude cases pairwise.
Click on Continue and then OK.

The output generated from this procedure is shown below.

In the table labeled Descriptives, you are provided with descriptive statistics and other information concerning your variables. In the Test of Normality table above, you are provided with the results of the Kolmogorov-Smirnov statistic. This assesses the normality of the distribution of scores. A non-significant result (Sig value of more than .05) indicates normality. In this case, the Sig. Value is .000 for each group suggests violation of the assumption of normality.

Obtaining Descriptive Statistics for Continous Variables

2008-07-27T00:33:00.006+07:00

We have talked about the procedure to obtain descriptive statistics for categorical variables. Now, I want to show you the procedure of obtaining descriptive statistics for continuous variables:

From the menu at the top of the screen, click on Analyze, then click on Descriptive Statistics, then Descriptives.
Click on all the continuous variables that you want to obtain descriptive statistics for. Click on the arrow button to move them into the variable box (e.g., age).
Click on the Options button. Click on mean, standard deviation, minimum, maximum, skewness, kurtosis.
Click on Continue, and then OK.

The output generated:

Reading the output:

Regarding the variable age, we have information from 1514 respondents, the range of ages is from 18 to 82 years, with a mean of 45.63 and standard deviation of 17.81 This information might be needed to be included in the method section of a research report to describe the characteristics of the sample.

The skewness value indicates that symmetry of the distribution. Kurtosis on the other hand provides information about the peakedness of the distribution. The value of 0 for skewness and kurtosis will be obtained when the distribution is perfectly normal. While positive skewness value indicates that the scores clustered to the left at the low values, negative skewness value indicates that scores clustered to the right-hand of the graph. While positive kurtosis value indicates that the distribution is rather peaked (clustered in the center) with long thin tails, the negative value indicates that a distribution is relatively flat due to the high number of extreme cases.

Descriptive Statistics using SPSS: Categorical Variables

2008-07-18T11:05:00.006+07:00

We have talked about descriptive statistics in general. Now, I would like to share with you how to work with categorical variables in terms of obtaining descriptive statistics for categorical variables. When you want to know how many people gave each responses (e.g. how many males and females in your samples), you should use Frequencies analysis. Frequencies is used for categorical data because it does not make any sense asking for means, standard deviations, maximum and minimum, etc. for categorical variables.

The procedure that you need to take for Frequencies analysis using SPSS 16.0 is the following steps:

From the menu at the top of the screen, click on Analyze, then click on Descriptive Statistics, then Frequencies.
Choose and highlight the categorical variables that you want to run for analysis (e.g., sex). Move these variables into the variables box.
Click on the Statistics button. In the Dispersion section, tick Minimum and Maximum. Click on Continue and then OK.

The output you generate from this analysis will look like the following figure:
The output shows that there are 635 males (41.9 per cent) and 880 females (58.0 per cent) in the sample, giving a total of 1515 respondents excluding two samples that had coded mistakenly.

Descriptive Statistics using SPSS

2008-07-15T13:45:00.003+07:00

Descriptive statistics has several uses. You can perform this statistics after making sure that your data is free from errors. Among these uses are:

1. To describe the characteristics of your sample in the Method section of your report. In studies involving human subjects, it is useful to collect information on the number of people or cases in the sample, the number and the percentage of males and females in the sample, the range and the mean of ages, education level and any other relevant background information.

2. To check your variables for any violation of the assumptions underlying the statistical techniques that you will use to address your research questions. It is important to make sure that your data is not violating any of the assumptions made by a certain statistical tests you use (such as t-test, ANOVA, MANOVA, etc.). These descriptive statistics include the mean, standard deviation, range of scores, skewness and kurtosis.

3. To address specific research questions. Usually you have a certain questions that must be addressed with descriptive statistics, such as the mean of scores.

Technorati Tags: SPSS, SPSS Manual, SPSS Tutorial

Saving a Data File in SPSS

2008-06-30T16:19:00.005+07:00

It is very important to save data file when you are working with SPSS. SPSS will not save it for you.

Then, you must do it manually and frequently enough in case thing goes wrong, such as a sudden blackout. How to do it?

Go to the File menu (top left hand corner) and choose Save.

You can also just click on the icon that looks like a floppy disk which appears on the toolbar at the top, left of your screen. Or you can use shortcut by pressing Ctrl + s on your keyboard.

If you are working with a new data and has not been saved, SPSS will ask you to specify a name for the file and to indicate a directory and a folder that it will be stored in. Choose the directory and then type in a file name. SPSS will automatically provide all data file names the extension .sav

Technorati Tags: SPSS, SPSS 16.0, SPSS tutorial, spss manual

Opening an Existing Data File in SPSS

2008-06-26T14:09:00.008+07:00

When you open SPSS Program, it displays SPSS Opening Screen.

From the this screen, SPSS asks whether you want to Run the tutorial, Type in data, Run an existing query, Create new query using Database Wizard or Open an existing data source.

At the moment, we want to talk about how to open an existing data file. If you want to open an existing data file (e.g. one of the files accompanied SPSS tutorials), click on the Opening an existing data source button from the opening screen, then on More Files.

This will take you to search through the various directories on your computer to find where your data file is located. Find the file you want to use and double click on it or click on Open from your Menu. The data file will open in front of you with the Data Editor label.

Which SPSS books to use?

2008-06-26T12:06:00.003+07:00

There are many books on SPSS available in the market and it is not easy for everyone to decide which one is the most appropriate for him/her to use or to buy.

One of the best ways to help you make decision by reading users' reviews and rating results. Here, we present a page to help you to make decision when you need to buy SPSS guide or manual books. Find more ...

Technorati Tags: SPSS, SPSS manual, SPSS guide, statistics

Discovering Statistics Using SPSS (Introducing Statistical Methods S.)

2008-06-25T14:27:00.003+07:00

Appropriate for All Levels--Undergraduate to Doctorate Programs in Every Discipline!

This new edition of Field's bestselling textbook provides students of statistical methods with everything they need to understand, use and report statistics - at every level.

Written in Andy Field's vivid and entertaining style, and furnished with playful examples from everyday student life (among other places), the book forms an accessible gateway into the often intimidating world of statistics and a unique opportunity for students to ground their knowledge of statistics through the use of SPSS.

The text is fully compliant with the latest release of SPSS (version 13)." Find this book ...

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Now, you can open several windows with SPSS 16.0 at one time.

2008-06-24T17:49:00.005+07:00

Unlike the previous versions of SPSS, SPSS 16.0 is more flexible and friendly to users.

When I started learning the usage of SPSS several years ago, the first thing I learned that SPSS did not let you to have more than one data file open at one time. When you want to open one data file while you are working on another data file, SPSS automatically ask you whether you want to save or close the current data file without saving before it lets you to open your new data file.

With SPSS 16.0 you can get several data files open at one time and simply choose from the menu Window to activate the data file you want to work with. It's just like what you do when you work with Microsoft Word Processors.

Technorati Tags: SPSS, SPSS 16.0, SPSS Program, SPSS Manual, SPSS Tutorial

SPSS Survival Manual

2008-06-19T22:05:00.004+07:00

In this fully revised edition of her bestselling text, Julie Pallant guides you through the entire research process, helping you choose the right data analysis technique for your project.

From the formulation of research questions, to the design of the study and analysis of data, to reporting the results, Julie discusses basic and advanced statistical techniques.

She outlines each technique clearly, with step-by-step procedures for performing the analysis, a detailed guide to interpreting SPSS output and an example of how to present the results in a report.

For both beginners and experienced SPSS users in psychology, sociology, health sciences, medicine, education, business and related disciplines, the SPSS Survival Manual is an essential guide

Illustrated with screen grabs, examples of output and tips, it is supported by a website with sample data and guidelines on report writing. In this third edition all chapters have been updated to accommodate changes to SPSS procedures, screens and output in version 15.

A new flowchart is included for SPSS procedures, and factor analysis procedures have been streamlined. It also includes more examples and material on syntax. Additional data files are available on the book's supporting website. Find more ...

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The rental editions of HLM 6.06 and LISREL 8.80 for Windows are now available

2008-06-13T22:31:00.003+07:00

You can now order internet installation applications for the 6-month or the 12-month rental editions of HLM 6.06 and LISREL 8.80 for Windows. Both new rental editions and rental edition renewals are available. Find more ...

New School University – FREE New School University Information | Encyclopedia.com: Facts, Pictures, Information!

2008-06-08T13:55:00.001+07:00

New School University – FREE New School University Information | Encyclopedia.com: Facts, Pictures, Information!: "New School University in New York City; coeducational; chartered and opened 1919 as the New School for Social Research, a center for adult education, renamed 1997. Founded by Charles Beard , Thorstein Veblen , John Dewey , and others, it originally emphasized classes for adults and became known for programs in social science, the humanities, and public policy." Find more ...

The Art of Writing Proposals

2008-06-07T15:34:00.001+07:00

Writing proposals for research funding is a peculiar facet of North American academic culture, and as with all things cultural, its attributes rise only partly into public consciousness. A proposal's overt function is to persuade a committee of scholars that the project shines with the three kinds of merit all disciplines value, namely, conceptual innovation, methodological rigor, and rich, substantive content. But to make these points stick, a proposal writer needs a feel for the unspoken customs, norms, and needs that govern the selection process itself. These are not really as arcane or ritualistic as one might suspect. For the most part, these customs arise from the committee's efforts to deal in good faith with its own problems: incomprehension among disciplines, work overload, and the problem of equitably judging proposals that reflect unlike social and academic circumstances. Find more ...

Link to Resource on the Net

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Sixty Graduate Students Win SSRC Fellowships to Develop Dissertation Proposals on Topics from Climate Change to Animal Studies

2008-06-07T15:12:00.004+07:00

The Social Science Research Council has selected 60 graduate students as the second cohort of its Dissertation Proposal Development Fellowship (DPDF) program. Go to DPDF Fellows List. Chosen from a highly competitive pool of applicants currently enrolled in U.S. doctoral programs, the 2008 fellows represent 39 institutions nationwide and a wide variety of disciplines, from anthropology and history to religious studies and environmental studies. The program is funded by the Andrew W. Mellon Foundation and administered by the SSRC. Find more ...

Technorati Tags: fellowship, dissertation, climate, animal study, graduate, student

Book: Just Schools: Pursuing Equality in Societies of Difference

2008-06-07T15:05:00.002+07:00

The result of an SSRC Working Group on “Law and Culture,” Just Schools examines schools with widely divergent methods of fostering equality in order to explore the possibilities and limits of equal education today. Educators and policymakers who share the goal of equal opportunity in schools often hold differing notions of what entails a just school in multicultural America. Some emphasize the importance of integration and uniform treatment for all, while others point to the benefits of honoring cultural diversity in ways that make minority students feel at home. The contributors to Just Schools combine empirical research with rich ethnographic accounts to paint a vivid picture of the quest for justice in classrooms around the nation. Find more ...

Technorati Tags: book, school, equality, society

Illinois Learning Standards for Social Science

2008-06-06T23:11:00.000+07:00

Illinois Learning Standards for Social Science: "Social Science The Illinois Learning Standards for Social Science were developed using the 1985 Illinois State Goals for Social Science, the National Standards for World History, the National Standards for United States History, the National Geography Standards, the National Standards for Civics and Government, other various state and national work, and local standards contributed by team members. The integrated study of the social sciences and humanities promotes civic competence. Within the school program social science provides coordinated, systematic study of such disciplines as anthropology, economics, geography, history, law, political science, and sociology, as well as appropriate content from the humanities, mathematics and natural sciences. The study of social science helps people develop the ability to make informed and reasoned decisions for the public good as citizens of a culturally diverse, democratic society in an interdependent world. The individual disciplines that comprise social science are often taught independently, yet all of these disciplines recognize that they owe much to the others. Students who achieve the standards for social science will have a broad understanding of political and economic systems. They will better understand events, trends, personalities and movements in local, state, national and world history. They will know local". Find more ...

Social Science and Power in Indonesia

2008-06-06T23:08:00.000+07:00

Social Science and Power in Indonesia: "SOCIAL SCIENCE AND POWER IN INDONESIA Edited by Vedi R. Hadiz & Daniel Dhakidae The premise of Social Science and Power in Indonesia is that the role and development of social sciences in Indonesia over the past fifty years are inextricably related to the shifting requirements of power. What is researched and what is not, which frameworks achieve paradigmatic status while others are marginalized, and which kinds of social scientists become influential while others are ignored are all matters of power. These and other important themes and issues are critically explored by some of Indonesia’s foremost social scientists in this seminal work. 'Western observers of Indonesia often wonder about the capacity of Indonesian social scientists to address issues of basic public importance. Here is a volume that answers that question with clarity and power. The essays address problems at the heart of Indonesia’s future. They also bear witness to the sophistication and achievement of a new generation of Indonesian social scientists.' – Robert Hefner, Boston University"

Intute: Social Sciences

2008-06-06T23:06:00.000+07:00

Intute: Social Sciences: "Welcome to the Social Sciences pages of Intute. We are a free online service providing you with access to the very best Web resources for education and research, evaluated and selected by a network of subject specialists."

Social Science Research Council

2008-06-06T22:59:00.000+07:00

Home: "Welcome to the Social Science Research Council (SSRC). Founded in 1923, we have developed a worldwide reputation for generating new knowledge to advance understanding of critical social issues, both nationally and internationally. Go to mission statement."

ESRC Society Today - Homepage - General Public

2008-06-06T22:56:00.001+07:00

ESRC Society Today - Homepage - General Public The Economic and Social Research Council (ESRC) is the UK's leading research funding and training agency addressing economic and social concerns. We aim to provide high quality research on issues of importance to business, the public sector and government. Find more ....

The Interaction of Logical Reasoning Ability and Socio-Economic Status on Achievement in Genetics Among Secondary School Students in Nigeria

2008-06-05T00:38:00.001+07:00

The study examined the interaction of logical reasoning ability (cognitive development) and socio-economic status on achievement in genetics amongst secondary school students in Nigeria. Factorial Analysis of variance design with one dependent variable and two independent variables at two levels together with the t-test was used in the analysis of the result. Two hundred and forty senior secondary three (S.S.S III) students randomly selected from six secondary schools in Ethiope East and Okpe Local Government Areas of Delta State, Nigeria, were used as subjects for the study. These were randomly assigned to four groups of sixty students per group. Four research instruments were developed and validated for the study. The result showed that the variables of cognitive development and socio-economic status have positive significant relationship with achievement in Biology. More over, it was also found that cognitive development and socio-economic status are two separate interactive independent variables in science education.

1. Is there any significant difference between the mean score of students in Genetics from upper socio - economic background and those from lower socio - economic background?

2. Is there any significant difference between formal operational students and concrete operational students on achievement in Genetics?

3. Is there any significant difference between the mean score of formal operational level students from upper socio-economic background and those of concrete operational level students from lower socio - economic background on achievement in Genetics? Find more ...

Technorati Tags: SES, achievement, students, school

Factors Affecting Students' Retention at Kuwait University

2008-06-03T19:58:00.001+07:00

This article addresses the factors that affect students' retention at Kuwait University. Five hundred seventy students participated in the study. A survey of 22 retention factors was designed to measure student perceptions. Students presented their agreement on factors which included: achieving personal aspiration, getting jobs, free-of-charge education, acquiring social class, developing skills, achieving academic merit in a field of study, high standard reputation of the university, and feeling one belongs to the university No statistical differences were found among the students' responses according to their colleges, marital status, grade point average, numbers of academic warning, and citizenship status. However, first-year students reported that these factors influence their retention better than other class levels. Increasing student retention is becoming a significant issue in most higher education institutions, specifically for the institutions that face a high drop-out rate. Barefoot (2004) stated that most universities and US colleges face a high rate of first and second year student attrition. For some universities, this issue may negatively affect their economic level, whereas students' fees and tuitions increase the financial revenue of the institution.
Although increasing student retention has a positive effect on the institution's academic side, the most important concern should focus on the effectiveness of the preparation of the student's academic level to provide the community with valuable professionals for work needs, and to participate in the improvement and development of different arenas.
To work toward increasing student retention in higher education institutions, administrators have the responsibility of providing students with an effective academic and non-academic environment that will positively motivate students to persist with their studies and achieve desirable progress in developing their personalities. Bailey and Alfonso (2005) reported that accreditation agencies and state regulators are able to measure community college students' outcomes by tracking their persistence and completion rates. Read more ...

Technorati Tags: Students' retention, student, retention, Kuwait, University

Describing Sets of Observations and Distributions [Intro. 2]

2008-06-01T13:45:00.001+07:00

Tukey (1977) has argued that data analysis is like detective work. The investigator examines a set of observations and tries to describe and explain how those observations were generated. From previous experience and knowledge learnt from study, the detective, like the investigator, has from the outset some ideas about what might have occurred. However, there is always the great danger of drawing false conclusions. Careful data analysis, like thorough detective work must be undertaken before an explanation is formally advanced. In data analysis both descriptive and inferential statistics provide the procedures for sound conclusions to be drawn. A detective investigating a robbery uses the best tools as well as an understanding of how robberies are carried out. There are fingerprints everywhere, but the burglar may have left fingerprints at a window or on a drawer where the jewels were kept. Fingerprint powder can be used to detect the fingerprints, but it must be used in appropriate places. Data analysis is very similar. The expedition involves hunting and not merely shooting at what is seen, or line fishing which involves waiting for the fish to bite. Read more ...

Technorati Tags: introduction to statistics, statistics, observations, distribution, central tendency

Introduction to Statistics [1]

2008-06-01T13:40:00.001+07:00

There are three different types of questions that can be asked about the observations made by human beings of characteristics and events in the real world.
First, there are questions concerned with the magnitude, the pattern and the likeliness of occurrence of the observed events in the world. Secondly, with knowledge gained as answers to the first set of questions, it is sometimes necessary to make a decision whether or not a specific course of action is likely to produce beneficial effects in a particular problem situation. The costs of the course of action are generally considered together with the financial gains or losses arising from the making of a decision. Thirdly, there are questions that take into account prior knowledge and that consider the likeliness of occurrence of observed events in the future.
These three types of questions have given rise to three different branches of statistics, namely the Fisherian approach, the Neyman-Pearson approach and the Bayesian approach, respectively. Failure to see these approaches as being concerned with different types of questions has led to widespread controversy and a tendency to view statistics as a field that is not unified and is divided by conflict. In addition, there is a tendency not to separate the different types of questions and develop a mixed statistical approach that does not distinguish between questions of likeliness and those associated with the making of decisions, or those concerned with predicting the occurrence of events in the future. Read more ...

Technorati Tags: introduction to statistics, statistics

New Study on Women in the Sciences [Robert VerBruggen]

2008-05-31T13:51:00.001+07:00

New Study on Women in the Sciences [Robert VerBruggen]

It suggests that the math gender gap diminishes as governments and societies give women more preferable treatment. Apparently, data from 40 countries indicates that, in Iceland, girls actually outperform boys in math. In a dozen other countries, the sexes are "roughly on par."

It's not that surprising, and it doesn't dispel the notion of biological gender differences. Take this little tidbit, which doesn't seem to find its way into the press coverage. Read more ...

Technorati Tags: new study, women study, research

Colleges Mine Data to Predict Dropouts

2008-05-30T13:38:00.001+07:00

When students log into the course Web site for Purdue University's freshman biology laboratory class, they see an image of a traffic light. Green means they're doing well, yellow means they're faltering, and red means they might fail.

So who's the traffic cop?

It's not the professor, and it's not the teaching assistants. Rather, it's a sophisticated computer algorithm that predicts when students are at risk of failing, based on their preparation going into the class and their behavior on the course's Web site. Read more ...

Educational Inquiry and Creativity: Developing Digital Resources in Ireland's Information Age Town

2008-05-27T15:03:00.001+07:00

By McInerney, Claire R

ABSTRACT

Despite cultural and social barriers to technology adoption, the teachers and administrators of schools in the Irish town of Ennis, with help from the Clare County Education Centre, integrated computers, networking, software, Internet, and digital imagery into the schools' curriculum. Read more ...

Technorati Tags: education, educational inquiry, creativity, digital resources

Statistical Techniques to explore relationship among variables using SPSS

2008-05-11T14:06:00.001+07:00

SPSS provides several techniques of analysis to explore relationships, namely Correlation, Partial Correlation, Multiple Regression and Factor Analysis.

Correlation Analysis: Correlation analysis is used to describe the strength and direction of the relationship between two variables that is usually continuous in nature. It also can be used when one of the variables is dichotomous (it has only two values), such as sex: males/females. The statistic obtained is Pearson's product-moment correlation (r) with its statistical significance.

Partial Correlation: Partial correlation is used to explore relationship between two variables while statistically controlling for a third variable. This is useful when it is suspected that the relationship between the two variables of interest might be influenced or confounded by the effect of a third variable. Partial correlation analysis statistically removes the influence of this variable that can provide a clear picture of the actual relationship between the two variables intended to be examined.

Multiple Regression: Multiple regression can be used to predict a single dependent variable from a group of independent variables. It is used to test the predictive power of a set of variables and to assess the relative contribution of each variable.

Factor Analysis: Factor analysis is used to deal with a large number of related variables (for example, a number of items representing a scale) when a researcher is interested to explore the underlying ideas of these variables. It is useful to reduce a large number of items to be a smaller and manageable number of dimensions or components. (Source)

Technorati Tags: spss, correlation, partial correlation, multiple regression, factor analysis, statistics

Flinders venture for research software

2008-04-27T11:51:00.001+07:00

FLINDERS University has formed a joint venture with US-based Innovista Systems to launch software to assist universities and research institutes in commercialising research.

The relationship with Innovista Systems is one of the first international collaborations for newly formed Flinders Partners, the university's commercial arm, after the merger of the former Flinders Technologies and Flinders Consulting.
The ASK software provides a platform for analysing research projects, assessing market readiness and advising on requirements before a product can be offered for sale.
Flinders Partners managing director Anthony Francis said ASK would be a useful tool for the university sector, which had a history of innovative research initiatives but less success in delivering products to market.
"We looked around and everyone seems to have the same problem," Mr Francis said. "Most universities, and even commercial firms, have done one or two projects really well, but there are plenty of good ideas and research out there that really aren't being forwarded."
The collaboration will incorporate Flinders University's commercialisation software with the intellectual property management system of the US company.
Both ASK and the combined software package will then be marketed globally.
Innovista Systems is an offshoot of Knowligent, a successful Massachusetts company designing and marketing intellectual property management systems.
"Most of the things that are around, such as Microsoft Project, assume that you know what you are doing from the outset," Mr Francis said. "Whereas the projects in the innovation space often have to adapt to what is learnt from the market or what happens along the way, and ASK is a way to ensure that the project keeps changing shape or adapting."
Mr Francis said the software's key feature was to generate a work plan for how to commercially develop a project, as well as the research and technical needs.
"Another feature is to keep track and report on those projects as you go," he said.
The software was expected to be due for commercial release in July at a cost of $25,000 to $30,000.The dollar value of the collaboration between Flinders University and Innovista Systems was not disclosed."We are trying to improve the integration of the technical side of the project with the distribution side of the process," Mr Francis said. (Source)

Technorati Tags: Flinders

Mengenal SPSS (5)

2008-03-05T17:06:00.012+07:00

Istilah Penting: File Data (Data File): File Data adalah file dalam direktori komputer dimana Anda menyimpan data.

Data Set: Data Set adalah lembaran kerja (spreadsheet) dimana Anda telah memasukkan data dengan berbagai variabel yang Anda miliki. Ada beberapa hal yang harus Anda ketahui sebelum memulai menggunakan SPSS.

Pertama, SPSS berfungsi dengan menggunakan beberapa tampilan yang berbeda, didesain untuk melakukan hal yang berbeda. Sebelum Anda dapat mengakses jendala-jendela tampilan ini, Anda perlu membukanya lewat file data yang telah ada, atau Anda membuat file data Anda sendiri. Jadi, dalam bagian ini, kita akan membicarakan tentang bagaimana cara membuka dan menutup program SPSS; bagaimana cara membuka dan menutup file data yang telah ada; dan bagaimana membuat file data dari nol.

Kita akan meneruskan dengan melihat beberapa jendela tampilan yang digunakan SPSS. Selengkapnya ...

Membuat Buku Kode (4)

2008-03-05T17:03:00.010+07:00

Istilah Penting: Buku Kode: Buku kode adalah keterangan yang berisi informasi tentang kode yang Anda akan atau pakai, yang berbentuk angka, saat memasukkan data dalam SPSS.

Nama Variabel: Dalam mengelola data, Anda memakai nama tertentu untuk setiap variabel atau skala. Dari nama ini, Ada nama lengkap dan ada nama singkat. Nama singkat Anda butuhkan saat mengisi data ke dalam SPSS. Sebelum memasukkan informasi dari kuesioner, interview atau ekperimen ke dalam SPSS, Anda perlu membuat buku kode terlebih dahulu.

Buku kode merupakan ringkasan dari instruksi atau keterangan lengkap yang akan Anda gunakan untuk mengubah informasi yang diperoleh dari setiap subyek penelitian ke dalam format atau bentuk yang dapat dibaca oleh program SPSS. Selengkapnya ...

Teknik Pembuatan Kuesioner (3)

2008-02-17T21:43:00.011+07:00

Istilah Penting:

Kuesioner: Kuesioner adalah instrumen pengumpulan data atau informasi yang dioperasionalisasikan ke dalam bentuk item atau pertanyaan.

Infomasi Demografik: Informasi demografik adalah informasi tentang karakter atau latar belakang responden yang dapat berupa tempat, usia, status ekonomi, tingkat pendidikan, status sosial dan lain-lain sebagainya. Informasi semacam itu sangat membantu dalam memahami data penelitian.

Subyek: Subyek penelitian adalah orang yang dilibatkan dalam memberikan informasi yang dibutuhkan terkait dengan pertanyaan penelitian. Manipulasi: Manipulasi adalah perlakuan yang diberikan kepada subyek studi dalam penelitian yang bersifat eksperimental atau quasi-eksperimental dalam penelitian sosial.

Sampel Populasi: Sampel populasi adalah beberapa responden yang dipilih dengan teknik tertentu dari kelompok populasi tertentu untuk mewakili populasi secara keseluruhan. Ini dimaksudkan untuk menghemat beban dana penelitian.

Survei: Survei adalah salah satu pendekatan penelitian kuantitatif yang pada dasarnya dilakukan pada satu kesempatan tertentu guna mendapatkan gambaran minimal tentang suatu fenomena sosial atau alam.

Skor Kontinus: Skor kontinus adalah salah satu skala bilangan yang digunakan dalam memberi kode terhadap respon pilihan dalam pertanyaan kuesioner. Selain skor kontinus, ada skor nominal dan kategorik. Hampir semua penelitian menuntut Anda untuk memperoleh informasi dari subyek atau responden Anda.

Proses ini dapat melibatkan beberapa aktivitas, seperti pengumpulan informasi demografik dari subyek sebelum melibatkan mereka dalam beberapa manipulasi eksperimental. Proses ini bisa juga melibatkan aktivitas mendesain sebuah survei secara ekstensif untuk didistribusikan kepada sampel populasi. Kuesioner yang direncanakan dan didesain dengan cara yang kurang baik tidak akan memberikan data yang baik untuk menjawab pertanyaan penelitian.

Oleh karena itu, dalam mempersiapkan kuesioner ada beberapa hal yang perlu diperhatikan. Anda harus mempertimbangkan bagaimana cara Anda menggunakan informasi yang akan diperoleh. Anda harus mengetahui persisi statistik apa yang Anda akan gunakan.

Bergantung pada jenis statistik yang akan Anda gunakan, Anda mungkin perlu mengajukan pertanyaan dalam cara tertentu atau menyiapkan format respon yang tepat. Bagian ini akan menguraikan secara singkat desain kuesioner. Sebab itu, Anda disarankan untuk membaca buku-buku lain yang secara khusus mengulas masalah teknik desain kuesioner. Selengkapnya ...

Memilih Skala dan Pengukuran yang Tepat (2)

2008-02-02T17:56:00.007+07:00

Istilah Penting:

Skala adalah sekumpulan item atau pertanyaan yang berhubungan satu sama lain, disusun dalam urutan tertentu yang mungkin digunakan untuk mendapatkan informasi tentang karakter tertentu. Atau sebuah garis bilangan yang digunakan untuk mengukur karakter tertentu dengan item atau pertanyaan yang digunakan dan dihitung terhadap skala tersebut.

Reliabilitas (reliability) sebuah instrumen adalah indeks konsistensi atau stabilitas yang memungkinkan instrumen menjadi pengukuran (measurements). Namun demikian, reliabilitas sebuah instrumen hanya dapat diuji dengan memberikannya kepada subyek sampel. Sebagai konsekuensinya, indeks ini bergantung pada karakteristik sampel dan karakteristik instrumen. Reliabilitas akan lebih besar apabila terdapat penyebaran skor yang lebih besar dan lebih kecil apabila penyebaran skor lebih kecil.

Validitas (validity) sebuah instrumen berhubungan dengan kekuatan dan kebermaknaan instrumen. Dengan kata lain, apakah instrumen mengukur konstruk atau konsep yang diinginkan? Anda memiliki berbagai pilihan cara untuk mengumpulkan data; tergantung pada karakter penelitian Anda.

Sebuah studi biasa melibatkan pengukuran hasil (output) atau pencapaian (performance) atas beberapa kriteria obyektif yang Anda konstruksikan. Penelitian dalam bidang ilmu-ilmu sosial banyak melibatkan pengukuran atas sikap atau perilaku atau karakteristik suatu benda dan produk yang diukur dengan seperangkat indikator yang dapat terobservasi secara langsung.

Indikator ini dikenal dengan item atau sejumlah item yang membentuk instrumen. Indikator atau item merupakan hasil operasionalisasi sejumlah skala, konsep atau konstruksi yang bersifat laten, abstrak dan tidak dapat diobservasi secara langsung. Misalnya konsep percaya diri (self-esteem). Konsep ini merupakan konsep yang abstrak dan tidak dapat diobservasi secara langsung sehingga harus dibuatkan sejumlah indikator yang dapat diukur secara langsung. Selengkapnya ...

Mendesain Studi (1)

2007-12-23T13:27:00.009+07:00

Istilah Penting

Desain studi: Desain studi merepresentasikan permasalahan yang diangkat dalam sebuah studi dan cara-cara yang ditempuh untuk menemukan solusi.

SPSS: SPSS merupakan singkatan dari Statistical Package for the Social Sciences. Software ini membantu peneliti untuk mengatur data dan menganalisisnya sehingga hasil yang diperoleh dapat dipahami dan lebih meyakinkan. SPSS hanya sebuah alat; efektif atau tidaknya tergantung pada pemahaman dan penguasaan peneliti terhadap masalah yang ditelitinya.

Subyek: Subyek adalah sampel studi Anda atau populasi studi Anda apabila Anda tidak menggunakan sampel. Kondisi: Kondisi adalah perlakuan yang Anda berikan kepada sampel studi Anda.

Mungkin agak aneh bagi teman-teman sebab kita membicarakan desain studi dalam materi SPSS. Akan tetapi sebenarnya tidak demikian sebab SPSS hanya sebuah alat. Data yang Anda masukkan ke dalam SPSS pasti datang dari salah satu sumber berikut: kuesioner, informasi yang dikumpulkan dari wawancara, hasil observasi yang telah diberi kode, atau pengukuran obyektif terhadap suatu hasil performans seperti hasil tes.

Data hanya akan memiliki kualitas yang baik apabila Anda mengumpulkannya dengan suatu instrumen yang baik dan teruji serta didasarkan pada kerangka penelitian yang menjadi acuan utama dalam pengumpulan data. Selengkapanya ...

Problem Obyektivitas dan Subyektivitas

2007-12-01T19:29:00.005+07:00

Tujuan: Materi kedua ini bertujuan untuk menjelaskan secara singkat akar permasalahan timbulnya perbedaan antara pendukung metode kualitatif dan kuantitatif. Diharapkan setelah mengenali secara singkat akar permasalahnnya, kita akan lebih arif dan lebih dapat menempatkan masalah ini secara proposional dan terhindar dari pedebatan sia-sia. Setelah materi ini dianggap cukup, kita akan kembali melanjutkan materi pertama seputar pengamatan kita terhadap dunia ril. Lalu, setelah itu kita akan menyajikan materi penelitian kualitatif dan kuantitatif secara terpisah atas alasan metodologis.

Perlu diingatkan, bahwa kajian penelitian kuantitatif akan diintegrasikan dengan penguasaan aplikasi SPSS untuk statistik dasar dan penelitian kualitatif akan diintegrasikan dengan penguasaan aplikasi Nvivo.

Secara filosofis, kecenderungan penelitian kuantitatif berakar dari pandangan dunia dalam melihat hubungan antara obyek dan subyek. Apakah kebenaran bersumber dari obyek atau subyek yang mengalaminya. Apabila kebenaran bersumber dari obyek, maka penetapan kriteria kebenaran mesti berdasakan pada kondisi obyektif sesuatu yang dialami. Sebaliknya, apabila kebenaran berasal dari subyek yang mengalami, maka penetapan akhir kebenaran sesuatu harus berdasarkan pada penilaian subyek yang mengalami.

Pada tataran pendekatan penelitian, kedua pandangan filosofis tersebut terbagi ke dalam dua kelompok, yaitu kelompok yang menekankan penetapan kebenaran pada hasil pengukuran secara obyektif dengan instrumen statistik dan kelompok yang menekankan penetapan kebenaran pada hasil laporan pengalaman subyek penelitian secara verbal.

Kecendungan kelompok pertama banyak dianut oleh pada sosiologis dan kecenderungankedua banyak dianut oleh para antropologis.

Secara metodologis, kelompok pertama memakai pendekatan keterukuran (measures) dan kelompok kedua cenderung menolak asumsi keterukuran.Karena itu, kelompok pertama lebih mengutamakan pemakaian pengukuran statistis dan kelompok kedua lebih mengutamakan informasi verbal dari pada angka statistik.

Secara politis, kelompok pertama banyak dikritik atas kecenderungan oversimplikasi atas fenomena sosial dengan model statistik; seakan-akan dipaksakan untuk menyamakan antara fenomena sosial dan alam. Padahal, fenomena sosial jauh lebih kompleks, dinamis dan cenderung mustahil untuk diprediksi. Sebaliknya, kelompok kedua dikritik karena cenderung tidak obyektif dan bias dalam menampilkan fenomena sosial sehingga teori keilmuan yang memiliki asumsi keberaturan (consistency) tidak dapat dipenuhi.

Sebagai konsekuensi dari pandangan ini, menolak kontrol fenemena sosial lewat pengukuran secara statistik. Angka-angka tidak mungkin dapat mengakomodasi kompleksitas fenemena sosial. Menanggapi perdebatan seperti ini, saya lebih cenderung berpendapat bahwa perdebatan seperti ini tidak terlalu berpihak pada pengembangan ilmu pengetahuan apabila dilakukan secara tidak proporsional.

Saya memiliki asumsi kuat bahwa perdebatan seperti ini sering muncul lebih banyak dipengaruhi kepentingan ideologis dan ekonomis dari pada kepentingan keilmuan. Contoh kasus di Indonesia misalnya, sebagian besar orang-orang yang memiliki latar belakang sains kuat cenderung melihat sebelah mata mereka yang berlatarbelakang ilmu sosial.

Sebagai reaksi atas sikap ini, orang-orang yang berlatarbelakang sosial menemukan tempat berlindung dalam pendekatan kualitatif antropologis yang tidak membutuhkan kemampuan matematis dan statistis, tapi dibubuhi dengan argumen pseudo-rasional. Kecenderungan ini sudah terbentuk sejak kita masuk bangku sekolah, khususnya pada tingkat SMU.

Perbedaan semakin kuat saat masuk bangku perguruan tinggi dan terus meningkat setelah terlibat dalam masyarakat sesuai dengan latar belakang ilmu masing-masing. Untuk memenangkan kepentingan ekonomi, politik dan sosial dalam bidang masing-masing, perseteruan antara kedua kubu semakin kuat. Kelompok ilmu-ilmu eksakta cenderung meremehkan kemampuan kelompok ilmu-ilmu sosial dan spirit negatif ini terbawa-bawa sampai pada perebutan pengaruh.

Sebenarnya, kalau kita mencoba jujur dan berbuat untuk kepentingan ilmu dan masyarakat, di antara kedua pendekatan tersebut tidak ada yang sepenuhnya benar dan tidak ada pula yang sepenuhnya salah. Masing-masing memiliki titik kekuatan dan kelemahan.

Kalau ada dua elemen yang kita tahu masing-masing memiliki titik kelemahan dan kekuatan, apa yang kita lakukan? Apakah kita harus memilih salah satunya, membuang dua-duanya, atau memanfaatkan kekuatan kedua-duanya untuk memperoleh hasil yang lebih baik? Saya cenderung mengatakan, kalau bisa, mengapa kita tidak memanfaatkan kedua-duanya?

Adapun kecenderungan seseorang untuk memakai metode tertentu dalam penelitian lebih banyak dipengaruhi oleh latarbelakang keilmuan atau training yang mereka dapatkan. Misalnya, saya berlatarbelakang ilmu-ilmu sosial dan humaniora, tapi akhir-akhir ini selalu memakai pendekatan kuantitatif dalam penelitian. Itu bukan karena gak suka atau menyalahkan pendekatan lain, tetapi hanya karena kebetulan latihan penelitian yang saya peroleh mengajarkan itu lebih banyak.