Conselling Master Questionnaire Describe the Boolean Approach

Conselling Master Questionnaire

Describe the Boolean Approach (Hauser, 2009 p. 18)

In order to use counseling databases to search for information, it is important to develop appropriate search practices. The terms one types when searching will most likely provide the results owing to their meaning. Therefore, it is up to an individual to come up with a search strategy and learn the tips of using counseling databases. The tips will assist an individual in acquiring high quality information in the required amount. Additionally, an individual is likely to add intelligence to their search strategies, the more one knows the tips of searching, and they are likely to obtain better results.

The Boolean approach is among the techniques that will generate useful results when using counseling databases, and it aims at supporting scholarly research. On the other hand, Boolean operators are words, which have unique meanings that instruct the database to either combine various search terms in particular ways or avoid particular notions in the search. The operators include AND, OR and Not. When an individual uses them properly, they will enhance the search. For instance, operator AND, narrows a search and retrieves results, which involves both terms together.

2) Name some major databases (Hauser, 2009 p. 18)

A database is a collection of organized information, which aims at providing an efficient point of retrieving information. The organization follows a systematic manner that makes it easy to access information, management and updating. Owing to the many disciplines that exist, there are many databases designed to fulfill each field of education. A database makes it easy to do research, access important information and develop knowledge. In the field of counseling and education research, there are various databases. Some of the databases include Academia Search Primer, Expanded Academic, JSTOR, PsycCritiques, Sociological Abstracts, Biological Abstracts and PsycArticles. In addition, for one to find the right or appropriate information, which is relevant to their topic of research, using the correct terms when researching is crucial.

3) What does the "Web of Science" do? (Hauser, 2009 p. 19)

"Web of Science" is an extensive interdisciplinary, bibliographic database with article references ranging from various research materials. The materials include journals, books and proceedings. Most of them come from various fields such as sciences and technology, arts and humanities, counseling and social sciences. The Web of Science provides access to numerous databases that reference various researches, which allows for comprehensive exploration of exceptional sub-fields provided in a scientific discipline.

Development of a citation index is possible. This is because citations in science act as connectors between the same research items and result to similar or correlated scientific information from abstracts journals and proceedings. In addition, the literature that has the largest influence in a given field, or more than one discipline, is easy to find using the citation index. For instance, the citation index makes it possible to establish the papers influence by connecting all papers, which have cited the index.

4) Define and explain briefly the difference between a PDF and an HTML page

According to Hauser (2009, p. 19), both PDF and HTML provide an interface where investigators can view citations after searching for information on a database. The both offer full text options where one can see the article they were searching. A PDF (Portable Document Format) is a file design that has captured all aspects of a printed text in the form of a viewable, electronic image. On the other hand, HTML is a language that describes a web page. Although a PDF file and HTML files comprise of similar texts, they have differences. A PDF file reveals the actual view of an article and will have the page numbers, which were on the actual article. On the other hand, an HTML file is computer generated, which does not show images that were on the original article.

5) Explain what are plagiarism and the types of plagiarism that can happen (Hauser, 2009 p. 21)

Plagiarism is the act of utilizing another person's language, thoughts, texts, writing, speech or song without permission, and using the above in one's own literary works, without accrediting the original author. Plagiarism is a form of cheating because the act is illegal, in other terms; it is actually taking without permission, which is a form of theft. It is academic dishonesty because people use other people's ideas or writings as their own. In addition, there are various forms of plagiarism. Copy and paste plagiarism is the most dominant type of plagiarism where an individual the highlights information from any research material and using it on his own, without changing any word.

6) What is "cryptomnesia?" (Hauser, 2009 p. 21)

"Cryptomnesia" is a psychological condition in which a person mistakes a memory for a new idea. However, the phenomenon is common to every human being from time to time, but the degree of the condition and the control intervention is controversial. Hauser (2009, p. 21) further suggests that unintentional plagiarism and a result of not remembering that one acquired the information source as "cryptomnesia." Some psychologists have used the condition to explain inadvertent plagiarism, and they have described the condition as a theory of forgotten memory. Some of the common aspects used in the study of this condition include understanding the way the brain stores information and subsequently processes the same information.

7) What is "cyber cheating?" (Hauser, 2009 p. 21)

Plagiarism as described by Hauser is an academic dishonesty because a person takes another person's ideas and uses them in his or her own writing as their own, without accrediting the original author. There are various forms of plagiarism ranging from copy and pasting to "cyber cheating." "Cyber cheating" is another form of plagiarism, whereby an individual takes other people's ideas or writings from the web, copies it and pastes it in their own piece of work.

8) Name the four different measurement scales in statistics (Hauser, 2009 p. 23)

Hauser suggests that for one to be in a position to evaluate research it is important for them to understand numerous common statistical methods and the terminologies included. In his book, Hauser defines statistics as "an area of science concerned with the extraction of information from numerical data and its use in making inferences about population from which data are obtained (p. 23)." Therefore, it is apparent that statistics is primarily concerned with making inferences concerning population. In addition, there are various measurement scales, identified and utilized in quantitative research. They include a nominal scale, an ordinal scale, an interval scale and a ratio scale.

9) What is the difference between "numerals" and "numbers?" (Hauser, 2009 p. 23)

In order to provide an efficient evaluation of research, Hauser suggests that there is a need to differentiate between numerals and numbers. In his definition, he suggests that numerals primarily deal with symbols including words and letters, for example male and female. It is not possible to assume that the distance between intervals is equal. In addition, some measurement scales such as nominal an ordinal scales utilize fall under the numerals category. On the other hand, Hauser defines numbers as values used in mathematics to perform operations such as addition, subtraction, division, multiplication and so on, and the numbers have an even distance between units. For instance, the interval distance between 102 and 103 is similar to that between 104 and 105. Nominal scales and ratio scales are central to the use of numbers.

10) What is a "data set?" (Hauser, 2009 p. 25)

A "data set" is among the many statistical definitions, which refers to scores found in a particular collection of scores, commonly from a sample of a given population. A typical example of a data set s the scores of 60 participants in a specific research study.

11) Explain what is/are: (Hauser, 2009 p. 25)

a) A distribution: According to Hauser (2009 p. 25), a distribution refers to the manner in which distribution of scores across a data set occurs, or the path used to distribute scores across a data set.

b) Frequencies: According to Hauser (2009 p. 25), frequencies refer to the degree at which a particular score appears in a distribution, or the number of times a score appears in a particular distribution.

c) Median: According to Hauser (2009 p. 25), Median refers to the middle point (average), where half of scores fall above and the other half fall below this point.

d) Mode: According to Hauser (2009 p. 25), mode refers to the score that appears the most or many numbers of times in a given distribution.

e) Normal curve: According to Hauser (2009 p. 25), a normal curve is the symmetrical bell-shaped curve.

f) Range: According to Hauser (2009 p. 25), a range is the distance between the highest and the lowest score in the distribution.

g) Scales of measurement: According to Hauser (2009 p. 25), scales of measurement are the various types of scales utilized in measuring the variables.

12) Explain what "Descriptive statistics" is? (Hauser, 2009 p. 26)

The primary objective of statistics is to make inferences concerning a population. In so doing, there is a need to explain or offer information concerning the sample, which will major in a given study. Descriptive statistics come in, and assist in describing the sample on which the study will take place. In addition, descriptive statistics first offer substantial information concerning the sample to major in the study. Statistics associated with descriptive statistics shown on the normal curve are identified as the measures of central tendency. Additionally, researchers have described descriptive statistics as an effort to summarize the gathered data comprehensively.

13) Explain the purpose and meaning of "Pearson" (Hauser, 2009 p. 30)

In statistics, "Pearson" in full Pearson product-moment correlation coefficient is the measure of the linear dependence between two or more variables. It is the most frequently utilized correlation statistic, denoted as a Pearson r. In addition, the Pearson r is usable with both interval and ratio data. The primary purpose of "Pearson" is to measure the relationship between two ratio variables. The Pearson r correlation coefficient works with the assumption that the relationship between two or more variables is linear.

14) Explain what "Inferential statistics" is with four assumptions (Hauser, 2009 p. 30)

According to Hauser, in statistics, inferential statistics refer to the process of making conclusions from information that are central to random variation. The inferential statistics help n making conclusions whether it is possible to apply the outcomes of a sample to the target population. In addition, for investigators to understand inferential statistics, it is important for them to understand the basic assumptions associated with inferential statistics. The assumptions include normality, linearity, independence and homogeneity. The assumption behind normality is that a set of scores does not have substantial difference from a normal curve. In the case of linearity, assumption comes in when the investigator aims to establish the relationship between variables, and it outlines the degree to which variables correlate in a linear function. The third assumption, independence states that scores must show independence, and individual scores should not have the capacity to influence each other. The last assumption, homogeneity of variance, states that the degree of differences of the categories is homogenous.

15) What are the differences between parametric and parametric statistics? (Hauser, 2009 p. 30)

In inferential statistics, parametric statistics assumes that the data has come from a kind of probability distribution and consequently makes inferences concerning the parameters of the distribution. Using Hauser (2009, p. 30), in comparison with non-parametric methods, parametric methods make more assumptions. However, if the postulations are correct, they have the capacity to produce more approximations that are accurate. On the other hand, non-parametric statistics are the opposite of parametric statistics. They do not assume that the data has any attribute or parameters. A typical example, non-parametric statistics are apparent if there is a violation of the normality assumption in inferential statistics. In this case, it happens if there is no representation of the normal curve. In addition, another example provided by Hauser (2009, p. 31), non-parametric statistics come in when there is a violation of the linearity assumption.

16) What is "level of significance?" (Hauser, 2009 p. 30)

Level of significance is an important aspect, which helps an investigator to understand and interpret inferential statistics. According to Hauser (2009 p. 30), levels of significance refer to the possibility of accepting or rejecting a null hypothesis. In addition, the author further refers it to p (probability) value or alpha point. Additionally, this level of significance is generally the chance that an error will occur during the process of rejecting a null hypothesis. A typical example is a researcher comparing the effects of a stress-training program (independent variable) on the reported levels of stress (dependent variable).

17) What is "null hypothesis?" (Hauser, 2009 p. 31)

In inferential statistics of observed data, the null hypothesis is the general position: that there is no relationship between the measured variables. In addition, Hausa (2009, p.31) further states, "A null hypothesis means that there is no difference between the compared groups." A typical example is an investigator comparing the effects of a stress-training program on reported levels of stress. The objective of the investigator is to determine whether the program resulted to variations in stress scores between the group that experienced the intervention and the group that did not take part. In this case, the null hypothesis states that there is no variation between the groups on generalized stress scores.

18) What types of errors are there? (Hauser, 2009 p. 32)

In the process of rejecting or accepting a null hypothesis, it is possible that errors will come up. Hauser (2009, p. 32) defines the chance of making these errors using Type I and Type II errors. He further defines the errors as Type I error, refers to the one, which the investigator rejects the null hypothesis, although there are no apparent variations between the groups. A Type II error refers to the one, which the investigator accepts the null hypothesis, although there are apparent variations in the compared groups. Hauser (2009, p. 32) further suggests that the increasing the chance of resulting to Type I error, leads to a reduced chance in making Type II error.

19) Explain what "descriptive statistics" is:

a) Meaning: Hauser defines Descriptive Statistics as statistics, which primarily concerned with describing the sample used in conducting a study, not the population.

b) Purpose: As Hauser suggests, it is apparent that descriptive statistics helps an investigator to have substantial information concerning the sample used in the study.

c) Methods: Methods used in descriptive statistics include measures of central tendency, measures of variability, using tables and graphs, and utilizing the measures of relationships.

Mean: Mean is a method that falls under measures of central tendency and it is the most applied measure of central tendency.

Tables and graph: This refers to the visual techniques used to illustrate the frequency of distribution of a data set. They include histograms, bar graphs and so on.

Measures of relationship: They assist an investigator to establish the relationship between variables. For instance, they help in answering the question: to what extent to the two variables vary. They include Pearson, correlation and spearmen rho.

20) Explain the meaning and purpose of (Hauser, 2009 p. 33-40)

a) ANOVA: This refers to analysis of variance. It is the most utilized statistical procedure, which mainly assists in comparing groups. In addition, it is mainly utilized in case a study has one independent variable, and three or numerous groups.

b) MANOVA: This refers to multivariate analysis of variance. This statistical procedure assists in calculating variation among variances of means of groups, in a study using two or numerous dependent variables.

c) ANCNOVA: This refers to analysis of covariance. This is a statistical procedure used to calculate divergences among variances of means of groups and controls the effects of extraneous variables on the dependent variable.

d) Multiple regressions: This procedure helps in calculating the relationship between a predicted variable and other predictor variables.

e) Chi-square: This statistical method helps in testing theory. The technique uses significant constructs and instruments verified to test the approach. The technique also uses correlational methods to test the theory.

21) Explain briefly the strengths and weaknesses of quantitative research (Hauser, 2009; McLeod, 2003, p. 72-75)

Quantitative research provides information, which an investigator can express in numbers, and owing to this, it is possible to apply statistical tests in generating hypothesis concerning the information. These include descriptive statistics such as the mean, median and standard deviation, but it can also include inferential statistics such as ANOVAs. The greatest strength of quantitative data is providing information, which is descriptive. The second strength is that this method has the capacity to generalize research findings when it comes from various populations. In addition, this method is efficient when acquiring data that offers for quantitative predictions. On the other hand, the method also has significant weaknesses. In this method, the researcher's groups used might not reflect understanding of local constituencies. In addition, the theories used by an investigator might not provide for the understanding for local constituencies.

22) What is the difference between the listed variables (Hauser, 2009 p. 43-44)

According to Hauser (p. 43), a variable is the condition or attributes that an investigator manipulates, controls or examines. There are various types of variables listed below; their meaning will provide an apparent difference among them.

a) Independent variable: An independent variable refers to a condition, condition or a measured characteristic, which an investigator is able to control. In addition, an independent variable must have two or more levels because of comparison purposes.

b) Dependent variable: A dependent variable refers to the terms of alterations in the subject because of the independent variable. In addition, the dependent variable refers to the level of stress depicted by the subject at any time of interest.

c) Extraneous variable: According to Hauser, extraneous variables refer to the uncontrolled or unknown aspects that have the capacity to influence the results of a dependent variable.

d) Control variable: A control variable refers to an extraneous variable identified by an investigator, and one has established ways to control its influence.

23) What is research design? (Hauser, 2009 p. 45-47)

A research design refers to the manner in, which collection of data takes place. It may also refer to the method applied in a particular study to collect information. Therefore, this makes it clear that there are numerous research designs. However, many of the designs either apply quantitative or qualitative research, but other research designs may utilize both methods and then distinguished by the type of data collection done and how evaluation of data takes place.

24) Explain the following research designs (Hauser, 2009 p. 45-58)

a) True experimental: This refers to a map or plan utilized in conducting collection of information. The design aims at comparing treatment categories to control traditional techniques.

b) Quasi-experimental: This research design provides comparisons between categories or similar groups at various times.

c) Pre-experimental: This research design helps an investigator to compare groups or within a group, but commonly not over time.

d) Descriptive: This research design mainly applies to describe a phenomenon.

e) Correlation: This research design aims at testing variables to determine for statistical relationships. It falls under quantitative research method.

Ethnographic: Ethnographic research methods involve the utilization of comprehensive study of the attributes of a culture or a distinct group using their own worldview from an original or firsthand effort. Similar to other qualitative techniques, this approach comes from the anthropologists.

f) Historical: Historical research is a technique applied when conducting qualitative research. The primary purpose is to assist in understanding past events and occurrences to acquire a comprehending of current issues. In addition, it may involve the utilization of systematic methods including diaries, oral records and relics.

25) Explain what "mixed methods" are

Mixed methods refer to a research methodology, which has utilized several research methods. According to Hausa (2009), mixed methods as outlined, is the integration and mixing qualitative and quantitative research techniques, although it has proved to be a complicated task. In the history of mixed methods, quantitative and qualitative research methods are core to the development of mixed methods. For instance, quantitative research developed owing to psychology dating back to the late 1800s. On the other hand, quantitative approach developed because of positivism.