Conselling Master Questionnaire Questionnaire

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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)Buy full Download Microsoft Word File paper
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Questionnaire on Conselling Master Questionnaire Describe the Assignment

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… [END OF PREVIEW] . . . READ MORE

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