Last 100 Updated Flashcards
Chapter 15. A spuious relationship exists when there appears to be a real relationship between two vaiables
false
A spurious relationship exists when there appears to be a relationship between two variables, but the relationship is not real: it is being produced because each variable is itself related to a third variable.
Chapter 15. A null hypothesis stipulates that two vaiables are related in the population – for example there is a relationship between age and voting intentions in the population from which the sample was selected.
false
A null hypothesis stipulates that two variables are not related in the population— for example, that there is no relationship between gender and visiting the gym in the population from which the sample was selected.
Chapter 15. A Chi-Square test allows us to establish how confident we can be that there is a relationship between the two vaiables in the population.
True
The chi-square (χ2) test allows us to establish how confident we can be that there is a relationship between the two variables in the population.
Chapter 16. SPSS stands for “Statistical Package for the Social Science”
true
SPSS, which is short for Statistical Package for the Social Sciences, has been in existence since the mid-1960s and over the years has undergone many revisions, particularly since the arrival of personal computers.
Chapter 16. What is the advantage of using SPSS over calculating statistics by hand? Name all advantages.
A It equips you with a useful transferable skill.
B It reduces the chance of making erors in your calculations.
C Many researchers use SPSS as it is a recognised software package.
D It provides better understanding of the statistical techniques applied duing the research.
Correct answers: ABC
Many researchers use SPSS as it is a recognised software package.
It reduces the chance of making errors in your calculations.
It equips you with a useful transferable skill
Many quantitative data analysts use SPSS or an equivalent statistical software package. Such tools are widely regarded as being much faster and more efficient than mental arithmetic, as they can generate huge volumes of complex statistical data within seconds. If you prepare a probability sample, SPSS can help you to produce high-quality results. It might be however less transparent on the content of statistical techniques compared to manual calculation.
Chapter 16. The SPSS Data Editor consists of two views, the Data Viewer and the Numeical Viewer3
false
The SPSS Data Editor. This is the sphere of SPSS into which data are entered and subsequently edited. It is made up of two screens: the Data Viewer and the Variable Viewer. You move between these two viewers by selecting the appropriate tab at the bottom of the screen.
Chapter 16. The SPSS Data Viewer is the spreadsheet into which your data are entered.
True
How is a variable name different from a variable label?
A It is shorter and less detailed.
B It is longer and more detailed.
C It is abstract and unspecific.
D It refers to codes rather than variables.
A ) It is shorter and less detailed.
Clicking the tab on the bottom of the Data Editor screen will switch the programme to the ‘Variable View’. You are limited to eight characters for the variable name, so there is a limit on how you can express the variable for the purposes of SPSS calculations. However, you can enter a longer and more meaningful name as a variable label. SPSS will use the label for all printed output. An example within the Gym dataset would be reasons. A variable label provides a more detailed description of what this means, and serves as a memo to oneself: for example: reasons for visiting gym.
Missing values are when you do not have data for a particular variable when entering a case, you must specify how you are denoting missing values for that variable.
True
Missing values are when you do not have data for a particular variable when entering a case, you must specify how you are denoting missing values for that variable.
When cross-tabulating two variables, it is conventional to:
A)
represent the independent variable in rows and the dependent variable in columns.
B)
assign both the dependent and independent variables to columns.
C)
represent the dependent variable in rows and the independent variable in columns.
D)
assign both the dependent and independent variables to rows.
Correct answer:
C) represent the dependent variable in rows and the independent variable in columns.
It is conventional to represent an inferred relationship between two variables in this way, because it makes tables easier to read. Typically this is done when you feel you can make a claim of causality, so that a change in the independent variable produces a change in the dependent variable. Similarly, when producing a bar chart or scatter-plot, you should assign the independent variable to the x axis (to produce columns) and the dependent variable to the y axis (to produce horizontal readings).
To generate a Spearman’s rho test, which set of instructions should you give SPSS?
A
Analyze; Crosstabs; Descriptive Statistics; Spearman; OK
B
Graphs; Frequencies; [select variables]; Spearman; OK
C
Analyze; Compare Means; Anova table; First layer; Spearman; OK
D
Analyze; Correlate; Bivariate; [select variables]; Spearman; OK
Correct answer: D
Analyze; Correlate; Bivariate; [select variables]; Spearman; OK
Spearman’s rho is a test of correlation, so we should expect to find the SPSS function under ‘Analyse’ – ‘Correlate’. Selecting ‘Bivariate’ opens up the “Bivariate Correlations” dialog box and allows you to generate a coefficient to show the strength of the relationship between variables you selected.
How would you print a bar chart that you have just produced in SPSS?
A
In Output Viewer, click File, Print, select the bar chart and click OK
B
In Variable Viewer, open bar chart, click File, Print, OK
C
In Chart Editor, click Descriptive Statistics, Print, OK
D
In Data Editor, open Graphs dialog box, click Save, OK
Correct answer: A
In Output Viewer, click File, Print, select the bar chart and click OK
This is a straightforward way of printing your bar chart as a piece of “output” from SPSS. If you do not specify which things you want to print from the output summary box on the left of the screen, SPSS will print all of the graphs and tables in the Output Viewer. You can also locate a printer ‘icon’ like you have seen in many other computer programmes, which will open a ‘Print dialog box’. SPSS will warn you that your output has not been saved if you try to close the Output Editor. If that should happen, save your output as a file (SPSS gives you many types to choose from) and decide later on which material you want to print (and even which programme to print from).
Qualitative research takes a deductive view of the relationship between theory and research, where the latter is tested by the former.
false
Most obviously, qualitative research tends to be concerned with words rather than numbers, but three further features are particularly noteworthy: an inductive view of the relationship between theory and research, whereby the former is generated out of the latter.
The epistemological position of qualitative research could be best described as interpretivist.
true
an epistemological position described as interpretivist, meaning that, in contrast to the adoption of a natural scientific model in quantitative research, the stress is on the understanding of the social world through an examination of the interpretation of that world by its participants.
The ontological position of qualitative research
could be best described as objectivist
false
an ontological position described as constructionist, which implies that social properties are outcomes of the interactions between individuals, rather than phenomena ‘out there’ and separate from those involved in their construction.
Which of the following is not a main research
method associated with qualitative research?
A
Ethnography
B
Focus Groups
C
Content Analysis
D
Qualitative Interviewing
C) Content Analysis
The following are the main research methods associated with qualitative research. Ethnography/participant observation. While some caution is advisable in treating ethnography and participant observation as synonyms, they refer to similar approaches to data collection in which the researcher is immersed in a social setting for some time in order to observe and listen with a view to gaining an appreciation of the culture of a social group. Qualitative interviewing. This is a very broad term to describe a wide range of
interviewing styles. Moreover, qualitative researchers employing ethnography or participant observation typically engage in a substantial amount of qualitative interviewing. Focus groups. Language-based approaches to the collection of qualitative data, such as discourse and conversation analysis. The collection and qualitative analysis of texts and
documents.
Purposive sampling is a form of probability sampling.
false
Purposive sampling is a non-probability form of sampling.
The goal of purpose sampling is to sample cases/participants in a strategic way, so that those sampled are relevant to the research questions that are being posed.
true
Which of the following is not a purposive sampling approach?
A
Theoretical sampling
B
Snowball sampling
C
Opportunistic sampling
D
Cluster sampling
Cluster sampling D
The following is a list of some prominent types of purposive sample that have been identified by writers such as Patton (1990) and Palys (2008): 1. Extreme or deviant case sampling. Sampling cases that are unusual or that are unusually at the far end(s) of a particular dimension of interest. 2. Typical case sampling. Sampling a case because it exemplifies a dimension of interest. 3. Critical case sampling. Sampling a crucial case that permits a logical inference about the phenomenon of interest—for example, a case might be chosen precisely because it is anticipated that it might allow
a theory to be tested. 4. Maximum variation sampling. Sampling to ensure
as wide a variation as possible in terms of the dimension of interest. 5. Criterion sampling. Sampling all units (cases or individuals) that meet a particular criterion. 6. Theoretical sampling. 7. Snowballsampling. 8. Opportunistic sampling. Capitalizing on opportunities to collect data from certain individuals, contact with whom is largely unforeseen but who may provide data relevant to the research question. 9. Stratified purposive sampling. Sampling of usually typical cases or individuals within subgroups of interest.
Theoretical sampling is a form of purposive sampling associated with a qualitative data analysis approach known as grounded theory.
true
One form of purposive sampling is theoretical sampling, advocated by Glaser and Strauss (1967) and Strauss and Corbin (1998) in the context of an approach to qualitative data analysis they developed known as grounded theory.
In theoretical saturation, the researcher acknowledges that they need to collect more data to substantiate the categories they’ve identified.
false
In grounded theory, you carry on collecting data (observing, interviewing, collecting documents) through theoretical sampling until theoretical saturation has been achieved. This means that successive interviews/observations have both formed the basis for the creation of a category and confirmed its importance and there is no need to continue with data collection in relation to that category or cluster of categories
Snowball sampling is:
A
All of the below
B
A form of convenience sampling
C
Not a random sampling approach
D
A method where a researcher makes contact with a small group of people and uses them to make contact with others.
all of the below
At the outset of any qualitative research project, the researcher should be able to determine how many people should be interviewed.
false
One of the problems that the qualitative researcher faces is that it can be
difficult to establish at the outset how many people will be interviewed if
theoretical considerations guide selection.
In most Business Research, saturation is claimed, justified and explained.
false
If saturation is the criterion for sample size, specifying minimum or maximum sample sizes is pointless. Essentially, the criterion for sample size is whatever it takes to achieve saturation. The problem is that, as several writers observe (e.g. Guest et al. 2006; Mason 2010), saturation is often claimed but not justified or explained (Bowen 2008).